10 BEST DATA SCIENCE PROGRAMMING LANGUAGES
    17Dec, 2021
    10 Best programming languages for Data Science to learn in 2023

    with expanding volume and complexity of big business data, and its focal job in direction and vital preparation, are driving associations to put resources into individuals, cycles and advances they need to figure out and gain experiences from their data assets.

    According to Market research Business IDC estimated that more than 64 zettabytes of data — the equivalent of 64 billion TB — was created, captured, copied and consumed during 2020

    Python

    Python
    Python is open-source, interpreted, high-level language and provides a great approach for object-oriented programming. It is one of the best languages used by a data scientist for various data science projects/applications. Python provides great functionality to deal with mathematics, statistics, and scientific function.

    1. Python is a relatively simple and easy to learn programming language. Python’s main advantage is that it is simple and straightforward, which makes it appealing to anyone.
    2. Libraries and tools One of the primary responsibilities of Data scientists is to analyse data, and data can take many forms in the real world.
    3. Notebook Jupyter. Another reason Data Scientists like Python is the Jupyter Notebook, which allows you to code and collaborate with other Data Scientists via a web browser.
      Help from the
    4. Community. Another reason for Python’s popularity among people learning Data Science in the community that I discovered.
    5. Pandas
      If you want to work with Python, you must be familiar with Pandas. It includes a high-performance data structure known as gritty of Pandas

    Java

    Java is probably the best language to learn for big data for a number of reasons; MapReduce, HDFS, Storm, Kafka, Spark, Apache Beam, and Scala (are all part of the JVM (Java Virtual Machine) ecosystem. Java is by far the most tested and proven language.

    Although Java may appear to be unrelated to data science, there are many frameworks, including Hadoop, that run on JVM and are an essential component of the data stack. Hadoop is a software method for processing and storing large amounts of data in distributed structures. Because of its increased processing power, it can process large amounts of data and handle virtually infinite tasks at once.

    JAVASCRIPT

    JavaScript just doesn’t have the range of data science packages and inbuilt functionality compared to languages such as R and Python. … js is not suited for computationally intensive, CPU-bound tasks. For such tasks, languages such as Python, Java or Scala have the upper hand over JS.

    JavaScript is undeniably better than Python for website development for one simple reason: JS runs in the browser while Python is a backend server-side language. While Python can be used in part to create a website, it can’t be used alone. … JavaScript is the better choice for desktop and mobile websites.

    SQL

    SQL
    SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and extracting various data types from databases. A working knowledge of databases and SQL is necessary to advance as a data scientist or a machine learning specialist.

    1. Simple to Understand and Apply

    2. Getting to Know Your Dataset

    3. Compatibility with Scripting Languages

    4. Handle massive amounts of data

    5. A Pathway to Jobs in Data Science

    C/C++

    C/C++
    Learning C/C++ offers excellent capabilities for building statistical and data tools. These will translate well to Python and scale well for performance-based applications.

    C/C++ is also surprisingly useful because it compiles data quickly. It builds highly functional tools and allows for serious fine-tuning. It can be complicated to pick up if you’ve never studied programming languages before.

    When to use C/C++ in data science: Web developers with experience in low-level languages could use C/C++ for scalable projects.

    SCALA

    Researchers are working hard to develop application-specific tools, programming languages, and frameworks. Scala is one of those inventions to leverage big data processing. Scala programming language is built to implement scale able solutions to crunch big data in order to produce actionable insights.

    Julia

    Julia has many features and resources advantageous to machine-learning and data science. This language was designed with a focus on numerical and scientific computation. Julia’s math-friendly syntax makes it ideal for users of Matlab, Octave, Mathematica, R, among other computing languages and environments.

    R

    R
    The good news is R is developed by academics and scientist. It is designed to answer statistical problems, machine learning, and data science. R is the right tool for data science because of its powerful communication libraries.

    Some of R’s key features for data science applications include:

     

    • R provides extensive statistical modelling support.
    • R is a good tool for a variety of data science applications because it has attractive visualisation tools.
    • R is commonly used in data science applications for ETL (Extract, Transform, Load). It serves as a gateway for many.
    • R also includes a number of useful data-wrangling packages.
    • Data scientists can use R to apply machine learning algorithms to predict future events.

    MATLAB®

    Engineers and scientists use MATLAB® to organize, clean, and analyze complex data sets from diverse fields such as climatology, predictive maintenance, medical research, and finance. MATLAB provides: Datatypes and preprocessing capabilities designed for engineering and scientific data.

    SAS

    SAS
    SAS is a tool for analyzing statistical data. SAS is an acronym for statistical analytics software. The main purpose of SAS is to retrieve, report and analyze statistical data. Each statement in SAS environment ends with a semicolon otherwise the statement will give an error message

    In addition, larger industries rely on SAS for data security and stability. SAS is a critical tool for data scientists for these reasons. With the growth of the data science community, there is a shift away from SAS and toward more open-source tools.

    Data Science vs. Data Analytics: Choosing the Right Career
    ROLE AND RESPONSIBILITY OF DATA SCIENTIST
    11Dec, 2021
    What is data science role and responsibility of data scientist ?

    what is data science ?

    Data Science is “about using various techniques, algorithms to analyze large amounts of both structured or unstructured data, to extract useful data, thus applying them in various business domains.
    Data science combines the scientific method, mathematics and statistics, specialized programming, advanced analytics, AI, and even storytelling to tell and understand the business insights buried in data.

    Data preparation may involve cleaning, collecting, and manipulating it to be ready for specific types of processing. Analysis requires the development and use of algorithms, analytics and AI models.

    Why are data scientists in demand?

    Why are data scientists in demand?
    Data is being generated on a massive scale day by day and to process such large data sets, large firms, companies can extract valuable data from these data sets with deep access data and use them for various business strategies, models. Looking for good data scientists to do

    How does data science work?

    How does data science work?
    Data science involves the proficiency of disciplines and areas of expertise to produce a comprehensive, complete and clean data set in raw data. Data scientists must be proficient in everything from data engineering, mathematics, statistics, advanced computing and visualization to effectively create exhaustive and clear data from a tangled medium of information and extract only the most important information that is most important. Help drive innovation and efficiency.

    Data scientists also rely heavily on artificial intelligence, particularly machine learning and its subfields of deep learning, to build models and make predictions using algorithms and other techniques.

    Data Science Tools

    Learn python

    The first and foremost step towards data science should be a programming language (ie Python). Python is the most common coding language, used by most data scientists, because of its simplicity, versatility, and being pre-equipped with powerful libraries (such as NumPy, SciPy, and Pandas) in data analysis and other aspects of data analysis. is useful. Science. Python is an open-source language and supports various libraries.

    Statistics

    If Data Science is a language, then Statistics is basically grammar. Statistics is basically a method of analysis, interpretation of large data sets. When it comes to data analysis and insight gathering, statistics are just as remarkable as the wind for us. Statistics help us understand the hidden details from large datasets.

    Data storage

    This is one of the important and important steps in the field of data science. This skill includes knowledge of various tools for both importing data from local systems, such as CSV files, and scraping data from websites, using the Beautiful Soup Python library. Scraping can also be API-based. Data collection can be managed in Python with knowledge of the query language or ETL pipelines.

    Data cleaning

    This is the stage where most of the time is being spent as a data scientist. Data cleaning is suitable for obtaining, working and analyzing data from the raw form of the data by removing unwanted values, missing values, categorical values, outliers and incorrectly submitted records.

    Data cleaning is very important as real world data is messy and achieving this with the help of various Python libraries (Pandas and NumPy) is really important for an aspiring data scientist.

    Machine learning model

    Machine learning models are the way by which you integrate machine learning models into an existing production environment to make practical business decisions based on data.

    machine learning

    Machine learning is the idea that computers can learn from examples and experience, without being explicitly programmed to do so. Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given.

    For example, one type of algorithm is a classification algorithm. It can keep data in different groups. Classification algorithms used to detect handwritten letters can also be used to classify emails into spam and non-spam.

    Real world test

    After deployment, the machine learning model should be tested and validated to check its effectiveness and accuracy. Testing is an important step in data science to keep the efficiency and effectiveness of ML models under control.

    The various benefits of Data Science are as follows

    1. It is in demand

    Data science is in great demand. Potential job seekers have many opportunities. It is the fastest growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. This makes data science a highly employable job field.

    2. Excess of posts

    There are very few people who have the required skill-set to become a complete data scientist. This makes Data Science less saturated as compared to other IT sectors.

    Hence, Data Science is a very abundant field and has a lot of opportunities. The field of data science is high in demand but low in supply for data scientists.

    3. A Highly Paying Career

    Data Science is one of the highest paying jobs. According to Glassdoor, data scientists earn an average of $116,100 per year. This makes data science a highly lucrative career option.

    4. Data Science is Versatile

    There are many applications of Data Science. It is widely used in healthcare, banking, consulting services and e-commerce industries. Data Science is a very versatile field. So you will get opportunity to work in different sectors.

    5. Data Science Makes Data Better

    Companies need skilled data scientists to process and analyze their data. They not only analyze the data but also improve its quality. Hence, Data Science is concerned with enriching the data and making it better for your company.

    6. Data Scientists Are Highly Reputable

    Data scientists allow companies to make better business decisions. Companies rely on data scientists and use their expertise to provide superior results to their customers. This gives data scientists an important position in the company.

    7. No more boring tasks

    Data Science has helped various industries to automate redundant tasks. Companies are using historical data to train machines to perform repetitive tasks. It has simplified the difficult tasks previously done by humans.

    8. Makes Data Science Products Smart

    Data Science involves the use of machine learning that has enabled industries to create better products specifically tailored to customer experiences.

    9. The Data-Powered Adventure Begins

    Hey there, curious minds! Imagine having the power to predict the future, solve mysteries, and make smarter decisions, all with the help of data. That’s the enchanting world of Data Science! Buckle up, because we’re about to embark on a journey that will open your eyes to the incredible benefits of this magical field.


    10 Explore the Unknown: Discover Hidden Patterns

     

    Have you ever wondered how Netflix knows exactly what shows you’ll love? Or how your phone’s GPS finds the fastest route? It’s all thanks to Data Science. This wizardry involves digging into piles of data (numbers, pictures, words) to uncover patterns that even the sharpest human minds might miss. Think of it as a treasure hunt for insights!


    11. Predicting the Future: Your Crystal Ball Upgrade

    What if I told you that Data Science can predict if it will rain tomorrow, or which new gadget will become the next big thing? By analyzing past data, we can predict future events with impressive accuracy. Farmers use it to know when to plant crops, and doctors use it to predict disease outbreaks. It’s like getting a glimpse into the future!


    12. Machines with Brains: Making Computers Smart

    Remember R2-D2 from Star Wars? Data Science turns computers into brainy sidekicks! With the magic of algorithms (fancy recipes for solving problems), we can teach computers to learn from data and make decisions. Self-driving cars, virtual assistants like Siri, and even robots on Mars owe their smarts to Data Science.


    13. Solving Mysteries: Data Detectives at Work

    Imagine you’re a detective solving a crime. Data Science helps real-life detectives too! By analyzing clues like fingerprints, security camera footage, and witness statements, they can catch the bad guys. Data Scientists use similar skills to solve mysteries hidden within data, helping businesses find out what customers want or even preventing diseases.


    14.Skyrocketing Success: From Startups to Space Missions

    Have you heard of SpaceX? They used Data Science to make rockets more reliable and less expensive. But it’s not just for space enthusiasts. From designing cool video games to helping farmers grow better crops, Data Science is everywhere. Startups use it to understand their customers, while big companies use it to improve products and services.


    15. Changing the World:

    Making a Difference Imagine if we could predict earthquakes or stop endangered species from going extinct. Data Science is lending a hand to save our planet. By analyzing data from sensors placed deep in the ground or on animals, scientists can make the world a safer and better place for all living things.


    16. Your Journey Begins: Becoming a Data Wizard

    Now that you know the secrets of Data Science, do you want to be a part of this adventure? Learning about data analysis, statistics, and coding is like learning spells to unlock the magic. As a Data Wizard, you could shape the future, tackle mysteries, and create innovations that will astonish the world!


