supervised and unsupervised machine learning:
    1Sep, 2023
    Supervised Machine Learning VS Unsupervised Machine Learning

    Supervised Machine Learning:

    Supervised machine learning. Supervised machine learning is a subfield of artificial intelligence (AI) and machine learning where the algorithm learns from labeled data to make predictions or classify new, unseen data. Here’s an overview of supervised machine learning:

    Imagine you’re teaching a child to recognize different animals. You show them pictures of cats, dogs, and horses, and you tell them which animal is in each picture. The child learns from your guidance and starts to recognize these animals on their own.

    In this scenario:

    • You, as the teacher, provide labeled data (telling them which animal is in each picture).
    • The child learns to make predictions (recognizing animals) based on the examples you’ve given.
    • This is similar to supervised machine learning.

    In supervised machine learning:

    • You have a dataset with labeled examples (input data with corresponding output or target labels).
    • The algorithm learns from these examples to make predictions or classifications when presented with new, unseen data.
    • Common tasks include image recognition (like the child recognizing animals), spam email classification, and predicting house prices based on features like size and location.

    Imagine you have a super-smart computer friend named AI-Buddy.

    AI-Buddy’s Superpower:

    It’s really good at learning from examples and making predictions.

    Here’s how AI-Buddy works:

    Step 1: Learning from Examples

    Imagine you have a bunch of pictures of different animals, and each picture comes with a label that tells you what animal is in it. For example, a picture of a cat is labeled “cat,” and a picture of a dog is labeled “dog.” These labels are like cheat sheets for AI-Buddy.

    So, you show all these pictures and labels to AI-Buddy. It looks at them and starts to notice patterns. It learns, “Oh, when I see pointy ears and whiskers, it’s usually a cat, and when I see floppy ears and a wagging tail, it’s usually a dog.”

    Step 2: Making Predictions

    Now, here’s the cool part. After AI-Buddy has learned from these examples, you can show it a new picture, one it has never seen before. You don’t tell AI-Buddy what’s in the picture; you keep it a secret. But AI-Buddy uses what it learned from the labeled pictures to guess what’s in the mystery picture.

    It might say, “Hmm, based on what I’ve learned, I think this new picture has a cat in it!” or “I think it’s a dog!”

    Step 3: Checking How Good It Is

    To make sure AI-Buddy is really good at guessing, you give it more mystery pictures with hidden labels. You see how many times it’s right and how many times it’s wrong. This helps you figure out if AI-Buddy is doing a great job or if it needs more practice.

    Some Special Jobs for AI-Buddy:

    Sorting Emails: You can use AI-Buddy to figure out which emails are spam (annoying) and which ones are not. It’s like having a personal email bouncer.

    Pricing Houses: If you want to sell your house, AI-Buddy can look at details like the size and location and tell you how much it’s worth. Handy for home sellers!

    Cool Facts:

    AI-Buddy can be trained to do all kinds of jobs, not just animals and houses. It can help doctors diagnose diseases, decide which movies to recommend, and even recognize your face to unlock your phone.

    Sometimes, AI-Buddy can get a bit too confident (like a student who memorizes answers but doesn’t understand). Or it can be too shy (like when it’s too scared to guess). So, we need to find the right balance to make it super smart.

    So, that’s supervised machine learning in a nutshell! It’s like having a smart friend who learns from examples and helps you make predictions. It’s used in lots of real-life things, making it easier for computers to understand the world around us.

     

    overview of supervised machine learning:

    Labeled Data:

    In supervised learning, you have a dataset that consists of input features and corresponding output labels. These labels provide the “ground truth” or correct answers, which the algorithm uses to learn and make predictions.

    Learning Objective:

    The primary goal of supervised learning is to learn a mapping or relationship between input features and output labels. This relationship is often represented by a mathematical model that can generalize from the training data to make predictions on new, unseen data.

    Examples of Supervised Learning Tasks:

    Classification:

    In classification tasks, the algorithm assigns data points to predefined categories or classes. For example, classifying emails as spam or not spam.


    Regression:

    In regression tasks, the algorithm predicts continuous numeric values. For instance, predicting house prices based on features like size and location.
    How Supervised Learning Works:

    The supervised learning process typically involves three main steps:

    • Training: The algorithm is exposed to the labeled training data, where it learns the relationships between input features and output labels.
    • Validation: A portion of the data (validation dataset) is used to fine-tune model hyperparameters and assess its performance before deployment.
    • Testing: The model’s performance is evaluated on a separate testing dataset to measure its ability to make accurate predictions on new, unseen data.
    • Evaluation Metrics: To assess the performance of a supervised learning model, various evaluation metrics are used, depending on the task. Common metrics include accuracy, precision, recall, F1-score (for classification), and mean squared error (MSE) or R-squared (for regression).

