Start your journey in Data Science
    7Jun, 2022
    Tips to Start your journey As Data Scientist from Scratch

    Essential Advice for People Starting a Data Science Career

    It might be intimidating to learn data science. This is especially true when you are just getting started on your trip. Which tool should you learn first: R or Python? What tactics should you concentrate on? How many statistics do you need to know? Is it necessary for me to learn to code? These are just a few of the numerous questions you’ll have to answer along the way.

    That is why I decided to write this tutorial to assist people who are new to analytics or data science. The goal was to produce a short, easy tutorial that will help you get started understanding data science. This tutorial will provide a foundation for learning data science during this challenging and daunting time.

    It might be intimidating to learn data science. This procedure, in particular, does not need sophisticated mathematics, deep learning knowledge, or many of the other abilities listed above. However, it does need familiarity with a programming language as well as the ability to interact with data in that language. And, while mathematical fluency is required to excel at data science, a fundamental comprehension of mathematics is all that is required to get started.

    The additional specific talents described above may assist you address data science difficulties in the future. To start a career in data science, you don’t need to master all of those talents. You may start right now, and I’m here to assist you!

    Start a Data Science career

    Choose the appropriate role.

    In the data science sector, there are many different positions to choose from. A data visualisation specialist, a machine learning expert, a data scientist, a data engineer, and other professions are just a handful of the numerous possibilities. Getting into one position may be simpler than another, depending on your history and previous experience. For example, if you’re a software developer, switching to data engineering shouldn’t be too tough. So, until and until you’re clear on what you want to be, you’ll be unsure of what path to pursue and what abilities to develop.

    What should you do if you’re unsure about the distinctions or what you should become? The following are a few items I would recommend:

    Find out what each of the positions requires by speaking with professionals in the business.
    Take advantage of people’s mentoring by requesting a modest amount of time from them and asking pertinent questions. No one, I’m sure, would hesitate to assist someone in need!
    Determine what you want and are excellent at, then select a career that fits your field of study.
    Here’s a terrific resource to help you distinguish between business analyst, data scientist, and even data engineer —

    Take on a Course and Finish It

    Now that you’ve settled on a position, the next obvious step is to devote time and effort to learning the role. This entails more than simply going through the job’s needs. Because there is such a high need for data scientists, there are thousands of courses and studies available to help you learn whatever you desire. Finding information to learn from isn’t difficult, but learning it might be if you don’t put out the necessary effort.

    When you enrol in a course, make an effort to complete it. Follow up with the curriculum, assignments, and all of the class conversations.

    Now you have to diligently follow all the course material provided in the course. This also means the assignments in the course, which are as important as going through the videos. Only doing a course end to end will give you a clearer picture of the field.

    Participate in a peer group.


    Now that you’ve decided on whatever position you want to pursue and are preparing for it, the next step is to find a peer group to join. What is the significance of this? This is due to the fact that having a peer group keeps you motivated. Taking up a new field might be intimidating when you’re on your alone, but with friends at your side, the effort looks a little less frightening.

    Having a group of individuals you can physically engage with is the best method to be in a peer group.

    This is most likely the most often requested question by newcomers. The most easy option is to begin your data science adventure with any of the common tools/languages available.

    This is most likely the most often requested question by newcomers. The most easy option is to begin your data science adventure with any of the common tools/languages available. After all, technologies are only instruments for putting ideas into action; comprehending the notion is far more crucial.

     

    However, the issue remains as to which is the best alternative to begin with. At Vista Academy we will guide you to start a program in Data Science and step by step training  

    Concentrate on real-world applications rather than theory.

    Concentrate on real-world applications rather than theory.
    You should concentrate on the practical applications of what you’re learning when taking courses and training. This will assist you in not only comprehending the concept but also in gaining a better understanding of how it might be utilised in practise.

    Following are a few pointers to keep in mind when taking a course:

    • To fully comprehend the applications, make sure you complete all of the exercises and assignments.
    • Apply what you’ve learned to a few open data sets. Comprehend the assumptions, what it accomplishes, and how to interpret the outcomes, even if you don’t understand the math behind a strategy at first.
    • You may always go back and learn more at a later time.
    • Take a peek at what people who have worked in the field have come up with.
    • They’d be able to target you using the best method in a shorter amount of time.

    Improve your communication abilities.

    Improve your communication abilities.
    In data science jobs, people don’t frequently equate communication abilities with rejection. They hope to ace the interview if they are technically proficient. This is a fabrication. Have you ever been turned down at an interview because the interviewer said thank you after hearing your introduction?

    Once you’ve tried this task, have a buddy with strong communication skills listen to your introduction and give you honest comments. He’ll most certainly show you the mirror!

    When working in the field, communication skills are even more crucial. You should be able to communicate effectively in order to discuss your thoughts with a colleague or to make your point in a meeting.

    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 libraries7

    SQL and basic database expertise are required.

    Tables don’t just emerge in the form of data. Beginners usually begin their machine learning adventure with data in the form of a CSV or excel file. But there’s something that’s lacking! It’s a SQL query. For a data science practitioner, it is the most essential ability.

    Because firms are still sorting out their data science requirements, having knowledge of data storage strategies as well as the fundamentals of big data will make you far more appealing than someone with hi-fi terms on their CV.

    These companies are looking for SQL experts that can assist them with day-to-day activities.

    Data Visualization

    Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from. The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets.

    Data visualization tools

    • Tableau. Tableau is a data visualization tool that can be used to create interactive graphs, charts, and maps.
    • QlikView.
    • Microsoft Power BI.
    • Datawrapper.

    Machine Learning

    For a profession in data science, predictive modelling employing machine learning techniques, tools, and algorithms is essential. Tree models, regression methods, clustering, classification strategies, and anomaly detection are all ideas you should be well-versed on. Without writing any Python code, you can work with datasets using a variety of services available online.