    17. Conclusion: Embrace the Data-Driven Future

    So, young explorers, the realm of Data Science is yours to conquer! Remember, every time you ask a question, play a game, or even post a picture, you’re generating data. And with the power of Data Science, that data can become a force for incredible change. Embrace the magic, keep asking questions, and who knows what you might discover next?

    Beginner's Guide Machine Learning and Data Science

    Common Data Scientist Job Titles

    1. Data Scientist: The Insight Explorer

    Imagine you’re a detective of data. You sift through information like Sherlock Holmes, uncovering hidden connections and building magic algorithms that predict the future. You’re the brain behind making decisions smarter and helping businesses see beyond the obvious.

    2. Data Analyst: The Data Detective

    You’re like a data detective. Imagine you’re Sherlock Holmes, but instead of solving crimes, you solve business puzzles. You dive into huge piles of data, sort through them, and reveal secrets hidden within. Your findings guide companies on what moves to make next.

    3. Data Engineer: The Data Organizer

    Picture yourself as a data organizer. You’re like a librarian for data – you collect, clean, and neatly arrange information from different sources. Think of it as building the foundation for data scientists and analysts to work their magic.

    4. Business Intelligence Specialist: The Trend Spotter

    Imagine you’re a trend spotter for businesses. You’re like a fashion guru, but for data trends. You analyze data sets to find patterns that guide companies towards the hottest trends in the market. Your insights make sure businesses are always one step ahead.

    5. Data Architect: The Blueprint Designer

    You’re an architect, but instead of buildings, you design the data structures that hold a company’s information. Just like an architect plans a house, you create the blueprint for how data flows, making sure it’s efficient, organized, and ready for action.

    Remember, these roles are like pieces of a puzzle that come together to transform raw data into goldmine insights. Whether you’re building algorithms, spotting trends, or designing data structures, you’re the hero behind the scenes making businesses smarter, sharper, and ready for whatever the future holds!

     

    Experts are heavily preferred over the general data scientist

    In the data science and analytics community, experts are heavily preferred over the general data scientist – that’s just the way. We naturally believe that in some role
    Or more expertise is a sure way to guarantee success for a business outcome. Unfortunately, it’s not that easy. While experts are excellent at reconstruction work, their
    well practiced,

    WHAT IS THE FUTURE OF DATA SCIENCE IN 2023

    Data Science is foundation for machine learning and AI.

    Jobs in data science are predicted to increase by 30%, according to an IBM analysis. In 2023, there will be an anticipated 2,720,000 employment openings for data scientists. Additionally, the US Bureau of Labour Statistics predicts that by 2026, 11 million new employment would have been generated.

    Every business seeks to maximise earnings. Every sector has realised that it needs data scientists to play with data in order to maximise corporate profitability since data is the main component of data science. The need for careers in data science is due to this.

    All of this makes it clear that the finest occupations in the world today are those of data scientists, analysts, engineers, or business analysts.

    Additionally, data is providing a wealth of opportunities for data scientists in all significant public and private sectors worldwide. We will now examine the industries where employment in data science might be produced in 2023.

     

    Data scientist role and responsibilities

    • Data Analytics and Exploration.
    • Machine Learning and Statistical Modelling.
    • Data Visualization
    • Predictive Modeling and Forecasting

    The Data Detective: Investigating Insights

    Imagine you’re a detective in a data world. Your mission? To dig deep into mountains of data and find hidden treasures. Data Scientists do just that! They collect, clean, and organize data like puzzle pieces. Then, using their magical magnifying glass (also known as algorithms), they uncover patterns that help businesses make better decisions.

    Fortune Telling with Numbers: Predicting the Future

    Ever heard of predicting tomorrow’s weather or the next big trend? Data Scientists do it all! By analyzing past data, they create crystal balls of algorithms that forecast what might happen next. Just like wizards foreseeing future events, Data Scientists help companies prepare for what’s coming, whether it’s stocking up on umbrellas or crafting the next hit product.

    Teaching Computers to Think: Machine Learning Maestros

    Imagine having a pet robot that learns tricks from you. That’s what Data Scientists do with computers! They teach machines to learn from data, so the machines can do things on their own. From suggesting songs you’ll like to helping doctors diagnose diseases, Data Scientists make computers super smart.

    Storytellers of Data: Creating Visual Magic

    Remember your favorite storybook with captivating pictures? Data Scientists are like modern-day storytellers, but with data. They turn boring numbers into stunning visuals, like colorful graphs and interactive charts. This makes it easier for everyone to understand the story the data is telling.

    Business Wizards: Advising and Innovating

    Imagine you own a magical store, and you want to know which items your customers love the most. Data Scientists help businesses with exactly that! They analyze data to reveal what customers want, so businesses can make products that people can’t resist. They’re also the brains behind new inventions and improvements, making the world a more exciting place.

    Making a Better World: Solving Big Problems

    Ever thought about saving the planet with data? Data Scientists are on it! They work on things like predicting earthquakes, stopping pollution, and even curing diseases. By crunching data numbers, they create solutions that have the power to change the world for the better.

    Becoming a Data Hero: Your Journey Begins

    Ready to join the Data Scientist league? Start by learning about numbers, coding, and the magic of statistics. With the right skills, you could be the one making apps that recognize your voice or helping farmers grow more food. The world is your playground, and data is your key to unlocking its secrets!

    Real-World Adventures:

    Imagine you work for an online store. By digging into data about what people buy and when they buy it, you might discover that customers tend to buy more winter coats when the temperature drops. This could lead to a “Winter Coat Sale” exactly when people are ready to shop for warmth. And just like that, you’ve helped boost sales and keep customers happy!

    Problem Solving Guru:

    Imagine you’re helping a healthcare company. They want to figure out if a new drug they’re developing is effective. You’ll use historical patient data to see if the drug really makes a difference. This is like solving a jigsaw puzzle, but with numbers and facts!Customer Secrets:

    Imagine you’re helping an online streaming service. They want to know what shows people like the most. By analyzing viewing habits and ratings, you can reveal which genres and actors are most popular. This helps the service recommend shows that people are likely to love.


    Finding Hidden Gold in Data:

    You’re a modern-day prospector, sifting through data to find nuggets of gold. Let’s say you’re working with a transportation company. By analyzing travel patterns and routes, you can suggest more efficient routes that save time, fuel, and money.

    Conclusion: Embrace Your Inner Data Wizard

    So, brave souls, Data Science might sound like a complex spell, but it’s really about making sense of the digital universe. Every click, every like, every step you take online generates data. And with Data Scientists on the case, this data becomes a powerful tool to shape the world. So, don your cape, grab your coding wand, and let the data adventures begin!

    Data analysis and exploration

    Large and complex datasets will be gathered, cleaned, and preprocessed by you. Exploratory data analysis (EDA) is used in this to comprehend the data, spot trends, and derive insights.

    1. Data Cleaning and Preprocessing:

     Data scientists often work with large and complex datasets that may contain missing values, outliers, or inconsistencies. They perform data cleaning tasks to ensure the data is accurate, consistent, and ready for analysis. This involves handling missing data, removing outliers, resolving inconsistencies, and transforming data into a suitable format.

    2, Exploratory Data Analysis (EDA):

     EDA involves examining and summarizing the main characteristics of the data. Data scientists use various statistical techniques and visualization tools to understand the data’s categorizing , identify patterns, detect anomalies, and uncover relationships between variables. EDA helps to generate hypotheses, guide feature selection, and inform subsequent modeling decisions.

    3. Statistical Analysis:

    Data scientists apply statistical techniques to gain insights from the data. They use descriptive statistics to summarize and describe the data, inferential statistics to make inferences and draw conclusions about the population based on sample data, and hypothesis testing to validate assumptions and test relationships between variables.

    4. Data Visualization:

    Data scientists leverage data visualization techniques to effectively communicate insights and findings. They create visual representations such as charts, graphs, and interactive dashboards to present patterns, trends, and relationships in the data. Visualizations help stakeholders understand complex information quickly and make informed decisions.

    5. Feature Engineering:

    Feature engineering involves creating new features or transforming existing ones to improve the performance of machine learning models. Data scientists identify relevant features, apply mathematical transformations, combine variables, or extract useful information from the data to enhance the model’s predictive power.

    6. Dimensionality Reduction:

    In cases where the dataset has a high dimensionality, data scientists use dimensionality reduction techniques such as principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) to reduce the number of features while preserving the most important information. This helps simplify the analysis and visualization processes.

    Machine learning and Statistical Modeling

    interview questions for data analytics jobs

    To address business issues, you will create and use a variety of machine learning algorithms and statistical models. This entails choosing the right models, developing, testing, and fine-tuning their parameters.

    1 Model selection and assessment:

    The right machine learning models for a specific problem must be chosen by data scientists. They take into account elements like the type of data being used, the type of problem (classification, regression, clustering, etc.), and the particular demands of the assignment.

    2. Feature Selection and Engineering:


    Data scientists identify the most relevant features that contribute to the predictive power of the model. They use techniques such as statistical tests, correlation analysis, and domain knowledge to select the most informative features. Additionally, they may engineer new features by transforming, combining, or extracting meaningful info.

    3.Training and Fine-tuning Models:

    Data scientists train machine learning models using labeled data. They split the data into training and validation sets, feed it to the model, and adjust the model’s parameters to minimize errors or optimize specific objectives. This process involves selecting appropriate algorithms, tuning hyperparameters, and utilizing techniques like cross-validation to prevent overfitting and ensure the model generalizes well to unseen data.

    5. Ensemble Methods:

    Data scientists often employ ensemble methods, such as random forests, gradient boosting, or stacking, to combine multiple models and improve predictive accuracy. Ensemble methods harness the diversity and complementary strengths of individual models to make more robust predictions.

    6. Deep Learning:

    In recent years, deep learning has gained prominence in various domains. Data scientists may work with deep neural networks for tasks such as image classification, natural language processing, or sequence prediction. They use frameworks like TensorFlow or PyTorch to design, train, and fine-tune deep learning models with large-scale datasets.

    Data Visualization

    In order to convey complicated findings and insights to stakeholders, you will produce visualisations and reports. This includes presenting data in an understandable way by using tools like charts, graphs, and dashboards.

    1.Communicating Data:

    Data visualization helps in effectively communicating complex information, patterns, and trends in a visual and intuitive manner. It enhances understanding and enables stakeholders to grasp insights quickly, facilitating decision-making processes.

    2. Selecting the Right Visualizations:

    Data scientists choose appropriate visualizations based on the nature of the data and the specific objectives. Common types of visualizations include bar charts, line charts, scatter plots, histograms, pie charts, heatmaps, treemaps, and network diagrams. The choice of visualization depends on the variables being analyzed, the relationships between them, and the intended message.

    3.Exploratory Visualization:

    Data scientists use visualizations to explore and analyze data during the exploratory data analysis (EDA) phase. They create visual representations to examine the distribution of variables, detect outliers, identify patterns, and uncover relationships between data elements. Exploratory visualizations help generate insights and guide subsequent analysis.

    4.Storytelling with Data:

    Data visualization is a powerful tool for storytelling. By carefully designing and arranging visual elements, data scientists can construct a compelling narrative around the data. They organize visualizations in a logical flow, highlight key findings, and emphasize the main message they want to convey.

    5.Interactive Visualizations:

    Interactive visualizations enable users to interact with the data and explore different aspects dynamically. Data scientists develop interactive dashboards and visualizations using tools like D3.js, Tableau, Power BI, or Python libraries such as Matplotlib, Plotly, or Seaborn. Interactivity allows users to filter, drill down, zoom in/out, and customize visualizations to gain deeper insights.

    5. Geographic Visualization:

    Geographic data visualization involves representing data on maps or geographical coordinates. Data scientists use techniques such as choropleth maps, bubble maps, and heatmaps to visualize spatial patterns, regional variations, and relationships between variables across different locations.

    6.Temporal Visualization:

    Temporal visualizations show how data changes over time. Data scientists use line charts, area charts, time series plots, or animated visualizations to explore trends, seasonality, and patterns in time-dependent data. Temporal visualizations are effective in understanding historical data, forecasting, and identifying patterns in time series data.