    Common Algorithms: There are numerous supervised learning algorithms, including:

    • Linear Regression: Used for regression tasks.
    • Logistic Regression: Commonly used for binary classification.
    • Decision Trees: Versatile for both classification and regression.
      Support Vector Machines (SVMs): Suitable for classification and regression.
    • Random Forest: An ensemble method for classification and regression.
    • Neural Networks: Deep learning models used for various tasks.
      Challenges:

    Common challenges in supervised learning include overfitting (when the model performs well on training data but poorly on new data) and underfitting (when the model is too simple to capture the data’s complexity).
    Applications: Supervised learning is applied in a wide range of fields, including healthcare (medical diagnosis), finance (credit scoring), natural language processing (text classification), and computer vision (image recognition).

    Supervised machine learning is a foundational concept in AI and data science, and it has enabled many real-world applications where making predictions or classifying data based on known patterns is crucial.

    Unsupervised Learning in Machine Learning

    Unsupervised Machine Learning:

    Unsupervised machine learning is another important subfield of artificial intelligence and machine learning. In unsupervised learning, algorithms work with unlabeled data to uncover hidden patterns, structures, or relationships within the data. Here’s an overview of unsupervised machine learning:

     

    Unsupervised machine learning is another important subfield of artificial intelligence and machine learning. In unsupervised learning, algorithms work with unlabeled data to uncover hidden patterns, structures, or relationships within the data. Here’s an overview of unsupervised machine learning:

    Now, let’s say you have a basket of assorted fruits, but you don’t know which fruits are similar or different. You ask the child to group the fruits based on their similarities without telling them the names of the fruits.

    In this case:

    • The child groups the fruits based on some hidden patterns or similarities they observe among them.
    • The child doesn’t have labels or prior information; they are exploring the data on their own.
    • This is similar to unsupervised machine learning.

    In unsupervised machine learning:

    • You have a dataset without labeled outcomes.
    • The algorithm tries to find patterns, similarities, or groupings in the data without any pre-existing knowledge.
    • Common tasks include clustering (grouping similar items), dimensionality reduction (simplifying complex data), and anomaly detection (finding unusual patterns in data).

    In summary, supervised machine learning is like teaching with labeled examples, while unsupervised machine learning is like discovering patterns and structures in data without any pre-existing information. Both approaches have their own uses and are valuable in different real-life applications.

    What Unsupervised Learning Does:

    Sorting Similar Things:

    One thing unsupervised learning can do is group similar things together. It’s like when you put all your toys in one box, your books on a shelf, and your clothes in a drawer without anyone telling you how to organize them.

    Simplifying Complicated Stuff:

    Imagine you have a really messy room with lots of different things scattered around. Unsupervised learning can help tidy up by finding ways to represent all those things with just a few key items, making it easier to understand.

    Spotting Odd Stuff:

    Let’s say you’re checking your piggy bank, and you notice that one coin doesn’t look like the others. Unsupervised learning can help you find those strange things, like a coin that doesn’t belong.

    How Unsupervised Learning Works:

    Think of unsupervised learning like a detective. It doesn’t have a list of suspects or clues; it has to find them itself. It uses math and patterns to organize, simplify, and spot unusual things in the data.

    When is Unsupervised Learning Useful:

    Unsupervised learning can be helpful when you want to organize a messy room, group similar things, or spot something unusual in a big pile of stuff. It’s like having a super-smart helper when you’re trying to make sense of things without a rulebook.

    Examples of Unsupervised Learning:

    Organizing Things: Imagine you have a collection of colorful marbles, and you want to group them by color. Unsupervised learning can do that without you telling it which colors are which.

    Cleaning Up Data:

    If you have a lot of messy data with lots of numbers, unsupervised learning can help simplify it, like turning a messy painting into a simple drawing.

    Detecting Strange Behavior:

    In a big crowd of people, unsupervised learning can help security teams find someone who’s acting strangely or doesn’t belong, just like a superhero spotting a hidden villain.

    Challenges of Unsupervised Learning:

    Sometimes, unsupervised learning might group things in a way that doesn’t make sense to us, or it might not find the odd things if they’re really tricky to spot. It’s like a detective who occasionally makes mistakes.

    Unsupervised learning is like a helpful detective for data, helping us organize, simplify, and spot unusual things in a way that makes complex data easier to understand.

    overview of unsupervised machine learning:

    Unlabeled Data:

    Unlike supervised learning, unsupervised learning deals with data that lacks predefined output labels. Instead, it focuses solely on the input features.

    Learning Objective:

    The primary goal of unsupervised learning is to discover and understand the inherent structure or organization within the data without relying on prior knowledge or guidance.

    Examples of Unsupervised Learning Tasks:

    • Clustering: Unsupervised algorithms group similar data points together. For instance, clustering customers based on their purchasing behavior.
    • Dimensionality Reduction: These algorithms reduce the number of input features while preserving important information. For example, simplifying a dataset with many attributes.
    • Anomaly Detection: Unsupervised methods can identify unusual or abnormal patterns in data, such as detecting fraudulent transactions in financial data.
    • Feature Learning: Techniques like autoencoders are used to automatically learn useful representations or features from raw data.