    Making business judgments using data visualisation and its patterns is an excellent use of machine learning. To create charts, graphs, histograms, and other graphics used in client-side meetings, you can enlist the aid of Graphics User Interface (GUI) tools.

    Staying on top of your resume game

    Let’s try to figure out a riddle: What is the first thing a recruiter notices about you that might be your last? It’s your resume, after all! These are the final hurdles you must overcome in order to land the most sought job!

    Make sure you incorporate these tips into your next resume —

    Prioritize skills based on the employment role available.
    To demonstrate your abilities, mention data science initiatives.
    Remember to provide a link to your GitHub profile.
    Certifications are less significant than skills.
    Not once in a blue moon, but on a regular basis, update your talents and projects.
    Make sure your typefaces and structure are consistent throughout your resume.

    The Top 10 Internship Recruiting Websites
    18May, 2022
    The Top 10 Internship Recruiting Websites

    Internships are sought by college students in order to gain real-world experience in the dreaded “office environment.” However, job may become a strain. The finest internship websites are the most important issue for college students.

    The internship is being diluted as more universities push out qualified students. Companies begin by conducting selecting procedures or obtaining information through recognised links. Having so many interns has irritated some businesses. Some have even halted their internship programmes.

    Numerous experts, on the other hand, regarded this as a market problem that needed to be addressed. That is exactly what they did. The majority of them use the internet to construct India’s greatest internship websites.

    Why should you undertake an internship?


    This sort of inquiry frightens college students preparing for internships. I have an explanation for those. The purpose of internships is to gain experience. To learn more than simply how the workplace works. But also to get experience with work-life balance, and so on. It is a life coach for adults. Every college student should aim to do as many internships as possible. Internships are also an excellent method for students who aren’t sure what they want to do with their lives to test out several sectors.

    List of Best Internship Websites in India

    This article is just a list of the top 10 internship websites in India. It enables students to apply for jobs in a variety of industries. But, before I go through these, I’d want to bring out something crucial.

    LinkedIn

    Part-time/Full-time Internships on LinkedIn

    This is a professional networking website that was created as a business alternative to Facebook. It also serves as one of India’s finest internship websites.

    As a result, college students should come here for the internships they provide and build a solid profile that they can share with professional contacts. LinkedIn employs this kind of connecting to let you add friends to your profile. You may even receive faster information regarding internships if you are well-connected on the internet.

    Finding internships is a simple procedure. Search for internships on the home page. Other keywords, such as’marketing internships’ and ‘content writing internships,’ might be used to narrow down your search. 

    Internshala

    Internships can be part-time or full-time.

    “How to find internships in India?” is a common question here.

    It is one of India’s major internship sites. It has expanded not only to give internships but also to teach students in specialised skills.

    Students must create accounts by completing a registration form. This form collects information about your schooling, prior work experiences, awards, and so on, and generates a CV file for you.

    After choose which firm to apply to, you must answer 2-3 questions. Then you will be contacted for an interview.

    There is a wide range of companies here, from MNCs to tiny firms, and from digital marketing to content authoring. It provides students with a diverse selection of sectors to choose from.

    StuMagz

    Part-time/Full-time Internships at 

    This’student discovery platform’ provides many services to students and allows them to collaborate and share ideas.
    One of the most sought-after services offered by this company can be found on its website under the Opportunities category. This gives students the chance to look through a variety of internship possibilities.

    Certain internship possibilities are also directly communicated to students. Students might make this decision based on the results of their search. Following that, all students are asked to apply to the firm using the online CV.

    The main thing is to validate your abilities. This procedure may be initiated while enrolling your account. This increases their credibility and yours.

    Twenty19

    Internships can be part-time or full-time.

    This platform facilitates the interaction between businesses and college students. It is among the

    Furthermore, internship websites in India provide students with a more comfortable and friendlier environment in which to communicate with the firm.

    The utilisation of a personal page fosters a welcoming atmosphere for all students. This is similar to a profile page. It stores your contact information and your previous job experience. However, this allows the firm to view a more easily accessible description of the prospect.

    However, students may post employment possibilities on the internet, similar to a festival or anything. And can solicit review applications from the greater student body.

    LetsIntern

    Internships can be part-time or full-time.

    This internship website in India allows college students to locate internships in their professions.

    And it is this emphasis on individuality that distinguishes it. You may start setting up your account here by selecting internships by area or industry. Following that, the personalization qualities continue to accumulate.

    It distinguishes itself from competitors by having a broad reach inside small enterprises. You’re nothing more than a duck in the pond.

    As a result of the breadth of the start-up industry covered, college students might investigate such internship websites in India that facilitate the process of seeking internships.

    FreshersWorld

    Internships can be part-time or full-time.

    It is primarily an employment platform, but it is also one of the greatest internship sites in India.

    The contact with the government sector is what makes it special. FresherWorld claims to be the most popular website for applying for government employment. However, this gives students the opportunity to look for internships in both the commercial and governmental sectors.

    This internship website in India collects data from CVs submitted by students. And offers it out to the many firms with which it collaborates. FreshersWorld, on the other hand, supplies the data required for the selection process.

     

    HelloIntern

    Internships can be part-time or full-time.

    It is yet another of the top internship sites in India that students have used to find the greatest internships that suit them the most.

    It functions primarily as an alternative to the other internship websites in India, such as the ones mentioned above. It also functions as a conduit, linking student brilliance with the reputation of large corporations.

    The distinction is its potential to provide global internship information, i.e. you can receive offers from firms. Aside from that, they have a mentor programme that will assist you in selecting the right internship for you.

    Youth4Work

    Internships can be part-time or full-time.