    7.Data Dashboarding:

    Data scientists create interactive dashboards that consolidate multiple visualizations and key performance indicators (KPIs) into a single interface. Dashboards provide a holistic view of the data, enabling stakeholders to monitor metrics, track progress, and make informed decisions in real-time.

    Experimental design and A/B testing

    Experiment: Cooking Up Curiosity

    Imagine you’re in a kitchen, trying to create the perfect recipe for a delicious cake. Experimental design is just like that! Scientists and savvy businesses cook up experiments to answer questions. They mix different ingredients (or variables) and see how they affect the outcome. It’s all about turning curiosity into solid answers.


    The A and the B: Let the Battle Begin

    Ever had to choose between two different ice cream flavors? That’s the spirit of A/B testing! Let’s say you’re designing a website. You wonder if a blue “Sign Up” button gets more clicks than a green one. A/B testing sets up a battle: A is the blue button, and B is the green button. By comparing how people interact with both, you find out which one wins the popularity contest.


    The Wizardry of Randomness: Creating Fair Tests

    Imagine you’re a magician hosting a magical contest. You want to make sure everyone has an equal chance to win, right? That’s where randomness comes in! In experiments and A/B testing, we use random assignment. It’s like shuffling cards before a game. This ensures that each group (A and B) is a fair representation of the whole crowd.


    Crunching the Numbers: Analyzing Results

    Ever played a game and tallied up the scores to see who won? That’s exactly what happens after an experiment or A/B test. Data is collected, numbers are crunched, and voila! The winner emerges. Statisticians, who are like math detectives, help make sense of the data. They use their magic to decide if the results are reliable or just coincidental.


    Hunting for Insights: Unearthing Discoveries

    Imagine you’re a treasure hunter on a quest for a hidden chest of gold. In the world of experiments, data is the treasure, and insights are the gold. A/B testing helps you find out which changes work better and why. Maybe people prefer big fonts on a website or shorter videos for better attention. Insights like these guide decisions and make things awesome!

    You’ll plan studies and run A/B tests to evaluate the effectiveness of various treatments or adjustments. Making data-driven judgments and evaluating the efficacy of tactics are both aided by this.

    Experimental design and A/B testing are important methodologies used in data science to assess the impact of changes or interventions and make data-driven decisions. Here’s an overview of these concepts:

    1. Experimental Design:

    Experimental design refers to the process of planning and organizing experiments to obtain reliable and meaningful results. It involves defining research questions, identifying variables, designing treatments or interventions, and specifying the control groups. Experimental design aims to minimize bias, confounding factors, and sources of variability to ensure the validity and reliability of the experiment.

    2. Treatment and Control Groups:

    In experimental design, participants or subjects are divided into different groups. The treatment group receives the intervention or change being tested, while the control group does not receive the intervention and serves as a baseline for comparison. Random assignment is typically used to allocate participants to groups, ensuring that any observed differences between the groups are not due to pre-existing factors.

    3. A/B Testing:

    A/B testing, also known as split testing, is a specific form of experimental design used in marketing, user experience (UX), and web development. It involves comparing two versions of a webpage, advertisement, or user interface (A and B) to determine which performs better in terms of a specific metric, such as conversion rate, click-through rate, or user engagement. A random sample of users is assigned to each version, and their interactions and behavior are analyzed to determine the impact of the changes.

    4 Hypothesis Testing:

    In both experimental design and A/B testing, hypothesis testing is employed to determine if the observed differences between groups are statistically significant or simply due to chance. Data scientists formulate null and alternative hypotheses and use statistical tests, such as t-tests, chi-square tests, or ANOVA, to analyze the data and make inferences about the population based on the sample data.

    5.Sample Size Determination:

    Determining the appropriate sample size is crucial for the validity and power of an experiment. Data scientists use statistical power analysis to calculate the required sample size, taking into account the desired level of significance, effect size, and statistical power. A larger sample size generally leads to more precise and reliable results.

    6.Data Collection and Analysis:

    During the experiment, data scientists collect relevant data to evaluate the impact of the intervention. This may include quantitative metrics, user feedback, survey responses, or other forms of data. The collected data is then analyzed using statistical methods to assess the differences between groups and draw conclusions.

    7. Drawbacks and Considerations:

    Experimental design and A/B testing have certain limitations and considerations. These include potential biases, such as selection bias or sampling bias, that may affect the generalizability of the results. Data scientists need to carefully design experiments, control for confounding factors, and ensure that the observed effects are meaningful and not spurious.

    Experimental design and A/B testing provide rigorous methodologies for testing hypotheses, optimizing interventions, and making data-driven decisions. They help organizations understand the impact of changes, evaluate different strategies, and continuously improve their products, services, or user experiences.

    Syllabus for Data Science

    Introduction to Data Science:

    • Overview of data science and its applications
    • Introduction to data types and data formats
    • Basics of programming languages commonly used in data science (such as Python or R)

    Mathematics and Statistics Foundations:

    • Descriptive statistics (mean, median, mode, variance, standard deviation, etc.)
    • Probability theory
      Inferential statistics (hypothesis testing, confidence intervals, etc.)
    • Linear algebra (vectors, matrices, matrix operations)
    • Calculus (differentiation, integration)
    • Data Manipulation and Analysis:

    Data cleaning and preprocessing


    • Exploratory data analysis (EDA)
    • Data visualization techniques
    • Feature engineering


    Machine Learning:

    • Introduction to machine learning concepts and algorithms
    • Supervised learning (linear regression, logistic regression, decision trees, random forests, support vector machines, etc.)
      Unsupervised learning (clustering, dimensionality reduction, etc.)
      Model evaluation and validation techniques
      Deep Learning and Neural Networks:

    Introduction to neural networks

    • Deep learning frameworks (TensorFlow, PyTorch, etc.)
    • Convolutional neural networks (CNNs) for image data
    • Recurrent neural networks (RNNs) for sequential data


    Big Data Technologies:

    • Introduction to big data concepts
    • Distributed computing frameworks (Hadoop, Spark)
    • Data storage technologies (HDFS, NoSQL databases)
    • Data processing and querying (MapReduce, Spark SQL)


    Data Science Tools and Libraries:

    • Introduction to data science libraries (NumPy, Pandas, Scikit-learn, Matplotlib/Seaborn, etc.)
    • Data manipulation and analysis using Python or R
    • Version control systems (Git)
    • Integrated development environments (IDEs) and Jupyter notebooks
      Ethical and Legal Issues in Data Science:

    Data privacy and security

    • Ethical considerations in data collection and analysis
    • Bias and fairness in machine learning models
    • Real-world Applications and Case Studies:
    • Practical applications of data science in various industries (finance, healthcare, marketing, etc.)


    Case studies and projects demonstrating the application of data science techniques to solve real-world problems
    Capstone Project:

    A substantial project where students apply their knowledge and skills to tackle a significant data science problem, often in collaboration with industry partners or mentors.

    A data scientist is responsible for collecting, analyzing, and interpreting complex data to inform business decisions, solve problems, and discover insights. They use various tools and techniques to extract meaning from data and create models that can predict future trends and patterns.

    Data scientists are typically responsible for tasks such as data cleaning and preprocessing, exploratory data analysis, feature engineering, model selection and training, validation, and deploying models into production. They also collaborate with domain experts and stakeholders to understand business goals and formulate data-driven solutions.

    Data scientists need proficiency in programming languages like Python or R, data manipulation libraries (e.g., Pandas, NumPy), machine learning frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch), and SQL for database querying. They should also be familiar with data visualization tools (e.g., Matplotlib, Seaborn) and have a solid understanding of statistics and probability.

    Domain knowledge is crucial as it helps data scientists understand the context of the data they’re working with. It enables them to ask the right questions, validate results, and translate technical findings into actionable insights for business stakeholders.

    Data scientists collaborate with various teams, such as business analysts, engineers, and managers. They communicate findings, explain technical concepts, and work together to align data projects with organizational goals. Collaboration ensures that data-driven decisions are well-integrated into the overall strategy.

    While both roles deal with data, data scientists are more focused on building predictive and prescriptive models, often involving complex machine learning algorithms. Data analysts, on the other hand, primarily focus on descriptive analysis, creating reports, and visualizations to help organizations understand historical data and make informed decisions.

    Ethical considerations are important in data science. Data scientists should handle sensitive information responsibly, ensure privacy and data protection, and avoid biases in their models. Regular audits and checks on model fairness and transparency are also essential.

    The process typically involves: defining the problem, collecting and preprocessing data, exploring and analyzing the data, feature engineering, selecting an appropriate algorithm, splitting data into training/validation/test sets, training the model, tuning hyperparameters, evaluating its performance, and deploying the model into production.

    Data scientists often read research papers, attend conferences (e.g., NeurIPS, ICML), participate in online forums and communities, take online courses, and follow blogs and social media accounts of experts in the field to stay updated with the latest developments.

    A/B testing is a method used to compare two versions (A and B) of a webpage, app, or other digital asset to determine which one performs better in terms of user engagement or other desired metrics. Data scientists design and analyze A/B tests to make informed decisions about changes to products or services

    What is deep Learning in Data science explain with example?
    What is deep learning in Data Science
    10Dec, 2021
    What is deep Learning in Data science explain with example?

    As Data turns into the main thrust of the advanced world, essentially everybody has coincidentally found such terms as data science, AI, deep learning, and data mining.

    Deep learning is a class of AI algorithms that utilizes numerous layers to continuously extract high-level features from the raw input.

    Deep learning is a subset of Machine Learning, which then again is a subset of Artificial Intelligence. Man-made consciousness is an overall term that alludes to methods that empower PCs to emulate human conduct. AI addresses a bunch of calculations prepared on information that make the entirety of this conceivable.

    Deep learning is a subset of AI that prepares a PC to perform the human-like task, like such as picture distinguishing proof forecast making and speech recognition.

    What is AI

    Artificial Intelligence(AI) is a wide-going part of software engineering, worried about building brilliant machines fit for performing assignments that normally require human intelligent.

    Artificial Intelligence is the replication of human intelligence in computers.

    Artificial intelligence (AI) is subsequently, founded on the possibility of the capacity of a machine or PC program to think(reason), comprehend and learn like people.
    From the definition of intelligence, we can also say that artificial Intelligence is the study of the possibility of creating machines able to apply knowledge received from data in manipulating the environment.

    Example of AI

    If you look particular type of content on youtube or Netflix such as comedy movies it shows you option of a list of comedy movies to choose in the future as per your taste.

    What is machine learning

    Man-made reasoning is extremely immense. Machine learning(ML) is a subset of Artificial Intelligence. That is the place where ML comes in.
    Machine learning(ML) is a bunch of statistical instruments to gain from information.
    The core of ML is in showing PCs how to take in and make forecasts from information without essentially being customized.

    Machine learning is a technique for information investigation that robotizes analytical model structure. It is a part of artificial intelligence dependent on the possibility that frameworks can gain from data, identify patterns and make decisions with minimal human intervention.

    what is deep learning ?

    Deep Learning, on the other hand, is just a subset  of Machine Learning, inspired by the structure of a human brain. Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks.

    What is a neural network?

    Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.

    A neural  network has many layers. Each layer performs a specific function, and the complex the network is, the more the layers are.
    That is the reason a neural network  is additionally called a multi-facet perceptron.
    The most flawless type of a neural organization has three layers:

    • The input layer
    • The hidden layer
    • The output layer

    As the names recommend, every one of these layers has a particular reason. These layers are comprised of hubs.

    1. The input layer picks up the input signals and transfers them to the next layer.
    2. It gathers the data from the outside world.
    3. The hidden layer performs all the back-end tasks of calculation.
    4. A network can even have zero hidden layers

    you will have to train a neural network with some training data as well, before you provide it with a particular problem.

    Why Deep Learning is well known now days?

    Why Deep Learning is well known now days?

    The Deep learning market was worth USD 2.28 Billion in 2017 and is relied upon to arrive at USD 18.16 Billion by 2023, at a CAGR of 41.7% from 2018 to 2023. The base year considered for this review is 2017, and the figure time frame is from 2018 to 2023.

    Deep Learning is acquiring a lot of prominence because of it’s matchless quality as far as exactness when prepared with gigantic amount  of data. The product business now-a-days moving towards machine learning. AI has become vital in each area as a method of making machines Intelligent.