    How Unsupervised Learning Works:

    • Unsupervised learning generally involves exploring data without predefined labels or targets.
    • Algorithms analyze the data’s inherent structure, often using mathematical techniques like clustering, principal component analysis (PCA), or autoencoders.
    • The outcomes of unsupervised learning can include clusters of similar data points, reduced feature dimensions, or the identification of anomalies.
    • Evaluation Metrics: The choice of evaluation metrics in unsupervised learning depends on the specific task. For clustering, metrics like the silhouette score are used to measure the quality of clusters.

    Common Algorithms:

    • K-Means Clustering: Groups data points into clusters based on similarity.
      Hierarchical Clustering: Forms a hierarchy of clusters.
    • Principal Component Analysis (PCA): Reduces dimensionality by finding orthogonal axes capturing the most variance.
    • Autoencoders: Neural network-based models for feature learning.
      Isolation Forest: Anomaly detection algorithm.
      Applications:

    Unsupervised learning has applications in various domains, including marketing (customer segmentation), image compression, natural language processing (topic modeling), and network security (anomaly detection).
    Challenges:

    Unsupervised learning faces challenges such as determining the optimal number of clusters (in clustering tasks) and interpreting the discovered patterns.


    Use Cases:

    • Unsupervised learning can be used for exploratory data analysis, data preprocessing, and generating insights from large datasets.
    • Unsupervised machine learning is valuable for understanding data without prior labels or classifications. It helps uncover valuable insights and can be a critical step in the data analysis pipeline, enabling data scientists to make informed decisions based on the data’s inherent structure.

    Key differences between supervised and unsupervised learning

    Aspect

    Supervised Learning

    Unsupervised Learning

    Labeled Data Requirement

    Requires labeled data.

    Doesn’t require labeled data.

    Learning Objective

    Predict or classify based on labels.

    Discover data patterns/structures

    Teacher/Supervisor

    Requires a teacher/supervisor.

    Works without guidance.

    Output

    Generates predictions/classifications.

    Reveals hidden data structures.

    Common Applications

    Image recognition, sentiment analysis, recommendation systems.

    Clustering, dimensionality reduction, anomaly detection.

    Evaluation Metrics

    Accuracy, precision, recall, F1-score.

    Silhouette score, explained variance.

    Example Algorithms

    Linear Regression, Decision Trees

    K-Means Clustering, Principal Component Analysis.

    Feedback Loop

    Feedback loop for model improvement

    Limited feedback due to lack of labels.

    Data Preprocessing

    Often involves feature scaling, encoding.

    May involve preprocessing, less reliant on it.

    Validation Data Usage

    Common for tuning and validation.

    Less common, as no target labels.

     

     

     

    faq

    Supervised learning is a type of machine learning where an algorithm learns from labeled data to make predictions or classify new, unseen data.

    Labeled data consists of input features (data attributes) paired with corresponding output labels or target values. These labels provide the ground truth for training the model.

    Supervised learning algorithms analyze labeled data to identify patterns and relationships between input features and output labels. Once trained, the model can make predictions or classifications on new, unlabeled data.

    Supervised learning is used in various applications, including image recognition, natural language processing, recommendation systems, medical diagnosis, and financial forecasting.

    Common evaluation metrics include accuracy, precision, recall, F1-score (for classification tasks), and mean squared error (MSE) or R-squared (for regression tasks).

    in classification, the goal is to assign data points to predefined categories or classes. In regression, the goal is to predict continuous numeric values.

    Overfitting occurs when a model learns to perform exceptionally well on the training data but fails to generalize to new, unseen data. It often results from a model being too complex relative to the data.

    Underfitting happens when a model is too simple to capture the underlying patterns in the data. It performs poorly on both the training data and unseen data.

    A validation dataset is used to fine-tune model hyperparameters and assess its generalization performance before applying the model to real-world data. It helps prevent overfitting.

    One common example is the use of the Random Forest algorithm for classification tasks. It can be trained on labeled data to classify emails as spam or not based on various features.

    Unsupervised learning is a type of machine learning where algorithms work with unlabeled data to discover hidden patterns, structures, or relationships within the data

    The primary goal of unsupervised learning is to uncover the inherent structure or organization within data without having predefined output labels.

    Accordion Content

    In supervised learning, you have labeled data with input features and corresponding output labels, while unsupervised learning deals with unlabeled data and focuses solely on input features.

    Common tasks include clustering (grouping similar data points together), dimensionality reduction (simplifying data by reducing the number of features), anomaly detection (finding unusual patterns), and feature learning (automatically extracting useful features from data).

    Think of unsupervised learning as a detective who explores a jigsaw puzzle without a picture on the box. The detective tries to group similar puzzle pieces and discover any unusual ones to solve the puzzle.

    Unsupervised learning algorithms use mathematical techniques to analyze data and identify patterns. For example, clustering algorithms group similar data points, and dimensionality reduction techniques simplify complex data.

    The choice of evaluation metrics depends on the specific task. For clustering, metrics like silhouette score measure the quality of clusters. For dimensionality reduction,

    Common algorithms include K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), Autoencoders, and Isolation Forest for anomaly detection.

    Unsupervised learning is used in various domains, including customer segmentation in marketing, image compression in media, topic modeling in natural language processing, and network security for anomaly detection.