    Youth4Work describes itself as a “talent marketplace.” With 2.9 million members, Youth4Work is quickly becoming one of India’s leading internship websites.

    Youth4Work operates differently than its rivals. Furthermore, Youth4Work does not provide students with a list of internship options. Instead, it use a testing procedure to categorise pupils into several kinds.

    This test might assist you in determining the best job path for your talents. Following that, users are given internships, and in some cases, career possibilities, in their skill area. So, in a way, Youth4Work is appropriately referred to as a marketplace for talent since the website recognises and shows the individual skill in each user.

    InternWorld

    Type of Internships: Part-time/Full-time

     

     

    It uses its pan-India presence to find internships that best suit your needs.

     

     

    It offers a robust framework for college students to categorise and assemble their past experiences after creating an online profile with the information. The students can explore the thousands of internship listings that range from different industries.

    The best thing is the vastness of its options to college students.

    Work from home internships offers great relief to college students who find it hard to manage their classes and internships together. Most students seek internships in their summer breaks. Therefore, students can do work from home internships even while they are busy with morning classes. Work from home opportunities offers students the flexibility to design their day and allow time for work that suits them

     Internships.com

    It’s mainly the eHarmony of berth sites, allowing you to seek for internships by on major, job class, city, and firm. It also provides alternative resources such as suggestions and tales from other interns. You get the most out of this website, please sure to register an Associate in Nursing account. You’ll utilise their regular “Internship Predictor” to find internships that fit your temperament and references.

     

    Why a Career in Big Data Is the Right Choice for You?
    19Jan, 2022
    Reasons to Make a Change Of career to Big Data Now

    Why a Career in Big Data Is the Right Choice for You?

    Data has caught the interest of a number of young people, who are understandably excited about the promising employment opportunities that this new subject offers. Gaining a foundation in this dynamic field will undoubtedly advance your career. Let us begin by putting Big Data into context.

    Are you in a job market where Big Data skills are valued more than others? Are you unsure whether or not to pursue a career in Big Data? What career alternatives will be available to me after Big Data? Just spend some time reading this article to learn the answers to all of these questions as well as the benefits of choosing Big Data as a career.

    Big Data is a must-have in your career for a range of reasons.

    Increased Job Opportunities for Big Data professionals

    With the advancement of technology, Big Data will undoubtedly become a buzzword and a growing demand for businesses in the future years. “Data is useless without the skill to analyse it,

    Big Data scientists are in high demand in businesses all over the world nowadays. To keep ahead of the competition, businesses are relying heavily on Big Data. Professionals with Big Data experience and talents are in high demand. According to IBM, the number of data professionals employed in the United States will rise to 2,720,000 by 2020.


    SALARY

    If you feel your career is stagnant and your salary is not increasing, you should seriously consider changing jobs.

    A career is all about development and possibilities. If your salary does not increase, it could signify one of two things.

    Your growth has halted, and you are not upgrading yourself, or the company has lost business or is reluctant to share profits with you.

    With Big Data, you have the opportunity for a great career that will take you to new heights. A fresher’s average pay in Big Data is 8.5 lakhs. This status can be attained by mastering only two things: Hadoop and Spark.

    Not so easy, not so difficult. However, as Steve Jobs has stated.

    Massive adoption of Big Data

    Enterprise adoption of big data has risen from 17% in 2015 to 59 percent in 2018, with a 36 percent compound annual growth rate (CAGR). Big Data is quickly spreading its wings across a variety of industries, including sales, marketing, R&D, logistics, and strategic management.

    According to Intel’s ‘Peer Research – Big Data Analytics’ survey, Big Data is one of the top priorities of the firms that participated in the study because they believe it improves their organizations’ performance. According to the survey, 45 percent of respondents believe that Big Data would provide additional business benefits, allowing them to rank at the top of the Big Data market.

    Easy to Start

    Simple to Begin
    The involvement of programming frameworks such as Java, C, Python, or Scala is defined as “Data Analytics,” but the ability to think of ways to break through to a solution is also crucial.

    Having a natural motivation to solve problems, regardless of your current career, will make it easier and more enjoyable for you to study in-demand languages and specialize over time.

    There are numerous certification courses and materials available online at Vista Academy  for both new and experienced applicants who want to expand their Big Data knowledge.

    A Demanded Profession

    A Demanded Profession
    75 percent of Internet of Things (IoT) providers consider Big Data Scientist to be the most desired competence, and over 68 percent of them are having difficulty finding personnel with suitable experience. In the coming years, the range of professional options is expected to expand.

    Various job titles and responsibilities

    Various job titles and responsibilities to choose from
    Depending on the abilities they have acquired thus far, Big Data experts might choose from a variety of job titles. Big Data job seekers have a plethora of possibilities to choose from, allowing them to tailor their career paths to their specific interests. The following are some of the job roles that Big Data specialists can fill:

    Data Engineer

    Business Analyst,

    Visualization Specialist

    Machine Learning Expert

    Analytics Consultant

    Solution Architect

    Big Data Solution Architect

    Big Data Analyst

    New Skill Opportunities

    Since data assurance, finance, sales, marketing, and Business Intelligence (BI) are the most common work areas for Data Analyst specialists, you’ll be able to put your skills to work in a variety of sectors.

    Takeaway: Studying Data Mining, Data Infrastructure, Data Visualization, and Decisions Analysis abilities will allow you to broaden your service offerings while also improving your Data Analytics understanding.

    Due to digitization, there is an opportunity for growth.

    Due to digitization, there is an opportunity for growth.
    Many companies understand the value of Big Data research and have integrated extensive digital operations to improve customer satisfaction. And, because Big Data science enables businesses to fuel their skills of the students, there is a wide range of opportunities for novices and professionals with the necessary skill sets.