    The most important difference between deep learning and traditional machine learning is its performance as the scale of data increases. When the data is small, deep learning algorithms don’t perform that well. This is because deep learning algorithms need a large amount of data to understand it perfectly.

    Some time before Deep learning was utilized, conventional AI techniques were basically utilized.

    Deep learning functions as follows:

    1. It first distinguishes what are the edges that are generally applicable to discover object.
    2. It then, at that point, expands on this progressively to track down what blend of shapes and edges we can find. For instance, regardless of whether specif quality are available,on object.
    3. After consecutive hierarchical identification of complex concepts, it then decides which of these features are responsible for finding the answer.

    Feature Extraction aims to reduce the number of features in a dataset by creating new features from the existing ones (and then discarding the original features). These new reduced set of features should then be able to summarize most of the information contained in the original set of features.

    Because this type of learning acts as a powerful brain, a large amount of data for training is required. So much data is needed that before big data and cloud computing, the amounts of data and processing power were not easily available. But just because there needs to be a lot of data, it does not mean the data must be structured. Deep learning can process both unlabeled and unstructured data. This learning method also creates more complex statistical models. With each new piece of data, the model becomes more complex, but it also becomes more accurate.

     

    Because this type of learning acts as a powerful brain, a large amount of data for training is required. So much data is needed that before big data and cloud computing, the amounts of data and processing power were not easily available. But just because there needs to be a lot of data, it does not mean the data must be structured. Deep learning can process both unlabeled and unstructured data. This learning method also creates more complex statistical models. With each new piece of data, the model becomes more complex, but it also becomes more accurate.

    Deep learning in data science

    Since this kind of learning goes about as an incredible cerebrum, a lot of data for preparing is required.

    So much data is needed that before big data and cloud computing, the amounts of data and processing power were not easily available.
    But just because there needs to be a lot of data, it does not mean the data must be structured.
    .

    Deep learning can deal with both unlabeled and unstructured data.
    This learning technique additionally makes more statistical measurable models. With each new piece of information, the model turns out to be more complicated, yet it additionally turns out to be more precise.

    How deep learning utilizes a hierarchical level of artificial

    Deep learning, on the other hand, is a subset of machine learning that does machine learning using a hierarchical level of artificial neural networks. Artificial neural networks are constructed in the same way as the human brain, with neuron nodes connected in a web-like pattern. While typical programmes develop analysis using data in a systematic way, deep learning systems’ hierarchical function allows machines to process data in a non-linear manner.

    Examples of Deep Learning

    Translations
    People are migrating across countries and companies are giving their products and services to people all over the world, making the world smaller than it has ever been. While this is excellent news, the most significant stumbling factor is language. Wouldn’t it be simpler if everyone spoke the same language? Deep learning algorithms, thankfully, can assist in this aspect by automatically detecting and translating from one language to another. Deep learning can assist a visitor in need of directions or even government officials meeting to discuss vital economic reforms.

    Customized Experiences

    As a last example, suggestions offered by sites like Amazon and Netflix are another deep learning function we witness on a daily basis. They offer recommendations based on our history, and this strategy has helped many of us discover items, films, and TV shows we didn’t even know existed. The recommendations function will only improve as technology advances in the coming years.

    Recognition of Facial Expressions

    In fact, facial recognition has a wide range of applications, therefore deep learning is very useful in this area. Some businesses, for example, are implementing facial recognition stations for their personnel. Other services, such as Facebook, are employing deep learning to recognise faces in photos in this way. Customers may be able to finalise orders and pay for things using facial recognition in the future.

    Defense and Aerospace

    Did you know that deep learning can help with national security issues? Satellites that need to detect areas of interest are one application. Deep learning could potentially be useful in the military. Rather than taking excessive risks, officers will be able to identify not only safe zones but also danger zones using deep learning.

    Medical Investigations

    Deep learning is being used in the medical business, and it’s now being used to detect cancer cells, as we discussed earlier. Cancer researchers at UCLA were able to employ deep learning in a high-tech microscope. The microscope can reliably distinguish cancer cells from other cells by using high-dimensional data.

    Virtual Personal Assistants

    Next, you presumably have Cortana, Alexa, or Siri in your home (most do these days), and they all use deep learning. Deep learning is necessary by companies like Google and Apple to interpret our speech. If you’ve ever wondered how Siri understands what to say in response to our queries, deep learning is the answer.

    DATA ANALYTICS TRAINING TO PLACEMENT
    BEST DATA SCIENCE COURSE IN DEHRADUN
    Top Data Analytics beginner Jobs An overview 2023
    Awesome Data Science Jobs Where You Don’t Need Any Coding Skills
    4Dec, 2021
    Does entering into data science job require coding ?

    Indeed, the facts confirm that coding is required in numerous Data science occupations. Indeed, it is actually the case that when you expect to join an Data science group that grows new applications that you want to code. And furthermore, following 8 years in the Data science field, I (at times) actually code and do code audits.
    However, there are many organizations and positions out there where individuals in different information science jobs can join, and not all need to have coding skills.
    I will likely empower individuals with different foundations, abilities, and interests to move into the Data science field and have an effective excursion.

     

    My goal is to enable people with various backgrounds, skills, and interests to move into the data science field and have a successful journey even if they dont have coding skills at all.

    Start your data science with vISTA aCADEMY without programming a single line.

    A many individuals began thinking of acquiring information on data science after it turned into an exceptionally well-known professional decision. Various people, then again, were debilitated by the possibility that one should be a developer to learn information science. Many individuals accept that AI is just conceivable with Python.

    However, I can guarantee you, and this is not correct. Data science is a crucial subject that you ought to comprehend. Prior to learning Python, you can take care of some certifiable issues with next to no programming information!

    Data science is totally founded on  any perspective with respect to information highlights, information quality, and exactness. In this manner, it doesn’t make any difference assuming you are prepared to utilize Python programs or different devices.

    If you have dreams of becoming a data scientist or a data engineer, you’d probably see a black screen full of codes in that dream. Polishing your coding skills may be the popular advice you get on this journey. Yet, surprisingly, it has nothing to do with programming.

     

    Data science is the process of making sense from a raw collection of records. A programing language is only a tool. It’s like a container for cooking your meals. But the container itself is not the meal.

    Data Science Jobs Where You Don’t Need Any Coding Skills

    Data science strategy consultant

    Most organizations even battle with beginning the information and information science venture. They need instruction about the potential outcomes and business benefits of the utilization of information.
    What is the present regular attitude of tech organizations is as yet in its earliest stages at numerous different organizations. Moving to the information driven dynamic world is a difficult business change.
    For what reason do we want information science? How might we move into an information driven business? What do we want for that?
    Organizations need support in responding to these inquiries and setting up the vision and execution guide. They need help with developing abilities and support in evaluating instruments and suppliers.
    Above all, the way of life and attitude of all representatives need to change. How to change that? How to empower the representatives? What’s more how is an effective information science group develop? Where is it set in the association? What’s more where they get the information?
    An information science system expert produces for this multitude of inquiries arrangements. You develop the full information on an information researcher, just as system and hierarchical mastery. However, you don’t have to code.

    Technical writer for data science software

    The Data science process includes numerous exclusive programming and stages: ace information the board frameworks, investigation stages, or perception and BI instruments. Furthermore someone needs to acclimate the client with every one of the specialized functionalities and the use prospects.
    We as a whole use help capacities and instructional exercises of devices and stages. Programming documentation, guidance manuals, and activities direction depict how the product functions and what should be possible. It states how it must be incorporated into existing IT design and cycles.
    The occupation of a specialized essayist is requesting. They need to get programming and information science. They work with the designers, clients, and promoting. It isn’t just the substance that must be created and should be right yet additionally the plan, lastly, legitimate necessities ought to be met.
    A specialized essayist for information science programming should comprehend start to finish the product, the Data science cycle and techniques, the applications, and the brain research and conduct of the client. The client experience represents the deciding moment with crafted by the specialized essayist.
    You must be an undeniable information researcher with an expansive scope of associations and information, yet you don’t have to code.

    Recruiter for technology and data science people

    Enormous associations have particular enrollment specialists for the innovation, AI, and information science positions.
    These spotters need to comprehend the essentials of the information science work and cycles, the applications, and objectives. Moreover, they need a ton of sympathy and great correspondences abilities.
    I worked a great deal with them, not just examining the CVs of potential colleagues yet additionally about the necessities of up-and-comers to such an extent that they could set up the work advertisements and do the primary evaluation of the candidates. We talked about how to foster the information science group, and they give their view and proposition of kinds of abilities and contender to accomplish the put out objectives.
    These selection representatives have top to bottom information on information science and can distinguish defects in CVs or made-up abilities and experience.
    Not a couple of them additionally figure out how to program and move later in more specialized work positions.
    It’s a task that unites the correspondence, human and specialized sides.

    Sales representative of a data science (software & platform)

    We utilize a great deal of exclusive programming and stages from information the board, cloud, information science instruments, and representation and detailing during our data science work.


    A sales rep is liable for creating leads, arranging conditions with the likely client, setting up agreements, bringing the deal to a close, and guaranteeing after-deals administrations.
    Programming and stage are not sold by a PowerPoint show. You want to exhibit the worth and functionalities of the item to the clients.
    In this way, you know the highlights and uses of the item and stage. You realize the techniques and how to take care of issues. Specialized information overall data science process and the capacity to speak with data researchers and money managers are fundamental.
    You are an data scientist with deals and correspondence capacities.

    Data science project manager

    Data science individuals are not great venture chiefs. In this way, particularly in enormous activities or tasks that are important for a business change, a venture director should neglect the entire undertaking and facilitate every single included partner.
    A data science project administrator plans, plan, and execute information science arrangements. You measure the advancement of the project and evaluate the dangers. At the point when issues emerge, you should raise and tackle them. What’s more you need to guarantee that the perfect individuals are on the task.
    In this way, you really want to have business space information, start to finish information science abilities, an organized task the executives approach, and the capacity to oversee individuals. It is an exceptionally adaptable work. Be that as it may, you don’t require (fundamentally) to be a coding master.
    6. Information Visualization and business insight (BI) master
    In the business climate, information science results should be introduced or are important for a revealing. The crowd are finance managers with just a little information in information science.
    Setting up important and reasonable announcing and an incredible information perception that recounts to non-specialized individuals the entire story is a workmanship. Along these lines, in all information science groups of bigger corporates, you find specific individuals liable for creating and keeping up with the legitimate detailing and perception.
    Likewise, most counseling firms have such a specialist group that assists their customers with doing that.
    The instruments utilized by organizations are predominantly Tableau Qlik Sense / QlikView, MicroStrategy, ThoughtSpot, or Power BI or Power BI. When searching for such a task, look for these instruments and “BI master.” Then, guarantee that it is in close joint effort with the information science group.
    You needn’t bother with coding experience for these instruments despite the fact that you are creating dashboards, perceptions, and BI revealing. Be that as it may, you want the data science information to get the correspondence and messages directly into revealing.

    Data Scientist working with no-code tools

    At last, there is an expanding number of cutting edge stages and apparatuses in the market that don’t need coding abilities.
    The blast of the advancement of these stages has, from one perspective, to do with the lack of information researchers with applicable coding information and empowers less specialized individuals to perform complex information science demonstrating.
    Be that as it may, then again, it additionally diminishes the wellspring of mistakes and the blunder rates in codes and makes the advancement of prescient and prescriptive models quicker. This recoveries many expenses and upgrades the speed-to-showcase, which turns out to be increasingly more basic in the present business world.

    For more uttarakhand most

    Best data science Placement Uttarakhand
    Data Scientist Goverment jobs
    1Dec, 2021
    TOP GOVERNMENT DATA SCIENCE JOBS IN INDIA TO APPLY

    Is there any government jobs for data scientist?

    Indeed, you read that right! Government occupations in the field of information science do exist. … Government is perhaps the greatest datum authorities. Directly from enumeration information to public safety knowledge, all the Government offices depend intensely on prepared information researchers to help them in settling on better choices.