    Challenges include determining the optimal number of clusters in clustering tasks, interpreting the discovered patterns, and handling noisy or complex data.

    nsupervised learning is useful when you want to explore data without prior labels, organize data into meaningful groups, simplify complex data, or detect unusual patterns in large datasets.

    Top 10 Example of machine learning for Data Analytics
    24Aug, 2023
    Step by step guide for Predictive model for Data Analytics

    Predictive modeling is a mathematical procedure that analyzes patterns in a set of input data to predict future events or outcomes. It is an essential component of predictive analytics, a sort of data analytics that predicts activity, behavior, and trends using present and past data.

    Example

    Example: Smart Weather Umbrella Alert

    Imagine you have a magical umbrella that can predict when it’s going to rain. This special umbrella uses a smart predictive model to keep you dry and comfortable.

    How it works:

    Magic Umbrella Data:

    This magical umbrella collects data from the sky, like clouds, humidity, and wind speed.
    It also knows your location, so it can understand the weather around you.


    The Magical Prediction:

    The umbrella uses its magical powers (actually, a smart computer program) to analyze the data.
    It looks for patterns in the past to guess when it might rain in the future.


    Your Personal Rain Alert:

    When the magical umbrella thinks it’s going to rain, it sends you a cheerful notification on your phone.
    The notification says something like, “Hey there! I have a hunch that rain might be on its way. Don’t forget me!”


    Umbrella’s Hints:

    Sometimes the magical umbrella might say, “I’m feeling extra sure about rain today, so consider bringing me along!”
    Other times, it might say, “It’s a bit iffy, so you might want to take me just in case.”


    Be Prepared and Happy:

    Thanks to your smart umbrella, you’re always prepared for unexpected rain.
    You can avoid getting wet and have a happier day because you’ve got your magical rain predictor with you.


    Learning and Fun:

    The more you use the magical umbrella, the smarter it gets. It learns from when it’s right and when it’s wrong.
    It’s like having a little weather buddy that learns about the sky while you both go on adventures.


    Sharing the Magic:

    You tell your friends about your magical umbrella, and they want one too!
    Now everyone can have their own rain-savvy companion, and nobody gets caught off-guard by rain anymore.


    This magical umbrella story simplifies the concept of a predictive model. It takes data from the environment, uses patterns from the past, and gives you helpful predictions to make your day better. It’s like having a trusty friend who knows when to pop open their umbrella and keep you dry.

    Types of Predictive Analytics

    There are numerous methods for classifying predictive models, and in practice, multiple types of models may be combined to achieve the best results. The main difference is between unsupervised and supervised models.

    Expanation with Example

    Supervised Predictive Model: Virtual Plant Whisperer

    Imagine you have a virtual plant whisperer named Lily. Lily is a master at predicting how much water different types of plants need to thrive.

    How Lily Works (Supervised Model):

    Learning from Plant Data:

    Lily has learned from a big collection of plants. For each plant, she knows its type, how much sunlight it gets, and how often it’s watered.


    Predicting Water Needs:

    When you introduce Lily to a new plant, you tell her the type and sunlight it gets. Lily takes a look at her plant database and predicts how often you should water it to keep it happy.


    Testing and Learning:

    Whenever you water the plant, you tell Lily how often you watered it. Lily remembers this and learns. Over time, she gets better at predicting and suggesting watering schedules for different plants.


    Helping Your Plants Flourish:

    Thanks to Lily’s predictions, your plants thrive! She’s like a personalized plant coach, making sure each one gets just the right amount of water, whether they’re sun-loving succulents or shade-loving ferns.
    Unsupervised Predictive Model: Party Playlist Genie

    Imagine you have a party playlist genie named Groove. Groove is an expert at finding the perfect songs to set the vibe at any gathering.

    How Groove Works (Unsupervised Model):

     

    Analyzing Musical Vibes:

    Groove starts with a giant collection of songs and their musical characteristics like tempo, energy, danceability, and mood.


    Grouping Songs Naturally:

    Without you telling Groove anything about specific songs, she uses her magic (a clustering algorithm) to group similar songs together based on their musical vibes.


    Understanding Party Moods:

    You describe the mood you want for your party: “energetic and danceable” or “chill and laid-back.” Groove knows the groups of songs that match these moods based on her magical groupings.


    Creating Tailored Playlists:

    When you give Groove a party mood, she uses her groupings to recommend a mix of songs that fit your desired vibe. It’s like she’s reading your mind for the perfect playlist!


    Party Jam Success:

    Thanks to Groove’s expertise, your party playlist becomes a hit. She’s like a musical maestro, curating tunes that make your guests groove to the rhythm and have a blast.


    Both Lily and Groove showcase the power of predictive models. Lily predicts water needs for plants, making you a green thumb, while Groove crafts playlists that make your parties unforgettable. It’s like having magical assistants that understand and enhance your world!

     

    How it work for Supervised Predictive Model

    How it Works:

    Learning from Data:

    Supervised learning involves having a labeled dataset where you know the input data (features) and the corresponding output (target). In the plant example, you have data about various plants, including the amount of sunlight they get and how often they’re watered.