    Lesson to be learned: A job in Big Data Science does not confine you to a specific field. You can quickly transfer and grow with industries that best suit your interests if you have an appropriate set of technological abilities.

    Engage as a Stakeholder

    Engage as a Stakeholder
    You can become an owner in firms that give more than a good wage if you have the ability to offer creative solutions using the power of Data Visualization.

    Key message: As a Data science professional, your compensation is really not set in stone; you can earn additional perks or a high commission by assisting companies in making better decisions.

    Opportunities in a variety of industries and domains

    Opportunities in a variety of industries and domains
    Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP, and Dell, with their well-known trademarks, have subsidised more than $20 billion in software enterprises specialising in Data Management Analytics, increasing the demand for Information Management specialists across a wide range of enterprises and domain types.

    Key Decision-Making Power

    Effective Decision
    Any firm can benefit from Big Data Analytics Science since it allows them to make more informed decisions and gain a competitive advantage. A career move towards Big Data increases your chances of becoming a key decision maker for a company.

    Lesson to take away: As more businesses turn to Big Data experts, you’ll collaborate with the organization’s key person to streamline decision-making layers from top to bottom and coordinate with local levels to act on findings.

    Improve the relationship between the retailer and the customer


    Big Data Analytics, particularly for B2B organisations, provides superior insights into their customers and minimises the danger of guesswork. You’ll be able to create a more fluid and user-driven customer experience with the firm thanks to the evidence-based segmentation and reporting framework.

    Lesson to take away: As a representative of a Big Data Analytics science team with access to customers’ acquisition channels, you may assist brands in implementing targeted marketing strategies that will increase customer loyalty.

    Vista Academy Training and Placement Guide
    14 top technologies to learn in 2022
    11Jan, 2022
    14 top technologies to learn in 2022

    14 growing industries of the future.

    Technology is evolving every day, and it is playing an increasingly essential part in the expansion of a variety of industries, including logistics, manufacturing, top future technologies, IT, and the retail sector. Various technologies have advanced one step further with improved inventions to assist humans in their day-to-day tasks. Emerging technologies provide us with the opportunity to progress and better our currently constrained human situation.

    Internet of things

    The Internet of Things (IoT) is a network of physical devices or items referred to as “things.” Sensors, electronics, software, and other components are embedded in these devices, allowing them to collect data and exchange it with other internet-connected devices and systems without the need for human contact.
    Internet of things applications for smart cities include smart surveillance, automated transportation, improved energy management systems, water distribution, urban security, and environmental monitoring.

    Artificial intelligence (AI)

    Artificial intelligence (AI) is a broad field of computer science that focuses on creating intelligent machines that can accomplish activities that would normally need human intelligence.
    Artificial Intelligence (AI) is a discipline of computer science that focuses on the creation of intelligent machines that think and function in the same way that people do. Speech recognition, problem-solving, learning, and planning are just a few examples.

    Cyber Security

    Cyber security refers to the collection of methodologies, technologies, and processes used to safeguard the confidentiality, integrity, and availability of computer systems, networks, and data from cyber-attacks or unauthorised access.

    Type of Cyber security

    • Cloud Security. Cloud-based data storage has become a popular option over the last decade due to its enhanced privacy. .
    • Network Security. Guard your internal network against outside threats with increased network security.
    • Application Security.Protect the data you store on the applications you use to run your business. Since applications are more accessible over various networks, they’re especially open to cyberattacks.You can protect applications with cybersecrity antivirus programs, firewalls, and encryption services.

    Drones

    A drone is essentially a flying robot that may be commanded remotely or fly autonomously utilising software-controlled flight plans in its embedded systems, which work in concert with onboard sensors and a global positioning system (GPS).

    Drones, also known as unmanned aerial vehicles (UAVs), miniature pilotless aircraft, or flying mini robots, are becoming increasingly popular. Drones are still in their infancy in terms of widespread adoption and use, but they have already broken down solid traditional barriers in industries that previously looked immune to similar technological advancements.

    rOBOTICS

    Robotics is the study, design, and application of machines (robots) to do tasks that were previously performed by humans. Robots are commonly utilised in areas such as vehicle manufacturing to perform simple repetitive jobs, as well as in businesses where workers must work in dangerous situations.

    Artificial muscles, elastic electric wires, artificial skin, artificial brain, rapid moving robots, artificial noses, and tactile finger devices are examples of these technologies that have the potential to bring artificial gadgets closer to live organisms, including humans.

    Virtual Reality

    The use of computer technology to create a simulated environment that can be explored in 360 degrees is known as virtual reality, or VR. In contrast to traditional interfaces, virtual reality immerses the user in the virtual environment.

    Virtual Reality (VR) is a computer-generated environment with realistic-looking images and objects that gives the user the feeling of being completely immersed in their surroundings. This environment is viewed through the use of a Virtual Reality headset or helmet.

    nANO TECHNOLOGY

    Nanotechnology is the science of manipulating matter at a microscopic level. Controlling stuff at the atomic level is what it’s all about. Nanotechnology is defined as structures or substances with a diameter of one hundred nanometers or less. Metals, polymers, ceramics, and biomaterials all utilize nanotechnology products.

    Many technology and industry sectors are benefiting from nanotechnology, including information technology, public safety, healthcare, infrastructure, energy, food security, and environmental science, to name a few.

    Genomics

    The science of genomics is the study of an organism’s entire genetic makeup (the genome). To sequence, assemble, and analyze the structure and function of genomes, it employs recombinant DNA, DNA sequencing technologies, and bioinformatics.