    Government Data Science Jobs expect possibility to add to the advancement of the country

    Government areas are presently expanding the utilization of innovation as they move towards cutting edge information bases rather than the paper-pen strategy for information arranging. The increment in information science use in India isn’t restricted to the arising tech organizations however isn’t found in the public authority area also. With this, there has been an expansion in information science occupations in the public authority area. Here are the essential subtleties:

    Data Analyst at Reserve Bank of India | New Delhi

    Details –
    Eligible candidates after checking all the details can apply for the RBI requirement
    The candidate is expected to own a postgraduate degree in mathematical statistics, mathematics, econometrics, statistics, economics, finance, computer science or B.Tech/BE in Computer Science click

    Project Assistant cum Data Analyst at ICMR | Bihar

    Details –
    He/she should be between 18-54 years old
    The candidate is expected to have certain educational qualifications. They are 10th, ITI, MBBS, PG Degree/Diploma
    Written test, interview, and document verification will be conducted for the selection
    Check details

    Analytics cum Chief Data Consultant at NABARD

    Details –
    The candidate should be less than 62-years of age
    He/she should have a minimum experience of seven years for graduates and five years for postgraduates in the field of Machine Learning and Artificial Intelligence
    The candidate is expected to hold a bachelor’s degree in computer science/engineering/mathematics/statistics.
    Check detail

    Data Scientist In Conservation - 21069

    • 6 to 8 years professional experience or 3 to 5 years with an advanced degree in data science, computer science, environmental science (with a quantitative focus) or related field
    • Proven data science or computer science expertise
    • Proven expertise in a data science programming language such as Python or R
    • click

    The Central Bank of India is recruiting 115 people in India under various profiles like Data Scientist, Economist, Income Tax officer and many more.

    Link

    Data Analytics Internship

    Data Analytics Internship
    Ministry Of Science and Technology, Government Of India

    Delhi

    Responsibilities:

    Coordinating between various data sources of the Government of India
    Cleaning and pre-processing of data, filling data gaps, and data mining
    Working on data interpretation and report generation
    Using basic machine learning tools for finding trends, patterns, and predictive and explorative data analytics
    Working closely with chief data officer, ministry of science and technology, to explore data-based governance improvement opportunities.
    Requirements:

    are available for full time (in-office) internship
    can start the internship between 18th Nov’21 and 23rd Dec’21
    are available for a duration of 6 months
    have relevant skills and interests

    click

    Commissionerate For Welfare Of Differently Abled Invites Application for Data Analyst Recruitment 202

    Data Analyst

    Job Location:

     

    Directorate for Welfare of the Differently Abled, No.5, Kamarajar Salai, Lady Willingdon College Campus, Chennai, 600005 Tamil Nadu

     

    Last Date: 27 December 2021

    Employment Type: Full Time

    Number of Vacancy: 4 Posts

     

    Data Analyst Jobs in Chennai at Directorate For Welfare Of The Differently Abled Persons – Apply Now

    Data Analyst Jobs in Chennai at Directorate For Welfare Of The Differently Abled Persons – Apply Now Recruitment 2021 Details

    Job Role Data Analyst

    Education Requirement

    Total Vacancy 4 Posts

    Job Locations Chennai

    Experience 2 – 5 years

    Salary 50000(Per Month)

    Posted on 15 Dec, 2021

    Last Date To Apply 27 Dec, 2021

    Educational Qualification: B.Sc

     

    1. RIGHTS Project: Inclusion, Accessibility & Opportunities for the Differently Abled Project in Tamil Nadu

    2. Post Name: Data Analyst – 04

    b. Excellent Power BI, Power Query and Advanced Excel Skills.

    c. Good knowledge in Databases: SQL Server, SSIS, SSAS, SSRS, Visual Studio 2010, MS Access.

    d. Languages: DAX, MySQL, Tabular Cubes, VBA

    e. Good Knowledge in Power BI, Excel – Power Pivot & Power Query, PowerPoint & Data Models.

    f. Good knowledge of Project management tools Microsoft Project Management, Asana. 

    g. Strong mathematical skills to help collect, measure, organise and analyse data.

    h. Strong analytical and conceptual skills.

    i. Knowledge of data modelling, data cleansing and data enrichment techniques

    j. Fluency in spoken and written Tamil and English is a must

    9. Remuneration:

    i. The remuneration for the Data Analyst would be in the range of Rs.50,000 per month inclusive of all. Only travel expenses will be paid as per actuals. The incumbent will be located at the Directorate for Welfare of the Differently abled, No.5, Kamarajar Salai, Lady Willingdon College Campus, Chennai – 600 005 during the course of the contracted period. The Data Analyst may require travel as per Project requirements

    ii. The Data Analyst will have to attend office at DWDA on all working days unless he/she is on official tour as approved by PD or allowed to work from home by PD.

    Pay Scale:

    INR

    50000(Per Month)

    Age Limit: As Per Rules Age

    click link

    Data science course and training
    Step up your Career in Data Science and achieve your Dream Job NOW!!
    1Dec, 2021
    Top Job titles in the World of Data Science for 2022

    Top 10 Data Science Careers and Demands in Industries

    Data scientist is “The Sexiest Job of the 21st Century,” according to the Harvard Business Review. Isn’t it enough to have a basic understanding of data science? When businesses are dealing with petabytes and exabytes of data, the era of Big Data has arrived. Until 2010, data storage became extremely difficult for industries. After popular frameworks such as Hadoop and others solved the storage challenge, the focus has shifted to data processing. Data Science plays an important role in this. Data science’s popularity has risen in recent years in a variety of ways, so one should prepare for the future by studying what it is and how we may add value to it.

    "What is Data Science?"

    “WHAT IS DATA SCIENCE?” 

    is the first question that comes to mind. 
    For various people, data science means different things, but at its core, data science is the use of data to answer questions. 
    This is a moderately broad definition, which is appropriate given that data science is a moderately broad field!
    The science of studying raw data using statistics and machine learning techniques in order to derive conclusions about that data is known as data science.
    In a nutshell, Data Science entails the following:
    • Mathematics, statistics, and computer science
    • Cleaning and formatting of data
    • Visualization of data
    Everyone knows how popular Data Science has become these days. 
    Now the questions are: Why Data Science (Decide the Goal First? ), and How Data Science

    Assuming you need to advance in your Career, do look at changed Career Paths planned to their separate Domains. Browse a bunch of Free directed courses. Gain admittance to our profession change programs in a flash. Take up the Data Science Foundations  Online Course and become familiar with the necessary abilities for you to assemble your information science vocation.

    Incredible Learning additionally offers different Data Science Courses and  projects that you can browse. Gain from industry-specialists through web-based mentorship meetings and devoted profession support with us.

    Data Scientist

    How about we start with the most Popular and demanded job: Data Scientist. As a Data Scientist you’ll manage all parts of a venture from knowing what’s essential to the business, to information assortment and examination, lastly to information representation and introductions.

    A Data Scientist is a handyman. Subsequently, they can offer bits of knowledge on the best answers for a particular venture while revealing bigger examples and patterns in the information. Besides, organizations frequently accuse information researchers of exploring and growing new calculations and approaches.

    In enormous organizations, group leads are frequently Data Scientist in light of the fact that their range of abilities permits them to regulate different representatives with particular abilities while directing a task beginning to end.

    Data Analyst

    Data analysts are responsible for a variety of tasks including visualisation, managing and processing of massive amounts of data. They also have to perform queries on the databases from time to time. One of the most important skills of a data analyst is optimization. This is because they have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.000000000000000000000000000000000000000000000

    Data Engineer


    Data engineers are answerable for planning, fabricating and keeping up with information pipelines. They need to test environments for organizations and set them up for information researchers to run their calculations. Information designs additionally work on clump handling of gathered information and match its configuration to the put away information.

    At long last, Data engineers need to keep the environment and the pipeline enhanced and effective to guarantee the information is accessible for information researchers and investigators to use without warning.
    Business intelligence developers — additionally called BI engineers — assume responsibility for planning procedures that permit organizations to observe the data they need to settle on choices rapidly and effectively. To do that, BI engineers should be open to utilizing new BI instruments or planning custom ones that give examination and business experiences.

    Business Intelligence Developer


    A BI designer’s work is for the most part business-situated so they need to have no less than an essential comprehension of the basics of business technique just as the intricate details of their organization’s plan of action.

    Statistician

    Statistician, as the name proposes, has a sound comprehension of factual speculations and information association.
    In addition to the fact that they extract and deal important bits of knowledge from the information groups, however they additionally assist with making new procedures for the specialists to apply.

    • Data collection, analysis, and interpretation
    • Using statistical methodologies/tools to analyse data, evaluate results, and forecast trends/relationships
    • Processes for collecting data
    • Findings dissemination to stakeholders
    • Advising/consulting on organizations business strategy on a regular basis

    Business Analyst

    The job of business examiners is marginally not the same as different information science occupations. While they do have a decent comprehension of how information situated advances work and how to deal with huge volumes of information, they likewise separate the high-esteem information from the low-esteem information. As such, they distinguish how the Big Data can be connected to significant business bits of knowledge for business development.

    The most basic job of a business intelligence analyst is to find patterns — and value — in company and industry data. In most companies, this is a data analyst position. BI Analysts must be comfortable analysing data, working with SQL, and performing data visualisation and modelling.

    Data and Analytics Manager

    A Data and Analytics Manager supervises the information science activities and doles out the obligations to their group as indicated by abilities and mastery. Their qualities ought to incorporate innovations like SAS, R, SQL, and so forth and obviously the executives.

     

    Data Architect

    An information engineer makes the plans for information the board so the data sets can be effortlessly coordinated, concentrated, and ensured with the best safety efforts. They additionally guarantee that the information engineers have the best devices and frameworks to work with.

    Data architect is one of the highest-paying data science occupations in the world, since it designs new database systems and uses performance and design analytics to improve the company’s interconnected data ecosystem. The ultimate goal is to make the data easily available to data scientists. It’s always been one of India’s greatest data science professions, and dealing with money — yours and others’ – is the stuff of fantasies.

    Database Administrator

    Once in a while the group planning the information base isn’t the group utilizing it. Right now, many organizations plan an information base framework dependent on explicit business prerequisites, however the organization purchasing the item will really deal with the framework. In such cases, an organization will employ an individual (or a group) to deal with the data set.

    Database Administrator overseer will screen the data set to ensure it works appropriately and monitor the information stream while making reinforcements and recuperation. Heads additionally manage security by conceding various authorizations to workers dependent on their work necessities and business level.

    Data Science Journey path

    Data science experts are needed in virtually every job sector—not just in technology. In fact, the five biggest tech companies—Google, Amazon, Apple, Microsoft, and Facebook—only employ one half of one percent of U.S. employees. However—in order to break into these high-paying, in-demand roles—an advanced education is generally required.

    “Data scientists are highly educated–88 percent have at least a master’s degree and 46 percent have PhDs–and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist,” reports KDnuggets, a leading site on Big Data.

    Here are some of the leading data science careers you can break into with an advanced degree.

    Data science has been viable in handling some genuine issues and is as a rule progressively embraced across ventures to drive more canny and better-educated direction. With the expanded utilization of PCs for everyday business and individual activities, there is an interest for keen machines, can learn human conduct and work designs. This brings Information science and enormous information examination to the bleeding edge.
    A review says that the worldwide Data science market is assessed to develop to USD 115 billion out of 2023 with a CAGR of ~ 29%. A report by Deloitte Access Financial matters says that a gigantic 76% percent of organizations have plans to expand their spend throughout the following two years on expanding their information logical abilities. Practically everything ventures can profit from information science and investigation. Notwithstanding, underneath are a few ventures that are better ready to utilize information science and investigation.

    DATA SCIENCE CAREER PATH TO BE SUCCESSFUL

    STEP BY STEP DATA SCIENCE CAREER PATH TO BE SUCCESSFUL

    Data science is needed wherever there is big data. As more and more industries begin to collect data on customers and products, the need for data scientists will continue to grow.

    The organization in today’s digital world, constantly looking for ways to turn that data into insight that improve business performance and profit.

    Notwithstanding, the companies are hiring data scientist who well versed in business, math, statistics, and computer science to leverage bid data.

    Data scientist need to build statistical model ,algorithms and present actionable insight as visualizations.

    What are Data scientist Skill set ?