    Training the Model:

    You use this labeled data to train your predictive model, such as a regression model. The model learns the relationship between the features (sunlight) and the target (watering frequency).


    Predictions:

    Once trained, the model can predict outcomes for new, unseen data. You provide Lily with the type of plant and its sunlight, and the model predicts how often to water it based on what it learned from the training data.

    How it Works: Unsupervised Predictive Model:

    How it Works:

    Clustering Similar Data:

    Unsupervised learning involves finding patterns in data without labeled outcomes. In the playlist example, Groove uses unsupervised learning to cluster similar songs together based on their musical features.


    Grouping Songs:

    Instead of predicting a specific output, Groove groups songs that share similar musical characteristics. These groups are found naturally by the algorithm.


    Understanding Party Moods:

    When you describe the mood for your party, Groove identifies the group of songs that match that mood. The unsupervised model didn’t need labeled “party” or “chill” songs—it just grouped songs based on their inherent similarities.

    Approach in Supervised Predictive Model and Unsupervised Predictive Model:

    Supervised Predictive Models:

    • Collect a dataset with known inputs and corresponding outputs.
    • Choose an appropriate algorithm based on the problem (classification or regression).
    • Split the data into training and testing sets.
    • Train the model on the training data, adjusting model parameters.
    • Evaluate the model’s performance on the testing data using metrics like accuracy, precision, recall, or RMSE.
    • Fine-tune the model and validate its generalization ability.


    For Unsupervised Predictive Models:

    • Collect a dataset with features but no labeled outputs.
    • Choose a suitable clustering algorithm (k-means, hierarchical clustering, etc.).
    • Apply the algorithm to group similar data points based on features.
    • Understand the natural groupings and patterns that emerge.
    • Use the clusters for various applications like recommendation, segmentation, or analysis.

     


    The key difference lies in the availability of labeled outcomes. In supervised learning, you have labeled data to teach the model, whereas in unsupervised learning, the model identifies patterns and similarities on its own without explicit labels. The approaches for each depend on the type of learning you’re using.

     

    Aspect

    Supervised Model

    Unsupervised Model

    Nature of Learning

    Learns from labelled data with inputs and corresponding outputs.

    Learns patterns and relationships from unlabelled data without specific outputs.

    Data Requirement

    Requires labeled training data

    Requires only input data

    Example              

    Virtual Plant Whisperer

    Party Playlist Genie

    Process

    Collect labeled data. 2. Train model. 3. Predict based on learned relationships.

    1. Collect unlabelled data. 2. Apply clustering to group similar data. 3. Use clusters for analysis or other tasks.

    Problem Types

    Classification, Regression

    Clustering, Dimensionality Reduction, Anomaly Detection, etc.

    Output Prediction           

    Predicts specific outputs based on input features.             

    Identifies natural groupings or patterns in the data

    Usage Example 

    Predicting housing prices based on features like area, location, etc.

    Grouping customer segments for targeted marketing.

    Evaluation

    Model’s predictions are compared to actual outputs.

    Quality of clusters is assessed based on similarity within clusters and dissimilarity between clusters.

    Data Splitting

    Data is split into training and testing sets, with labelled outputs in both sets.

    No labelled outputs needed; data can be split into training and testing sets based on features only.

    Model Tuning

    Adjust model parameters to improve prediction accuracy.

    Fine-tune clustering parameters to improve the quality of clusters.

    Use Cases

    Customer churn prediction, spam detection

    Market segmentation, image compression

    • Unsupervised models use traditional statistics to classify the data directly, using techniques like logistic regression, time series analysis and decision trees.
    • Supervised models use newer machine learning techniques such as neural networks to identify patterns buried in data that has already been labeled.
    data science course in India

    The most popular methods include

    Decision Trees

    1. Decision Trees Overview:

    Decision trees are a type of supervised learning algorithm that breaks down a dataset into smaller subsets while creating a tree-like model of decisions.
    These decisions or branches are made based on features (input variables) in the data and their relationships with the target variable.

    2. Graphical Representation:

    Decision trees visually represent the decision-making process as a tree structure with nodes (decisions), branches (outcomes), and leaves (final predictions or classifications).

    3. Classification and Prediction:

    Decision trees can be used for both classification and regression tasks.
    In classification, they categorize data points into classes or categories based on the features.
    In regression, they predict a continuous numerical value based on the input features.

    4. Handling Incomplete Data:

    Decision trees can handle missing values and incomplete datasets more gracefully than some other algorithms.
    They can make decisions based on available features and accommodate missing values by considering alternative paths.

    5. Explainability and Accessibility:

    One of the major strengths of decision trees is their interpretability.
    The visual nature of the tree makes it easy to understand how decisions are being made and what factors are influencing predictions.
    This transparency is valuable for novice data scientists, stakeholders, and domain experts who need insights into the model’s reasoning.