    Sharing Econnomy

    The sharing economy, also known as collaborative consumption or peer-to-peer-based sharing, is a concept that emphasizes people’s ability (and perhaps inclination) for renting or borrowing products rather than buying and owning them.

    e LEARNING

    A learning system based on formalised teaching but with the help of electronic resources is known as E-learning. While teaching can be based in or out of the classrooms, the use of computers and the Internet forms the major component of E-learning.

    bIG DATA

    Big data refers to massive, difficult-to-manage data volumes – both structured and unstructured – that populate enterprises on a daily basis. Big data may be analysed for insights that help people make better decisions and feel more confident about making key business decisions.

    3D printing

    3D printing, also known as additive manufacturing, is a method for layering a three-dimensional object using a computer-generated design. 3D printing is an additive technique in which layers of material are built up to form a three-dimensional object. As a result, there is less wastage from 3D printing.

    Blockchain

    Blockchain is a method of storing data in such a way that it is difficult or impossible to alter, hack, or cheat it.

    A blockchain is a digital ledger of transactions that is duplicated and distributed across the blockchain’s complete network of computer systems. Each block in the chain contains a number of transactions, and each time a new transaction takes place on the blockchain, a record of that transaction is added to the ledger of each participant. Distributed Ledger Technology is a decentralised database that is administered by various people (DLT).

    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.

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    Artificial intelligence in Hindi
    16Aug, 2021
    (Artificial Intelligence )आर्टिफिशियल इंटेलिजेंस क्या हैऔर यह कैसे काम करता है ?

    What is artificial Intellence

    आर्टिफिशियल इंटेलिजेंस क्या है?

    आज की दुनिया में, तकनीक बहुत तेजी से बढ़ रही है, और हम दिन-ब-दिन विभिन्न नई तकनीकों के संपर्क में आ रहे हैं।

    यहाँ, कंप्यूटर विज्ञान की उभरती हुई तकनीकों में से एक आर्टिफिशियल इंटेलिजेंस है जो बुद्धिमान मशीनें बनाकर दुनिया में एक नई क्रांति लाने के लिए तैयार है। आर्टिफिशियल इंटेलिजेंस अब हमारे चारों ओर है। यह वर्तमान में विभिन्न उपक्षेत्रों के साथ काम कर रहा है, सामान्य से लेकर विशिष्ट तक, जैसे कि सेल्फ-ड्राइविंग कार, शतरंज खेलना, थेओरम को साबित करना, संगीत बजाना, पेंटिंग करना आदि।

    आर्टिफिशियल इंटेलिजेंस दो शब्दों आर्टिफिशियल और इंटेलिजेंस से बना है, जहां आर्टिफिशियल “मानव निर्मित” को परिभाषित करता है और इंटेलिजेंस “सोचने की शक्ति” को परिभाषित करता है, इसलिए एआई का अर्थ है “एक मानव निर्मित सोच शक्ति”
    आर्टिफिशियल इंटेलिजेंस तब मौजूद होता है जब एक मशीन में सीखने, तर्क करने और समस्याओं को हल करने जैसे मानव आधारित कौशल हो सकते हैं

    आर्टिफिशियल इंटेलिजेंस के साथ आपको कुछ काम करने के लिए किसी मशीन को प्रीप्रोग्राम करने की जरूरत नहीं है, इसके बावजूद आप प्रोग्राम किए गए एल्गोरिदम के साथ एक मशीन बना सकते हैं जो खुद की बुद्धि के साथ काम कर सकती है, और वह है एआई का कमाल।

    ऐसा माना जाता है कि AI कोई नई तकनीक नहीं है, और कुछ लोगों का कहना है कि ग्रीक मिथक के अनुसार, शुरुआती दिनों में यांत्रिक पुरुष थे जो मनुष्यों की तरह काम और व्यवहार कर सकते थे।हिंदू महाकाव्य भी एआई, रोबोट से भरे हुए हैं |

    Why artificial Intelligence ?

    आर्टिफिशियल इंटेलिजेंस क्यों?

     

    आर्टिफिशियल इंटेलिजेंस के बारे में जानने से पहले हमें यह जान लेना चाहिए कि AI का क्या महत्व है और हमें इसे क्यों सीखना चाहिए। AI के बारे में जानने के कुछ मुख्य कारण निम्नलिखित हैं:

    • AI की मदद से, आप ऐसे सॉफ़्टवेयर या डिवाइस बना सकते हैं जो वास्तविक दुनिया की समस्याओं को बहुत आसानी से और सटीकता के साथ हल कर सकते हैं जैसे कि स्वास्थ्य संबंधी समस्याएं, मार्केटिंग, ट्रैफ़िक समस्याएं आदि।
    • एआई की मदद से आप अपने पर्सनल वर्चुअल असिस्टेंट जैसे कॉर्टाना, गूगल असिस्टेंट, सिरी आदि बना सकते हैं।
      एआई की मदद से आप ऐसे रोबोट बना सकते हैं जो ऐसे माहौल में काम कर सकें जहां इंसानों का अस्तित्व खतरे में पड़ सकता है।
    • AI अन्य नई तकनीकों, नए उपकरणों और नए अवसरों के लिए मार्ग खोलता है।

    Objective of artificial intelligence

    आर्टिफिशियल इंटेलिजेंस के उद्देश्य

    आर्टिफिशियल इंटेलिजेंस के मुख्य उद्देश्य निम्नलिखित हैं:

    • मानव मस्तिष्क की प्रतिलिपि बनाएँ
    • ज्ञान-गहन कार्यों को हल करें
    • धारणा और क्रिया का बुद्धिमान संबंध बनाएं
      एक ऐसी मशीन का निर्माण करना जो मानवीय बुद्धि की आवश्यकता वाले कार्यों को कर सके
    • थेओरम सिद्ध करना
    • शतरंज खेलना
    • कुछ सर्जिकल ऑपरेशन की योजना बनाएं
    • ट्रैफिक में कार चलाना
    • कुछ ऐसी प्रणाली बनाना जो बुद्धिमान व्यवहार प्रदर्शित कर सके, स्वयं नई चीजें सीख सके, प्रदर्शित कर सके, समझा सके और अपने उपयोगकर्ता को सलाह दे सके।

    How artificial intelligence work

    आर्टिफिशियल इंटेलिजेंस (एआई) कैसे काम करता है?