    Statistics, Machine Learning and Programming

    The basis of a data scientist’s knowledge is a good hand on statistical concepts and machine learning models.
    These are the basic constructs through which a data scientist delivers insights.
    Beyond that, a data scientist must be proficient in at least one programming language.
    The most commonly used today is Python, but some data scientists use other languages like R, Java, or Node.js.

    Data Preparation

    The data preparation pipeline consists of the following steps

    Access the data.

    • Ingest (or fetch) the data.
    • Cleanse the data.
    • Format the data.
    • Combine the data.
      And finally, analyze the data.

    Model building

    The model building process involves setting up ways of collecting data,
    understanding and paying attention to what is important in the data to answer
    the questions you are asking, finding a statistical, mathematical or a simulation model
    to gain understanding and make predictions.

    Machine Learning Operations

    Machine learning operations (MLops) is a work method inspired by modern development practices,
    which enables data scientists to communicate better with DevOps, to create a streamlined workflow
    for machine learning development. This includes automation of
    processes like data ingestion, training and deployment in production.

    Big Data

    The majority of organizations deal with massive amounts of unstructured and structured data. It is typically the responsibility of the data scientist to handle big data operations. This typically involves preparing the data, working with multiple data sources, understanding the data ecosystem and its components.

    If you have a  degree, then you just need to kick it into overdrive by taking some extra courses on the following:

    • Data Science approach
      Statistics
      Machine learning
    • Data visualization
    • Data storytelling
    • Python for data science
    • R programming
    • SQL

    DATA SCIENCE CAREER PATH AND PROGRESSION​

    The data science career path from junior to senior data scientist varies greatly in skill level, responsibilities, daily tasks, and everyone’s favorite topic- total compensation.

    Data Science Intern & Entry-Level Data Scientist

    At this stage, data scientists are super raw and mainly work on developing their core technical skills, like SQL and Python. As such, tasks are generally straightforward and have a clear objective goal.

    Mid-Level Data Scientist

    After around one to two years of experience as an entry-level data scientist, you can transition into a mid-level data scientist role. Mid-level data scientists are advanced individual contributors that can take up larger project scopes and more ambiguous business problems.

    Senior Data Scientist Career Path

    The skills of a data scientist can be measured in interviews by testing speed and accuracy on technical problems, but also by evaluating communication skills. Then, on the job, many of these skills are more observable.Your value as a data scientist then corresponds to how much value you can add and the solutions you build relating to data.

    How To Get A Job As A Data Scientist

    • The Most Important Skills Should Be Acknowledged. A data scientist is a combination of a programmer, a statistician, a software engineer
    • Continue to learn. To stay ahead in the world of data science, you must stay on top of skill development.
    • Improve your communication abilities.
    • Getting a career as a data scientist necessitates more than just having a strong analytical.
    • When it comes to creating a career as a data scientist, it takes time to find the right employment.
    DOES FUTURE BELONG TO DATA SCIENCE| CAREER & SCOPE
    VISTA ACADEMY PIONEER IN DATA SCIENCE EDUCATION IN UTTARAKHAND
    How to Learn Python for beginners
    25Nov, 2021
    How to Learn Python fundamentals for beginners

    Python is a universally useful deciphered, intelligent, object-arranged, and significant level programming language. It was made by Guido van Rossum during 1985-1990. Like Perl, Python source code is likewise accessible under the GNU General Public License (GPL). This instructional exercise gives sufficient comprehension on Python programming language.

    For what reason to Learn Python?


    Python is a significant level, deciphered, intelligent and object-arranged prearranging language. Python is intended to be profoundly coherent. It utilizes English catchphrases much of the time where as different dialects use accentuation, and it has less grammatical developments than different dialects.

    Python is a MUST for understudies and working experts to turn into an incredible Software Engineer uncommonly when they are working in Web Development Domain. I will list down a portion of the vital benefits of learning Python:

    Python is Interpreted − Python is handled at runtime by the mediator. You don’t have to arrange your program prior to executing it. This is like PERL and PHP.

    Python is Interactive − You can really sit at a Python provoke and connect with the translator straightforwardly to compose your projects.

    Python is Object-Oriented − Python upholds Object-Oriented style or strategy of programming that epitomizes code inside objects.

    Python is a Beginner’s Language − Python is an incredible language for the fledgling software engineers and supports the advancement of a wide scope of uses from straightforward text handling to WWW programs to games.

    Attributes of Python

    Following are significant attributes of Python Programming −

    • It upholds practical and organized programming techniques just as OOP.
    • It tends to be utilized as a prearranging language or can be arranged to byte-code for building enormous applications.
    • It gives exceptionally undeniable level powerful information types and supports dynamic sort checking
    • It upholds programmed trash assortment.
    • It tends to be effectively incorporated with C, C++, COM, ActiveX, CORBA, and Java.

    Installing Python on Windows takes a series of few easy steps.

    Stage 1 − Select Version of Python to Introduce

    Python has different Version accessible, with contrasts between the punctuation and working of various renditions of the language. We really want to pick the rendition which we need to utilize or require. There are various variants of Python 2 and Python 3 accessible.

    Stage 2 − Download Python Executable Installer

    On the internet browser, in the authority webpage of python (www.python.org), move to the Download for Windows area.

    Python windows installation

    Stage 3 − Run Executable Installer

    We downloaded the Python 3.9.1 Windows 64 digit installer.

    Run the installer. Make a point to choose both the checkboxes at the base and afterward click Install New.

    On tapping the Install Now, The establishment interaction begins.

    The establishment cycle will require few moments to finish and when the establishment is fruitful, the accompanying screen is shown.

    Stage 4 − Verify Python is introduced on Windows

    To guarantee on the off chance that Python is succesfully introduced on your framework. Follow the given advances −

    Open the order brief.

    Type ‘python’ and press enter.

    The form of the python which you have introduced will be shown assuming that the python is effectively introduced on your windows.

    install python

    Stage 5 − Verify Pip was introduced

    Pip is an incredible bundle the board framework for Python programming bundles. Subsequently, ensure that you have it introduced.

    To check in case pip was introduced, follow the given advances −

    Open the order brief.

    Enter pip – V to check on the off chance that pip was introduced.

    The accompanying result shows up assuming pip is introduced effectively.

    We have effectively introduced python and pip on our Windows framework.

    How to Install Charm

    Here is a step-by-step process on how to download and install PyCharm IDE on Windows:

    Step 1) To download PyCharm visit the website https://www.jetbrains.com/pycharm/download/ and Click the “DOWNLOAD” link under the Community Section.

    Step 2) Once the download is complete, run the exe for install PyCharm. The setup wizard should have started. Click “Next.

    install pycharm
    Install pycharm

    Step 3) On the next screen, Change the installation path if required. Click “Next”.

    Stage 4) When it gets done, you can see a screen that says the Arrangement was effective. Presently click on “Close”.

    process pf installing charm

    Making First Program

    Stage 1) Open PyCharm Editorial manager. You can see the early on screen for PyCharm. To make another undertaking, click on “Make New Task”.

    f you’re on the Welcome screen, click New Project. If you’ve already got any project open, choose File | New Project from the main menu.

    First programme on pycharm 1

    Step 2 :Now Go up to the “File” menu and select “New”. Next, select “main File”.

    Step 3: Type the command print(” Hello this is my first programme “);

    close it with ;

    writing first programme in pycharm

    Step for run the programme 

    run pycharm programme
    Data Science Training and placement
    Basic Concepts of Statistics For Data Scientists and Analysts
    Top 10 Data Science Careers and Demands in Industries
    24Nov, 2021
    Top 10 Data science Careers and Demands in Industries

    Data science experts are needed in virtually every job sector—not just in technology. In fact, the five biggest tech companies—Google, Amazon, Apple, Microsoft, and Facebook—only employ one half of one percent of U.S. employees. However—in order to break into these high-paying, in-demand roles—an advanced education is generally required.

    “Data scientists are highly educated–88 percent have at least a master’s degree and 46 percent have PhDs–and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist,” reports KDnuggets, a leading site on Big Data.

    Here are some of the leading data science careers you can break into with an advanced degree.

    Data science has been viable in handling some genuine issues and is as a rule progressively embraced across ventures to drive more canny and better-educated direction. With the expanded utilization of PCs for everyday business and individual activities, there is an interest for keen machines, can learn human conduct and work designs. This brings Information science and enormous information examination to the bleeding edge.
    A review says that the worldwide Data science market is assessed to develop to USD 115 billion out of 2023 with a CAGR of ~ 29%. A report by Deloitte Access Financial matters says that a gigantic 76% percent of organizations have plans to expand their spend throughout the following two years on expanding their information logical abilities. Practically everything ventures can profit from information science and investigation. Notwithstanding, underneath are a few ventures that are better ready to utilize information science and investigation.

    Demand of Data sceince in Industries

    1. Retail

    Retailers need to effectively expect what their clients need and afterward give those things. On the off chance that they don’t do this, they will probably be left behind the opposition. Huge information and examination give retailers the bits of knowledge they need to keep their clients glad and getting back to their stores. One concentrate by IBM said that 62% of retail respondents asserted that experiences gave by investigation and data gave them upper hands.

    There are numerous ways retailers can utilize huge information and investigation to make their customers want more and more. For example, retailers can utilize enormous information and examination to make hyper-individual and important shopping encounters that make their clients profoundly fulfilled and more inclined to settling on buy choices.

    2. Medication

    The clinical business is utilizing enormous information and investigation amazingly to further develop wellbeing in an assortment of ways. For example, the utilization of wearable trackers to give significant data to doctors who can utilize the information to give better consideration to their patients. Wearable trackers likewise give data like whether the patient is taking his/her prescription and following the right treatment plan.

    Information aggregated after some time give doctors extensive data on patients’ prosperity and give considerably more noteworthy information than simply diminutive in-person visits.

    Enormous information and investigation can likewise help clinic administrators further develop mind and decrease holding up occasions. Clinical information is an extraordinary illustration of how suppliers can see a lot of information to track down designs and endorse suitable approaches.

    3. Banking and Finance

    The financial business is for the most part not took a gander at as being one that utilizes innovation a great deal. In any case, this is gradually changing as brokers are starting to progressively utilize innovation to drive their direction.

    For example, the Bank of America utilizes normal language handling and prescient investigation to make a remote helper called Erica to assist clients with survey data on forthcoming bills or view exchange accounts.

    Erica, the remote helper, is additionally prepared to get more brilliant with each exchange. Bank of America delegates say that the associate will ultimately concentrate on their clients’ financial propensities and recommend important monetary guidance at proper occasions.

    4. Development

    It is nothing unexpected that development organizations are starting to accept information science and examination amazingly. Development organizations track everything from the normal time expected to wrap up responsibilities to materials-based costs and everything in the middle. Large information is currently being utilized incredibly in the development business to drive better independent direction.
    Transportation
    There is always a need for people to reach their destinations on time and data science and analytics can be used by transportation providers, both public and private, to increase the chances of successful journeys. For instance, Transport for London uses statistical data to map customer journeys, manage unexpected circumstances, and provide people with personalized transport details.

    Public transport officials also use predictive analytics to keep things functioning smoothly. In 2017, Americans took 10.1 billion public transit trips. The substantial data generated from these trips can allow data scientists to analyze this data to ensure that all obstacles are properly dealt with.

    5. Communications, Media, and Entertainment

    Consumers now expect rich media in different formats as and when they want it on a variety of devices. Collecting, analyzing, and utilizing these consumer insights is now a challenge that data science is stepping in to tackle. Data science is being used to leverage social media and mobile content and understand real-time, media content usage patterns. With data science techniques, companies can better create content for different target audiences, measure content performance, and recommend on-demand content.

    For example, Spotify, the on-demand music streaming service, uses Hadoop big data analytics to collect and analyze data from its millions of users to provide better music recommendations to individual users.

    6. Education


    One challenge in the education industry where data science and analytics can help is to incorporate data from different vendors and sources and use them on platforms not designed for varying data.

    For example, the University of Tasmania with over 26,000 students has developed a learning and management system that can track when a student logs into the system, the overall progress of the student, and how much time is spent on different pages, among other things.

    Big data can also be used to measure teachers’ effectiveness by fine-tuning teachers’ performance by measuring against subject matter, student numbers, student aspirations, student demographics, and many other variables.