    6. Potential Limitations:

    While decision trees are easy to interpret and explain, they can become overly complex and prone to overfitting, especially when the tree grows too deep.
    Ensemble methods like Random Forests and Gradient Boosting Trees are often used to mitigate this issue by combining multiple decision trees.

    Use Case Example: Customer Churn Prediction:

    Imagine a telecommunications company wants to predict whether a customer will churn (cancel their subscription).
    The company can use a decision tree to analyze customer data like contract length, usage patterns, and customer service interactions.
    The decision tree can provide insights into what factors contribute most to customer churn, helping the company make targeted retention efforts.
    Overall, your description provides an insightful overview of decision trees and their practical applications in data analytics. They are a versatile and powerful tool that offers a balance between accuracy, interpretability, and ease of use.

    Time Series Analysis

    1. Time Series Analysis Overview:

    Time series analysis involves studying data points that are ordered and collected over time intervals, such as days, months, or years.
    The data is often a sequence of observations, measurements, or measurements recorded at specific time intervals.


    2. Predicting Future Events:

    Time series analysis aims to predict future values based on historical data patterns.
    By identifying trends, seasonality, and other patterns in the data, the technique extrapolates this information to make predictions.


    3. Past Trends and Extrapolation:

    The analysis relies on the assumption that past behaviors or trends in the data will continue into the future.
    By understanding how data evolves over time, you can make educated predictions about what might happen next.


    4. Components of Time Series:

    Time series data often consists of various components, such as trend (long-term movement), seasonality (repeating patterns), and noise (random fluctuations).


    Use Case Example: Stock Price Prediction:

    Imagine you’re analyzing stock prices for a particular company.
    By applying time series analysis, you can identify trends and patterns in historical stock prices.
    If there’s a consistent upward trend over a certain period, you might predict that the stock’s value will likely increase in the near future.


    5. Forecasting Techniques:

    Time series analysis involves a range of techniques, including moving averages, exponential smoothing, and more advanced methods like ARIMA (AutoRegressive Integrated Moving Average) and machine learning-based models.


    6. Importance in Various Fields:

    Time series analysis is used in economics, finance, weather forecasting, epidemiology, and various other domains where data evolves over time.

    Logistic Regression Overview:

    1. Logistic Regression Overview:

    Logistic regression is a statistical technique used for binary classification, which means it’s particularly effective when you’re dealing with problems where the outcome can be one of two classes, like “yes” or “no,” “spam” or “not spam,” etc.


    2. Data Preparation and Sorting:

    Logistic regression helps in preparing data for classification tasks by finding the best-fitting line that separates the two classes based on the given features.
    The algorithm aims to draw a decision boundary that best divides the data into these distinct categories.


    3. Learning from Data:

    As more data is provided, the algorithm learns from it and adjusts the decision boundary to improve its accuracy in sorting and classifying new data points.


    4. Prediction Capability:

    Once the logistic regression model has learned from data, it can be used for making predictions on new, unseen data points.
    For instance, if you’ve trained a logistic regression model to classify whether an email is spam or not based on keywords, it can predict the likelihood of an incoming email being spam.


    5. Probabilistic Output:

    Unlike linear regression, which predicts a continuous output, logistic regression outputs a probability score.
    This probability score represents the likelihood of an instance belonging to a particular class (e.g., the probability of an email being spam).


    Use Case Example: Customer Churn Prediction:

    Imagine a telecom company wants to predict whether a customer will churn (cancel their subscription).
    Logistic regression can be used to analyze customer data like contract length, usage patterns, and customer service interactions to predict the likelihood of churn.


    6. Importance of Features:

    Logistic regression considers the importance of different features in predicting the outcome.
    It assigns weights to each feature based on its influence on the prediction.


    7. Regularization:

    Logistic regression can be extended to include regularization techniques that help prevent overfitting, which occurs when the model fits the training data too closely and doesn’t generalize well to new data.


    8. Interpretability:

    Logistic regression is relatively interpretable. The coefficients of the features can provide insights into the direction and magnitude of their impact on the prediction.
    Potential Considerations:

    Logistic regression assumes that the relationship between the features and the log-odds of the outcome is linear.

    Neural Networks Overview:

    1.Neural Networks Overview:

    Neural networks are a class of machine learning algorithms inspired by the human brain’s structure and functioning.
    They’re designed to process complex data and identify patterns by learning from examples.


    2. Analyzing Large Volumes of Labeled Data:

    Neural networks thrive on labeled data, where each data point is associated with a correct output or target.
    By reviewing a substantial amount of such data, neural networks learn to recognize correlations and relationships within the data.


    3. Correlation Detection and Feature Extraction:

    Neural networks automatically extract relevant features from the data without explicit programming.
    They detect intricate correlations between variables that might be challenging to identify through traditional programming.


    4. Artificial Intelligence (AI) Applications:

    Neural networks serve as the foundation for various AI applications, including:
    Image Recognition: They excel in recognizing objects, patterns, and structures within images.
    Smart Assistants: Power speech recognition and natural language understanding in assistants like Siri, Alexa, and Google Assistant.
    Natural Language Generation: Generate human-like text, making chatbots and content creation more natural.