    मशीन लर्निंग: Machine Learning

    ML एक मशीन को सिखाता है कि पिछले अनुभव के आधार पर निष्कर्ष और निर्णय कैसे लें। यह पैटर्न की पहचान करता है, मानव अनुभव को शामिल किए बिना संभावित निष्कर्ष तक पहुंचने के लिए इन डेटा बिंदुओं के अर्थ का अनुमान लगाने के लिए पिछले डेटा का विश्लेषण करता है। डेटा का मूल्यांकन करके निष्कर्ष पर पहुंचने के लिए यह अपनेआप काम करता है और मानव समय बचाता है और उन्हें बेहतर निर्णय लेने में मदद करता है।

    डीप लर्निंग: Deep Learning 

    यह एक मशीन को सिखाता है कि कैसे परिणामों को विभाजित करने, तर्क करने और भविष्यवाणी करने के लिए परतों के माध्यम से इनपुट को तैयार करता है।

    तंत्रिका नेटवर्क: Neural Networks

    वे एल्गोरिदम की एक श्रृंखला है जो विभिन्न बहुत से वेरिएबल्स के बीच संबंधों को पकड़ती है और डेटा को मानव मस्तिष्क के रूप में पेश करती है।

    प्राकृतिक भाषा प्रसंस्करण सी:

    Natural Language Processing

    एनएलपी एक मशीन द्वारा किसी भाषा को पढ़ने, समझने, उसकी व्याख्या करने का विज्ञान है। एक बार जब एक मशीन समझ जाती है कि उपयोगकर्ता क्या संवाद करना चाहता है, तो वह उसी के अनुसार प्रतिक्रिया करता है।

    कंप्यूटर विज़न: computer Vision 

    कंप्यूटर विज़न एल्गोरिदम एक छवि को तोड़कर और वस्तुओं के विभिन्न भागों का अध्ययन करके एक छवि को समझने की कोशिश करता है। यह मशीन को छवियों के एक सेट से वर्गीकृत करने और सीखने में मदद करता है, ताकि पिछली टिप्पणियों के आधार पर बेहतर आउटपुट निर्णय लिया जा सके।

    संज्ञानात्मक कंप्यूटिंग: Cognitive Computing

    संज्ञानात्मक कंप्यूटिंग एल्गोरिदम पाठ/भाषण/छवियों/वस्तुओं का विश्ले

    What does artificial intelligence contain

    आर्टिफिशियल इंटेलिजेंस में क्या शामिल है?

    मशीन या सॉफ्टवेयर के लिए उपरोक्त कारकों को प्राप्त करने के लिए आर्टिफिशियल इंटेलिजेंस को निम्नलिखित की आवश्यकता होती है:

    • गणित
    • जीवविज्ञान
    • मनोविज्ञान
    • समाज शास्त्र
    • कंप्यूटर विज्ञान
    • न्यूरॉन्स अध्ययन
    • सांख्यिकी

     

    • Mathematics
    • Psychology
    • Sociology
    • Computer science
    • Neurons Studies
    • Statistics

    benefits of artificial intelligence

    आर्टिफिशियल इंटेलिजेंस के लाभ

    आर्टिफिशियल इंटेलिजेंस के कुछ मुख्य लाभ निम्नलिखित हैं:

    कम गलतियों के साथ उच्च  सटीकता:

    एआई मशीन या सिस्टम कम गलतियों और उच्च सटीकता के लिए होते हैं क्योंकि यह पूर्व-अनुभव या जानकारी के अनुसार निर्णय लेता है।

    हाई-स्पीड:

    एआई सिस्टम बहुत तेज गति और तेजी से निर्णय लेने वाले हो सकते हैं, क्योंकि एआई सिस्टम शतरंज के खेल में एक शतरंज चैंपियन को हरा सकते हैं।

    उच्च विश्वसनीयता:

    एआई मशीनें अत्यधिक विश्वसनीय हैं और उच्च सटीकता के साथ एक ही काम को कई बार कर सकती हैं।

    जोखिम भरे क्षेत्रों के लिए उपयोगी:

    एआई मशीनें बम को डिफ्यूज करने, समुद्र तल की खोज करने जैसी स्थितियों में मददगार हो सकती हैं, जहां मानव को काम पर रखना जोखिम भरा हो सकता है।

    डिजिटल सहायक:

    एआई उपयोगकर्ताओं को डिजिटल सहायता प्रदान करने के लिए बहुत उपयोगी हो सकता है जैसे कि एआई तकनीक वर्तमान में विभिन्न ई-कॉमर्स वेबसाइटों द्वारा ग्राहकों की आवश्यकता के अनुसार उत्पादों को दिखाने के लिए उपयोग की जाती है।

    सार्वजनिक उपयोगिता के रूप में उपयोगी:

    एआई सार्वजनिक उपयोगिताओं के लिए बहुत उपयोगी हो सकता है जैसे कि सेल्फ-ड्राइविंग कार जो हमारी यात्रा को सुरक्षित और परेशानी मुक्त बना सकती है, सुरक्षा उद्देश्य के लिए चेहरे की पहचान, मानव-भाषा में मानव के साथ संवाद करने के लिए प्राकृतिक भाषा प्रसंस्करण , आदि।