    7. Manufacturing and Natural Resources


    The increasing demand and supply of natural resources, such as oil, minerals, gas, metals, agricultural products, etc. has led to the generation of huge amounts of data that is complex, difficult to handle, and a prime candidate for big data analytics. The manufacturing industry also generates huge amounts of data that has so far gone untapped.

    Big data allows decision-making to be supported by predictive analytics in the natural resources industry. Large amounts of geospatial data, text, temporal data and graphical data can be analyzed using data science to ingest and integrate these large datasets. Big data also has a role to play in reservoir characterization and seismic interpretation, among others.

    8. Government

    Big data has many applications in the public services field. Places where big data is/can be used include in financial market analysis, health-related research, environmental protection, energy exploration, and fraud detection.

    One specific example is the use of big data analytics by the Social Security Administration (SSA) to analyze large numbers of social disability claims that come in as unstructured data. Analytics is being used to rapidly process medical information and detect fraudulent or suspicious claims. Another example is the use of data science techniques by the Food and Drug Administration (FDA) to identify and analyze patterns related to food-related diseases and illnesses.

    9. Energy and Utilities

    he energy and utilities industry generates and will continue to generate huge amounts of data that can be analyzed using big data analytics. For instance, nowadays, smart readers allow data to be collected every 15 minutes or so as compared to how it was previously when it was once a day. This data can be used to better study the consumption of utilities, which in turn allows for better control of utility use and improved customer feedback. The use of big data by utility companies also allows for improved asset and workforce management and is useful for identifying and correcting errors as soon as possible.

    10. Outsourcing Industry

    The value of the global data science and analytics outsourcing market was US$ 2.49 Bn in 2018 and is expected to grow to USD 19.36 Bn by 2027 at a CAGR of 25.8%. Factors driving this growth are shortage of skilled resources and high adoption by diverse industries.

    Outsourcing companies are not far when it comes to Data Science Services. They are making use of data science to automate back-office processes, keep prices in check, and shorten the turnaround time. Flatworld Solutions is one such company using artificial intelligence (AI) and machine learning (ML) to automate the back-end processes for clients to automatically classify and index documents, process PDF files, name and classify files, automatically discover documents, use image annotation for inventory management, and more.

    Conclusion: The term ‘Data Science’ was first coined in 2001 and it took less than two decades for it to become the phenomenon it is today. Finance was the first industry to understand data science advantages when no one could and used it to sift through and analyze large amounts of data and help companies reduce losses.
    Today, Data Science is a force to reckon with and almost all industries are trying to leverage it’s potential, and this number will only continue to increase as data science technology becomes more reliable and cost-effective. However, to capitalize on data science opportunities, you will need to understand industry-specific challenges, understand data characteristics of each industry, and match market needs with custom capabilities and solutions.

    data analytics training institute in Dehradun UK
    Concepts of Statistics For Data Scientists and analytics
    21Nov, 2021
    Basic Concepts of Statistics For Data Scientists and Analysts

    What is statistics ?

    Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data.

    It keeps us informed about, what is happening in the world around us. Statistics are important because today we live in the information world and much of this information’s are determined mathematically by Statistics Help.It means to be informed correct data and statics concepts are necessary.

    The main advantage of statistics is that information is presented in an easy way.

    Statistics is a well-known subject that focuses on data collecting, data management, data analysis, data interpretation, and data visualisation, among other things. Previously, statisticians, economists, and business leaders used statistics to calculate and portray significant data in their fields. Statistics is now used in a variety of industries, including data science, machine learning, data analyst roles, business intelligence analyst roles, computer science roles, and more.


    Certain statistical concepts, such as central tendency and standard deviation, are presented considerably earlier. There are many more significant statistical ideas for data science and machine learning that we must learn and apply. Let’s go over the fundamental terminologies and categories of statistics.

    Why is it important to understand statistics concepts?

    Almost every company nowadays is data-driven, and they use a variety of concepts to evaluate their data. 
    That’s when fundamental statistical principles and their applications come into play, assisting us in describing the data we have.

    Basic statistics concepts for becoming a data scientist

    Data scientists are in high demand and in some cases, data scientists are taking over legacy statistician roles. While a career in data science might sound interesting and available, prospective data scientists should consider their comfort with statistics before planning their next step

    Understand the Type of Analytics

    Descriptive analytics statistics

    Descriptive analytics is a field of statistics that focuses on gathering and summarizing raw data to be easily interpreted. Generally, descriptive analytics concentrate on historical data, providing the context that is vital for understanding information and numbers.

    Diagnostic Analytics 

     

    Diagnostic Analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It is characterized by techniques such as drill-down, data discovery, data mining and correlations.

    Predictive analytics

     
    Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns.
     

    Prescriptive analytics

    Prescriptive analytics is a type of data analytics—the use of technology to help businesses make better decisions through the analysis of raw data.It can be used to make decisions on any time horizon, from immediate to long term.

    Probability

    Probability is the measure of the likelihood that an event will occur in a Random Experiment. It deals with the chance (the likelihood) of an event occurring. For example, if you toss a fair coin four times, the outcomes may not be two heads and two tails If you tossed a coin 2,000 times. The results were 996 heads. The fraction 996/2,000 is equal to 0.498 which is very close to 0.5, the expected probability.

    Statistical formulas related to probability are used in many ways, including actuarial charts for insurance companies, the likelihood of the occurrence of a genetic disease, political polling, and clinical trials, according to Britannica.

    Conditional probability

    Conditional probability is calculated by multiplying the probability of the preceding event by the probability of the succeeding or conditional event. Conditional probability looks at the probability of one event happening based on the probability of a preceding event happening.

    Independent event

    Independent events are those events whose occurrence is not dependent on any other event. For example, if we flip a coin in the air and get the outcome as Head, then again if we flip the coin but this time we get the outcome as Tail. In both cases, the occurrence of both events is independent of each other.

    Mutually Exclusive Events:

    Two events are mutually exclusive if they cannot both occur at the same time. P(A∩B)=0 and P(A∪B)=P(A)+P(B).

    Bayes’ theorem,

    Bayes’ theorem, named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions

     

    Measures of Central Tendency

    A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data.

    Mean is also known as the arithmetic mean of the given data.

    Median is the middlemost value of the given grouped data if the data is grouped and arranged in ascending order.

    Mode is the value that appears most in the data.

    Variability

    Variability refers to how spread scores are in a distribution out; that is, it refers to the amount of spread of the scores around the mean.

    Range

    The difference between the highest and lowest value in the dataset.

    Percentiles, Quartiles and Interquartile Range (IQR)

    Percentiles 

    A measure that indicates the value below which a given percentage of observations in a group of observations falls.

    Quantiles—

    Values that divide the number of data points into four more or less equal parts, or quarters.

    Interquartile Range (IQR)

    A measure of statistical dispersion and variability based on dividing a data set into quartiles. IQR = Q3 − Q1.

    Variance:

    The average squared difference of the values from the mean to measure how to spread out a set of data is relative to the mean.

    Standard Deviation:

    The standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance.

    Relationship Between Variables

    The statistical relationship between two variables is referred to as their correlation. A correlation could be positive, meaning both variables move in the same direction, or negative, meaning that when one variable’s value increases, the other variables’ values decrease,

    Causality: 

    Relationship between two events where one event is affected by the other.

     

    Covariance:

     A quantitative measure of the joint variability between two or more variables.

     

    Correlation: 

    Measure the relationship between two variables and ranges from -1 to 1, the normalized version of covariance.

    Probability Distribution

    In probability theory and statistics, a probability distribution is a mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment.

    Bernoulli distribution


    A Bernoulli distribution is a discrete probability distribution for a Bernoulli trial — a random experiment that has only two outcomes (usually called a “Success” or a “Failure”). … The expected value for a random variable, X, for a Bernoulli distribution is: E[X] = p. For example, if p = . 04, then E[X] = 0.4

    uniform distribution


    In statistics, uniform distribution refers to a type of probability distribution in which all outcomes are equally likely. A deck of cards has within it uniform distributions because the likelihood of drawing a heart, a club, a diamond, or a spade is equally likely.


    Binomial distribution


    The binomial is a type of distribution that has two possible outcomes (the prefix “bi” means two, or twice). For example, a coin toss has only two possible outcomes: heads or tails and taking a test could have two possible outcomes: pass or fail. A Binomial Distribution shows either (S)uccess or (F)ailure.


    Normal distribution


    Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.


    Poisson distribution


    In statistics, a Poisson distribution is a probability distribution that is used to show how many times an event is likely to occur over a specified period

     

     

    Hypothesis testing

    Hypothesis testing in statistics is a way for you to test the results of a survey or experiment to see if you have meaningful results.

    Data sceience Programme for Beginners
    Best data analytics course in Uttarakhand
    Data analytics of Management & Commerce need to learn
    17Nov, 2021
    Why Data analytics for students of Management & Commerce ?

    Top 10 Reasons for Data Analytics for Commerce and Management Student

    Data analytics has a big impact on business and management, and it has several advantages for students studying these fields. Here are seven justifications for why data analytics is crucial for management and business students.

    1.Informed Decision-Making:

    Data analytics provides students with the ability to analyze and interpret data to make data-driven decisions. It equips them with the skills to evaluate information, identify trends, and draw insights that can guide strategic decision-making in business and commerce.

    2.Competitive Advantage:

    In today’s data-driven world, organizations that leverage data analytics gain a competitive edge. Students proficient in data analytics can help companies analyze market trends, consumer behavior, and competitors, enabling them to develop effective strategies and stay ahead of the competition.

    3,Improved Efficiency:

    Data analytics helps students identify inefficiencies and bottlenecks in business operations. By analyzing data, students can identify areas for improvement, streamline processes, and optimize resource allocation, leading to increased operational efficiency and cost savings.

    4.Market Research and Customer Insights:

    Data analytics allows students to analyze customer data, conduct market research, and gain insights into consumer preferences, buying patterns, and trends. This information is invaluable for businesses to tailor their products, services, and marketing strategies to meet customer needs and preferences.

    5.Risk Management:

    Data analytics helps students in managing risk by analyzing historical data, identifying patterns, and predicting potential risks and challenges. This enables businesses to develop risk mitigation strategies, enhance decision-making under uncertainty, and minimize potential losses.

    6.Financial Analysis:

    Data analytics is essential for students studying finance and commerce. It helps in financial analysis, forecasting, and budgeting by analyzing financial data and identifying key performance indicators. This information assists in making sound financial decisions and optimizing financial performance.

    7.Supply Chain Optimization:

    Students in management and commerce can use data analytics to optimize supply chain operations. By analyzing supply chain data, they can identify areas for improvement, optimize inventory levels, reduce costs, and enhance overall supply chain efficiency.

    8.Marketing and Sales Optimization:

    Data analytics provides students with the tools to analyze marketing and sales data, measure campaign effectiveness, and optimize marketing strategies. It helps in identifying target markets, evaluating marketing channels, and personalizing marketing efforts to drive sales and customer engagement.

    9.Entrepreneurship and Startups:

    For students interested in entrepreneurship or starting their own business, data analytics is crucial. It helps in market analysis, identifying potential opportunities, understanding customer needs, and making informed business decisions to create a successful venture.

    10.Career Opportunities:

    Proficiency in data analytics opens up a wide range of career opportunities for students in management and commerce. There is a growing demand for professionals who can effectively analyze and interpret data to drive business growth, leading to diverse career paths in fields such as business analytics, data science, market research, financial analysis, and consulting.

    In summary, data analytics offers students of management and commerce the ability to make data-driven decisions, gain a competitive advantage, improve efficiency, and enhance various aspects of business operations. It equips them with valuable skills that are highly sought after in today’s data-centric business landscape, opening up numerous career opportunities.

    A data Analytics for commerce students

    A Big Yes, Commerce students could do data analytics. It will require some investment, yet with little exertion reliably, anybody ready to learn Data analytics could learn it. Data Analytics is the field where huge amounts of data (both organized and unstructured) are gathered, refined, handled to bring significant information for stakeholders. A Commerce student could turn into a data analytic. For that, there are sure prescribed things that should be done to find some work as a Data Analylist.