    Use Case Example: Image Classification:

    Imagine you’re building an image classification system to identify different species of flowers.
    Neural networks analyze thousands of labeled flower images, learning to distinguish unique features of each species.


    5. Layers and Neurons:

    Neural networks consist of layers of interconnected nodes called neurons.
    Input layer receives data, hidden layers process it, and output layer provides predictions or classifications.


    6. Activation Functions:

    Activation functions introduce non-linearity to neural networks, allowing them to capture complex relationships in the data.


    7. Training Process:

    Neural networks are trained through a process called backpropagation, where errors in predictions are used to adjust the weights of connections between neurons.


    8. Deep Learning and Complexity:

    Deep learning refers to neural networks with multiple hidden layers (deep architectures).
    Deep neural networks can capture intricate patterns and hierarchies in data, leading to advanced AI capabilities.


    Potential Challenges:

    Neural networks require substantial computational power and labeled data.
    Proper tuning of hyperparameters is crucial to achieving optimal performance.

    Data Analytics Insights Predictive Power in Excel
    data visualization in Data analytics
    11Aug, 2023
    What is data Analytics and Power of Data Analytics in Hindi

    डेटा से अनमोल जानकारी की खोज: डेटा एनालिटिक्स की शक्ति"

    आजकल के डिजिटल युग में, जानकारी हर तरफ है। लेकिन हम अपने चारों तरफ बिखरे डेटा के समय का सही तरीके से समझने में कैसे सक्षम हो सकते हैं? यहां डेटा एनालिटिक्स की जादूई ताक़त काम आती है। क्या आपने कभी सोचा है कि आपके पास लाखों छोटे-छोटे टुकड़ों से मिलकर बने एक विशाल पहेली हो? डेटा एनालिटिक्स उस महान पहेली के सभी टुकड़ों को मिलाकर एक खूबसूरत चित्र दिखाती है

    आसान शब्दों में कहें तो, डेटा एनालिटिक्स एक खज़ाने का नक्शा है। हम जो जानकारी इकट्ठा करते हैं, वो संकेत होते हैं, और डेटा विश्लेषक मौजूदा समय के संचालक होते हैं। वे विशेष उपकरणों और तकनीकों का उपयोग करके डेटा में खुदाई करते हैं और मूल्यवान जानकारियाँ ढूंढ़ते हैं जो अन्यथा छिपी रह सकती हैं।

    Example explain Data Analytics Hindi

    चलिए एक दिनबदिन का उदाहरण ले लें। कीजिए आप एक छोटे से आइसक्रीम की दुकान के मालिक हैं। आप डेटा इकट्ठा करते हैं कि कौन-कौन से आइसक्रीम फ्लेवर सबसे ज्यादा बिक रहे हैं और किन समयों पर। इस डेटा का विश्लेषण करके, आपके सामने आ सकता है कि गरम दिनों में, लोग प्यार करते हैं फलों के फ्लेवर्ड आइसक्रीम, जबकि ठंडी शामों में, वे अमीर और क्रीमी आइसक्रीम का चयन करते हैं। इस जानकारी के साथ, आप अपने स्टॉक को उसी अनुसार रख सकते हैं और अपने ग्राहकों की सेवा बेहतर तरीके से कर सकते हैं।

    इसलिए, चाहे आपकी पसंदीदा आइसक्रीम बनाना हो या यह पूर्वानुमान करना हो कि आप किस फ़िल्म को देखना पसंद करेंगे, डेटा एनालिटिक्स हमारे जीवन के विभिन्न पहलुओं में एक भूमिका निभाती है। यह एक ऐसा क्रिस्टल बॉल की तरह है जो व्यवसायों और व्यक्तिगत स्तर पर डेटा में छिपे पैटर्न के आधार पर बेहतर निर्णय लेने में मदद करता है।

    इसलिए, अगली बार जब आप डेटा एनालिटिक्स के बारे में सुनें, तो याद रखें कि यह एक ऐसा सुपर डिटेक्टिव है जो हमारे चारों ओर के बड़े समुद्र में सामान्य दृश्य में छिपे रहस्यों की पर्दाफाश करता है।

    Decoding the Magic of Data Analytics: Hindi

    “डेटा एनालिटिक्स का जादू कैसे काम करता है: जानकारी से अनमोल दर्शनों की पर्दाफाश”**