    Disadvantages of Artificial Intelligence

    आर्टिफिशियल इंटेलिजेंस के नुकसान

    हर तकनीक के कुछ नुकसान होते हैं, और वही आर्टिफिशियल इंटेलिजेंस के लिए जाता है। अभी भी इतनी फायदेमंद तकनीक होने के कारण, इसके कुछ नुकसान हैं जिन्हें हमें AI सिस्टम बनाते समय अपने दिमाग में रखना होगा। एआई के नुकसान निम्नलिखित हैं:

    उच्च लागत:

    एआई की हार्डवेयर और सॉफ्टवेयर की आवश्यकता बहुत महंगी है क्योंकि इसे वर्तमान विश्व आवश्यकताओं को पूरा करने के लिए बहुत सारे रखरखाव की आवश्यकता होती है।

    लीक से हटकर नहीं सोच सकते:

    यहां तक ​​कि हम एआई के साथ स्मार्ट मशीनें बना रहे हैं, लेकिन फिर भी वे लीक से हटकर काम नहीं कर सकते, क्योंकि रोबोट केवल वही काम करेगा जिसके लिए उन्हें प्रशिक्षित किया गया है, या प्रोग्राम किया गया है।

    भावनाएँ और भावनाएँ नहीं:

    AI मशीनें एक उत्कृष्ट प्रदर्शनकर्ता हो सकती हैं, लेकिन फिर भी इसमें भावना नहीं होती है इसलिए यह मानव के साथ किसी भी प्रकार का भावनात्मक लगाव नहीं बना सकती है, और कभी-कभी उपयोगकर्ताओं के लिए हानिकारक हो सकती है यदि उचित देखभाल न की जाए।

    मशीनों पर निर्भरता बढ़ाएँ:

    प्रौद्योगिकी की वृद्धि के साथ, लोग उपकरणों पर अधिक निर्भर होते जा रहे हैं और इसलिए वे अपनी मानसिक क्षमताओं को खो रहे हैं।

    कोई रचनात्मकता नहीं:

    चूंकि मनुष्य इतने रचनात्मक हैं और कुछ नए विचारों की कल्पना कर सकते हैं लेकिन फिर भी एआई मशीनें मानव बुद्धि की इस शक्ति को हरा नहीं सकती हैं और रचनात्मक और कल्पनाशील नहीं हो सकती हैं।

    एनालिटिक्स में AI: टॉप यूज केस और टूल्स

    एआई प्रौद्योगिकियों का एक संग्रह है जो डेटा के बड़े सेट से अंतर्दृष्टि और पैटर्न निकालने में उत्कृष्टता प्राप्त करता है। एआई उन अंतर्दृष्टि और पैटर्न का उपयोग भविष्यवाणियां करने के लिए कर सकता है कि परिणाम क्या हैं। यह समय के साथ अपनी भविष्यवाणियों को सुधारना भी सीख सकता है।

    यह एआई को निर्णय लेने के लिए एनालिटिक्स डेटा का उपयोग करने वाले किसी भी व्यक्ति के लिए एकदम सही बनाता है। हम Google Analytics, ऑटोमेशन प्लेटफॉर्म, बिजनेस इंटेलिजेंस सिस्टम, कंटेंट मैनेजमेंट सिस्टम और CRM जैसे सिस्टम का उपयोग करके डेटा विश्लेषण की बात कर रहे हैं।

    एनालिटिक्स में एआई के साथ, आप अपने पास पहले से मौजूद डेटा से अधिक मूल्य प्राप्त कर सकते हैं, उस डेटा को एकीकृत कर सकते हैं, और अपने डेटा के आधार पर तेजी से मूल्यवान भविष्यवाणियां कर सकते हैं।

    analytics एनालिटिक्स उपयोग के मामलों में एआई

    नई खोजें
    एआई बड़े डेटासेट में आत्मचिंतन और पैटर्न खोजने में उत्कृष्टता प्राप्त करता है जिसे मनुष्य अभी नहीं देख सकते हैं। यह बड़े पैमाने पर और गति से भी करता है।

    आज, AI मौजूद है जो आपके वेबसाइट डेटा एनालिटिक्स के बारे में आपके द्वारा पूछे गए सवालों के जवाब देगा। (सोचें “किस चैनल की रूपांतरण दर सबसे अधिक थी?”)

    एआई एनालिटिक्स टूल आपके एनालिटिक्स में देखे जाने वाले अवसरों के आधार पर कार्रवाई की सिफारिश भी कर सकता है।

    व्यापार और मार्केटिंग  परिणामों की भविष्यवाणी करें

    एआई आपको परिणामों  के सफल पाठ्यक्रमों की भविष्यवाणी करने में मदद कर सकता है।

     

    एआई-पावर्ड सिस्टम सैकड़ों स्रोतों से डेटा का विश्लेषण कर सकते हैं और इस बारे में भविष्यवाणियां कर सकते हैं कि क्या काम करता है और क्या नहीं।

     

    एआई आपके ग्राहकों के बारे में डेटा एनालिटिक्स में भी गहराई से गोता लगा सकता है और उपभोक्ता वरीयताओं, उत्पाद विकास और मार्केटिंग चैनलों के बारे में भविष्यवाणियां कर सकता है।

    पूर्वानुमान मांग

    अपनी भविष्य कहनेवाला क्षमताओं के लिए धन्यवाद, AI उपलब्ध स्टॉक, मौसमी रुझानों, पिछले खरीद व्यवहार, और बहुत कुछ के आधार पर उत्पाद की मांग का अनुमान लगाने के लिए आपके एनालिटिक्स डेटा का उपयोग कर सकता है।

     

    इस डेटा विश्लेषण के आधार पर, व्यवसाय सुधार कर सकते हैं कि वे उत्पादों को कैसे स्टॉक करते हैं, इन्वेंट्री खरीदते हैं, या सामग्री खरीदते हैं। वे अन्य व्यवसाय या विपणन निवेश की योजना बनाने के लिए AI मांग पूर्वानुमान का भी उपयोग कर सकते हैं।