    Data science is all about working with the existing data and identifying trends and patterns to comprehend the data so that the company can make the best decisions possible. Even if your background is in business, you may pursue a career in data science by attending Vista Academy and obtaining all the required skills.

    A B.Com. student can learn Python, right?

    Anyone may enrol in a Python course. It is much more helpful for those studying business. We must have the tools necessary to extract information from the growing amount of data being created.

    Can a student studying commerce use machine learning?

    Students studying commerce may also apply for these. The information and abilities needed for use in the workplace will be provided to the students. These involve using AI-driven technologies, data analytics, machine learning, deep learning, design, and programming.

    What is the starting pay for a data scientist in India?

    What does a Data Scientist make when they first start out in India? In India, the beginning salary for a data scientist is around 4.5 Lakhs (or 37.5k) per year. For a Data Scientist, one year of experience is the absolute minimum.

    Skill set and education

    • To begin with, you should be talented, and that implies you want to have strong information in Excel, SQL, programming progarmming  like R, Python, and so forth and information perception programming like Tableau, and so on
      When you get gifted, the following thing is to do as numerous modern standard tasks, you could do.
    • Then, getting a degree in an important field like math, statistics, IT, and so on will give you an edge while going for the Entry level employment, in the vast majority of the organization.

    Data analytics gives students from any stream to explore new career opportunities. Now the need of skill is more important than degree and practical training over rote learning .
    Even a simple job need a computer skill.


    Data is the future. Businesses today heavily rely on data-driven facts and statistics to make
    business decisions.


    In today’s highly competitive world, there is no longer scope for assumptions. So businesses base their decisions on the data that is engineered and analyzed by market experts.
    Data analytics allows affordable and accessible solutions to those who aim to learn new skills while managing their regular study or work schedule.


    Most of the non-tech students enrolled in our training have either pursued or were pursuing a Bachelor of Business Administration (B.B.A.) or Bachelor of Commerce (B.Com).

    Why to learn Data analytics ?


    Data analysis is the need of the hour -It is very important for every business to run smoothly, a data-driven approach is a must.
    In future every company will be tech company and data is engine to run it.
    Finance professionals and commerce students have an edge in learning analytics. They have strong quantitative
    skills and a solid understanding of statistics. They also have a robust understanding of business operations and the financial health of companies. Their understanding of finance and management gives them a leg up
    on the competition. Numeracy does, after all, lie at the heart of analytics as well as business development.

    Advantage of being a commerce student for studying data analytics

    • Your finance background can be an invaluable asset if you become a data scientist.
    • Your familiarity with numbers and business can be your secret weapon.
    • They can help you identify correlations that could escape others.
    • Your understanding of financial data is a big plus too
      Analytics is deployed extensively in the field of finance.

    what are data analytics key skills ?

    In general, investment professionals in data science and other fintech careers need the following skills
    to be successful:

    Problem-solving mindset


    A problem-solving mindset is an asset to any organization. It is more than just a skill.
    A person with a problem-solving mindset sees a problem as an opportunity to grow and is motivated to
    find solutions, thereby focussing on growth and achieving positive results.


    Business strategy


    A business strategy is a set of competitive moves and actions that a business uses to attract customers,
    compete successfully, strengthening performance, and achieve organizational goals.

    Communication skills


    Communication skills involve listening, speaking, observing and empathizing.


    Quantitative analysis


    Quantitative analysis (QA) is a technique that uses mathematical
    and statistical modeling, measurement, and research to understand behavior.

    Data anaytics technical skills?

    Python programming

    Python is especially popular among data scientists. … There are countless libraries like NumPy, Pandas, and Matplotlib available in Python to make data cleaning, data analysis, data visualization, and machine learning tasks easier

    R programming

    As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data. R is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling. R is an environment for statistical analysis

    Data visualization

    Data visualization is defined as a graphical representation that contains the information and the data. By using visual elements like charts, graphs, and maps, data visualization techniques provide an accessible way to see and understand trends, outliers, and patterns in data.

    Machine learning

    techniques
    Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

    Artificial intelligence (AI)

    AI-powered systems can analyze data from hundreds of sources and offer predictions about what works and what doesn’t. It can also can deep dive into data analytics about your customers and offer predictions about consumer preferences, product development, and marketing channels.

    SQL

    SQL is the most commonly used data analysis tool for data analysts and data scientists. The majority of the world’s data is stored in databases, and learning SQL will enable you to access and analyze this data with ease.

    Database architecture

    What is data architecture in data analytics?
    Image result for database architecture for data analytics
    Data architecture is a framework for how IT infrastructure supports your data strategy. The goal of any data architecture is to show the company’s infrastructure how data is acquired, transported, stored, queried, and secured. A data architecture is the foundation of any data strategy. … Data standards.
    Role of accountant in Data analysis .

    You will have an impact on how the business makes decisions.

    While the majority of employees feel that their lack of decision-making authority contributes to job discontent, data professionals do not.

    It will be a very gratifying career because you will play a special function inside the organisation and be a crucial component of corporate policies and future strategies.

    Professionals in data analytics are earning more money.

    Prospects estimates that entry-level pay for data analytics specialists is in the region of £24,000 to £25,000. Salaries can increase to between £30,000 and £35,000 after a few years of experience, and high-level consultants and professionals may make £60,000 or more.

    Role of Accountant

    The role of accountants in data analytics is evolving as businesses increasingly rely on data-driven decision-making. Accountants play a crucial role in analyzing financial data, identifying patterns and trends, and providing valuable insights to drive business performance. Here are some key roles and responsibilities of accountants in data analytics:

    Data Analysis and Interpretation:

    Accountants are responsible for analyzing financial data to identify trends, anomalies, and patterns that can provide insights into business performance. They use data analytics tools and techniques to interpret the data and extract meaningful information for decision-making.

    Financial Planning and Forecasting:

    Accountants utilize data analytics to forecast financial outcomes, assess risks, and develop financial plans. They analyze historical financial data, market trends, and other relevant factors to create accurate financial projections and assist in strategic planning.

    Cost Analysis and Optimization:

    Accountants use data analytics to analyze cost structures, identify cost drivers, and optimize resource allocation. By analyzing data, they can identify areas of cost savings, recommend efficiency improvements, and assist in budgeting and cost control efforts.

    Fraud Detection and Risk Management:

    Accountants leverage data analytics to detect fraudulent activities and manage financial risks. They use techniques like anomaly detection, data visualization, and trend analysis to identify unusual patterns that may indicate fraud or financial irregularities.

    Performance Measurement and Key Metrics:

    Accountants analyze data to measure and assess the financial performance of an organization. They develop and monitor key performance indicators (KPIs) to evaluate business performance, assess profitability, and track financial health.

    Compliance and Regulatory Reporting:

    Accountants use data analytics to ensure compliance with financial regulations and reporting requirements. They analyze financial data to identify discrepancies, ensure accuracy, and prepare reports in accordance with applicable accounting standards and regulations.

    Process Improvement and Automation:

    Accountants apply data analytics to improve financial processes and enhance efficiency. They identify opportunities for automation, streamline workflows, and leverage technology to reduce manual effort and increase accuracy in financial reporting and analysis.

    Business Insights and Decision Support:

    Accountants provide valuable insights to support strategic decision-making. By analyzing financial and operational data, they help management understand the financial implications of different courses of action, assess investment opportunities, and evaluate the financial viability of projects.

    Collaboration with Data Analysts and IT Professionals:

    Accountants collaborate with data analysts and IT professionals to leverage their expertise in data analytics. They work together to access, analyze, and interpret relevant data, ensuring data quality, and implementing appropriate data analytics techniques.

    Continuous Learning and Skill Development:

    Accountants need to stay updated with advancements in data analytics tools and techniques. They continuously develop their skills in data analysis, data visualization, and data manipulation to effectively utilize data analytics in their accounting roles.

    Overall, accountants who embrace data analytics are well-positioned to provide valuable insights, improve financial decision-making, and contribute to the overall success of the organization. By leveraging data analytics techniques, accountants can enhance their traditional accounting responsibilities and become strategic partners in driving business performance and growth

    Data analytics for bankers

    Data analytics is essential to the banking sector because it enables bankers to use data for better risk management, operational efficiency, and decision-making. The following are some essential uses of data analytics for bankers:

    1. Customer Segmentation: Data analytics helps bankers analyze customer data to identify segments based on demographics, behavior, and needs. This segmentation enables personalized marketing campaigns, targeted product offerings, and improved customer experiences.

    2. Customer Lifetime Value (CLV) Analysis: By analyzing historical customer data, bankers can determine the CLV for individual customers. This information helps prioritize high-value customers, develop retention strategies, and allocate resources effectively.

    3. Fraud Detection: Data analytics helps identify suspicious patterns and anomalies in transactional data, enabling bankers to detect and prevent fraudulent activities. Advanced analytics techniques can identify potential fraud in real-time, minimizing financial losses.

    4. Credit Risk Assessment: Data analytics enables bankers to assess creditworthiness by analyzing various factors such as credit history, financial ratios, and borrower characteristics. Predictive models and machine learning algorithms help make accurate credit risk assessments for loan applications.

    5. Anti-Money Laundering (AML) Compliance: Banks are required to monitor transactions for potential money laundering activities. Data analytics helps in identifying unusual patterns, transaction flows, and suspicious activities, ensuring compliance with AML regulations.

    6. Regulatory Compliance Reporting: Data analytics automates the process of collecting, analyzing, and reporting data required for regulatory compliance. It ensures accuracy, reduces manual effort, and helps banks meet reporting deadlines effectively.

    7. Risk Management: Banks analyze large volumes of data to assess and manage various risks, such as market risk, credit risk, and operational risk. Data analytics provides insights into risk exposures, portfolio performance, and risk mitigation strategies.

    8. Operational Efficiency: By analyzing operational data, banks can identify bottlenecks, streamline processes, and improve operational efficiency. Data analytics helps optimize resource allocation, reduce costs, and enhance overall productivity.

    9. Fraudulent Claim Detection: Insurance banking operations often involve processing claims. Data analytics helps identify potentially fraudulent claims by analyzing claim patterns, historical data, and other relevant factors. It helps reduce fraudulent payouts and improves claim processing efficiency.

    10. Financial Forecasting and Planning: Data analytics assists bankers in financial forecasting, budgeting, and strategic planning. By analyzing historical financial data, market trends, and economic indicators, banks can make accurate predictions and informed financial decisions.

    In summary, data analytics empowers bankers to make data-driven decisions, manage risks effectively, detect fraud, improve operational efficiency, and enhance customer experiences. By leveraging the power of data, bankers can gain valuable insights that drive profitability, improve compliance, and create a competitive advantage in the dynamic banking industry.

     

    Vista Academy analytics programme

    This course is designed for people with absolutely no background in analytics, statistics or programming. It will introduce you to the wonderful world of analytics in a fun and easy to learn manner.

    Q AND A

    Data analytics equips students with the skills to analyze business data, make informed decisions, and develop strategies for better outcomes in areas like marketing, finance, and operations.

    Data analytics helps students understand consumer behavior, market trends, and competition, aiding in designing effective marketing strategies and identifying growth opportunities.

    By analyzing financial data, students can make accurate forecasts, assess investment opportunities, manage risk, and optimize budgeting and resource allocation.

    Students can use data analytics to optimize inventory levels, predict demand fluctuations, enhance logistics efficiency, and reduce operational costs.

     Data-driven insights help students identify strengths, weaknesses, opportunities, and threats in the market, leading to well-informed strategic decisions.

    Students can analyze customer data to personalize offerings, improve customer satisfaction, and predict customer needs, leading to stronger brand loyalty

    They acquire skills in data interpretation, statistical analysis, data visualization, and using analytical tools, making them proficient in translating data into actionable insights.

    Certainly. For instance, Netflix uses data analytics to recommend personalized content, and Amazon employs it for inventory management and targeted marketing.

    Yes, many employers seek candidates with data analytics skills, as businesses increasingly rely on data-driven decision-making to stay competitive

    They can take VISTA AC ADEMY courses, workshops, or pursue degrees that offer data analytics modules. Hands-on experience with tools like Excel, Python, or specialized software is beneficial.

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    Thanks, I’m not interested