    आर्टिफिशियल इंटेलिजेंस दुनिया को हर पहलू में बदल रहा है। अब यह एक सच्चाई है कि पिछले दशक के अंत से ‘डेटा नया तेल है’। मैन्युफैक्चरिंग, कम्युनिकेशन, इंश्योरेंस, हैवी इंजीनियरिंग, डिफेंस से लेकर हेल्थकेयर तक, आर्टिफिशियल इंटेलिजेंस बिजनेस और इनोवेशन को चला रहा है।

    पैरामीटर

    एआई (Artificial Intelligence)

    डेटा साइंस

    परिभाषा

    मानव बुद्धि की तरह काम करने का क्षमता जो मशीनों द्वारा प्रकट की जाती है

    डेटा से ज्ञान प्राप्त करने और उसका उपयोग करके फैसलों को समर्थन देने की प्रक्रिया

    उदाहरण

    चेस खेलने वाला कंप्यूटर प्रोग्राम, चैटबॉट

    विश्लेषण और पैटर्न पहचान के माध्यम से विपणन की स्थिति का विश्लेषण करने से विपणन सुधार सुनिश्चित करने के लिए डेटा का उपयोग

    केंद्रीय धारा        मशीन शिक्षा       डेटा विश्लेषण, स्टैटिस्टिकल मॉडेलिंग

    प्रमुख उद्देश्य

    तर्कसंगत निष्कर्ष निकालना, पैटर्न पहचान, स्वायत्तता

    डेटा से ज्ञान प्राप्त करने के माध्यम से निर्णय और उपयोगी जानकारी प्राप्त करना

    उपयुक्त एल्गोरिदम

    न्यूरल नेटवर्क, गेनेटिक एल्गोरिदम, विशेषज्ञता प्रणाली

    लिनियर रिग्रेशन, क्लस्टरिंग, निर्णय पेडिंग

    उद्योग अनुप्रयोग

    स्वास्थ्य सेवाएं, वित्तीय विपणन, विनिमय व्यवस्था

    विपणन विश्लेषण, ग्राहक सेवाएं, वित्तीय पूर्ति

    डेटा आवश्यकताएँ

    बड़े मात्रा में डेटा आवश्यक

    डेटा विश्लेषण के लिए योग्य डेटा

    उपयोग की विधियाँ

    सुपरवाइज्ड, अनुपयोगी शिक्षा, स्वयं-संग्रहित शिक्षा

    विश्लेषण, मॉडेलिंग, विश्लेषण

    चुनौतियाँ

    सामग्री की कमी, नैतिक मामले, स्वायत्तता की सीमाएँ

    डेटा की गुणवत्ता, अद्यतन और सुरक्षा

     


    उत्तर: आर्टिफिशियल इंटेलिजेंस (AI) एक शाखा है जो कंप्यूटरों और मशीनों को मानव बुद्धि की तरह काम करने की क्षमता प्रदान करने के लिए डिज़ाइन की गई है।

    उत्तर: AI काम करने के लिए कई तकनीकों का उपयोग करता है, जैसे कि मशीन लर्निंग, न्यूरल नेटवर्क, गहरा लर्निंग, और संवैधानिक प्रोग्रामिंग। इन तकनीकों के माध्यम से, कंप्यूटर सिस्टम डेटा से सीखता है और समस्याओं का हल ढूंढता है।

    AI कई क्षेत्रों में उपयोग किया जाता है, जैसे कि स्वयं चालित गाड़ियाँ, चिकित्सा, वित्तीय सेवाएँ, संवाद विधाएँ, और अन्य।

    AI कम्प्यूटेशनल क्षमता को बढ़ाता है और समस्याओं के हल को और अधिक उत्तम बनाता है। यह मानवता के लिए समृद्धि, सुरक्षा, और और भी बहुत कुछ लाता है।

    हां, AI के उपयोग में निशाने पर अनुमानित और अनचाहे परिणाम हो सकते हैं, जैसे कि गोपनीयता की कमी, नैतिक मुद्दे, और बाहरी हमले। इसलिए, इसका उपयोग सावधानी से किया जाना चाहिए।

    उत्तर: AI के प्रमुख प्रकार हैं मशीन लर्निंग, न्यूरल नेटवर्क, गहरा लर्निंग, संवैधानिक प्रोग्रामिंग, और बड़ा डेटा विश्लेषण। ये तकनीक कंप्यूटरों को सीखने और समस्याओं का हल ढूंढने में मदद करते हैं।

    उत्तर: मशीन लर्निंग AI का एक उपकारी प्रकार है, जो कंप्यूटर को डेटा से सीखने की क्षमता प्रदान करता है। AI अन्य तकनीकों के साथ इस्तेमाल किया जा सकता है, जैसे कि न्यूरल नेटवर्क और गहरा लर्निंग।

    उत्तर: AI नई संभावनाओं को खोलता है, समय और वित्त की बचत करता है, ज्ञान को साझा करता है, विज्ञान और चिकित्सा में सुधार करता है, और बहुत कुछ।

    त्तर: हां, AI के उपयोग में नैतिकता का महत्व है। संदेश, सुरक्षा, और नैतिक मानकों का पालन करना आवश्यक है ताकि इसका उपयोग समाज के लिए सकारात्मक हो।

    : AI निरंतर विकास के रास्ते पर है और भविष्य में और भी अधिक उपयोगी और समृद्धि संपन्न हो सकता है। यह विभिन्न क्षेत्रों में नए और रोचक उपाय लाने में मदद कर सकता है।

    क्या है (Cloud Computing ) क्लाऊड कम्प्यूटिंग
    DATA SCIENCE TRAINING TO PLACEMENT

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