    क्या आप सोच सकते हैं कि हम अपने चारों तरफ की बड़ी सी जानकारी के समुंदर में छिपे हुए पैटर्न्स को पहचान सकते हैं, जैसे कि एक खज़ाने के नक्शे के रहस्यों को सुलझाना। यही वह बात है जो डेटा एनालिटिक्स करता है। यह ऐसे ही एक कांची की तरह काम करता है जो हमें सतह से पार देखने में मदद करता है, और कच्चे डेटा को मूल्यवान दर्शनों में बदल देता है।
    आइए इसे और सरल तरीके से समझें। सोचिए कि आप एक डिटेक्टिव हैं और डेटा आपकी अपराध स्थल है। डेटा एनालिटिक्स आपको विशेष उपकरणों और तकनीकों से लैस करता है ताकि आप प्रत्येक संकेत – हर नंबर, शब्द या क्लिक – की जांच कर सकें और जोड़ियाँ ढूंढ सकें जो एक कहानी सुनाती है। यह एक पहेली को एक साथ रखने की तरह है, टुकड़े-टुकड़े करके, जब तक स्पष्ट तस्वीर उभर नहीं जाती।
    अपने स्मार्टफोन को ध्यान में रखें। जिसे आप उपयोग करते हैं, जिस वेबसाइट पर आप जाते हैं, वह सब एक डिजिटल पदचिह्न छोड़ते हैं। डेटा एनालिटिक्स कंपनियों को आपकी पसंदों को समझने में मदद करता है, जिससे वह विज्ञापन बना सकती है जो आपको इतने अच्छे से जानते हैं। यह एक ऐसे व्यक्तिगत खरीददार की तरह है जो आपकी आवश्यकताओं की पूर्वानुमानी करता है।
    मूलतः, डेटा एनालिटिक्स कच्चे डेटा और मायनेदार निर्णयों के बीच का सेतु है। यह हमें सूचना को ज्ञान में बदलने की शक्ति प्रदान करता है, पहले ही विद्वेष्य होने से पहले प्रवृत्तियों को पहचानने में मदद करता है, और सफलता की ओर जाने वाले चयन करने में सहायक होता है।

    Types of Data in Data Science:

    Exploring Different Types of Data Analysis in Hindi

    डेटा एनालिटिक्स की दुनिया की खोज: विभिन्न प्रकार के डेटा विश्लेषण का अन्वेषण”**

    क्या आपने कभी सोचा है कि कैसे कंपनियाँ, वैज्ञानिक और खेल टीमें सफलता के लिए एक गुप्त रेसिपी रखती हैं? यह राज डेटा एनालिटिक्स में छिपा होता है, जहाँ विभिन्न प्रकार के डेटा विश्लेषण तकनीकों का महत्वपूर्ण योगदान होता है जिससे छिपी हुई जानकारियों की पर्दाफाश होती है और सूचित निर्णय लिए जाते हैं।

    Descriptive Analytics): छवियों को जीवंत करने वाला डेटा ज्ञान

    डेटा विश्लेषण के एक अहम रूप को वर्णनात्मक एनालिटिक्स कहा जाता है। यह एक जैसे डेटा के विभिन्न पहलुओं को समझने और प्रस्तुत करने की कला है, जिससे आपको डेटा के साथ आपके आसपास की घटनाओं का एक पूरा चित्र प्राप्त होता है। यह आपके डेटा के साथ किये जाने वाले सामान्य गतिविधियों और प्रवृत्तियों का एक सामान्य जानकारी प्रदान करता है, जिससे आप अपने डेटा में छिपी हुई जानकारी का पता लगा सकते हैं।

    वर्णनात्मक एनालिटिक्स के मुख्य पहलु:

    समारचना और प्रस्तुति:Summarizing and Presenting

    वर्णनात्मक एनालिटिक्स से आप डेटा की संक्षिप्त समारचना और तबलों, ग्राफों, चार्ट्स और अन्य ग्राफिकल उपकरणों के माध्यम से डेटा की प्रस्तुति कर सकते हैं। यह आपके डेटा में विशेष प्रमुख अद्यतनों और प्रवृत्तियों की चित्रण करने में मदद करता है।


    तारीख़ और समय की  विश्लेषण:Time and Date Analysis:

    यह आपको डेटा में तारीख़ और समय की प्रमुख प्रवृत्तियों की पहचान करने में मदद करता है। आपके पास यह जानकारी होने पर, आप आपके डेटा की विभिन्न समय सीमाओं में कैसे बदलाव हो रहे हैं या किसी विशिष्ट तारीख़ के आसपास क्या परिवर्तन हो रहे हैं वो जान सकते हैं।
    वर्गीकरण और गुणवत्ता का

    मूल्यांकन:Categorization and Quality Assessment:

    वर्गीकरण और गुणवत्ता का मूल्यांकन करके, आप डेटा को विभिन्न समूहों में विभाजित कर सकते हैं और उनकी गुणवत्ता को मूल्यांकन कर सकते हैं। इससे आप विशिष्ट समूहों के बीच विभिन्नताओं और समानताओं की पहचान कर सकते हैं।

    प्रमुखताएँ और प्रवृत्तियाँ Identifying Patterns and Trends:

    वर्णनात्मक एनालिटिक्स  से आप डेटा में प्रमुख प्रवृत्तियों की पहचान कर सकते हैं जो कि सामान्यत: अद्यतन दिन, महीने या साल के आधार पर होती हैं। आपके पास यह जानकारी होने से, आप विशिष्ट प्रमुखताओं के बीच के अद्यतन में कैसे वैशिष्ट्यपूर्णता का पता लगा सकते हैं।
    वर्णनात्मक एनालिटिक्स आपको उपलब्ध डेटा के साथ जो घटनाएँ, प्रवृत्तियाँ और विशिष्टताएँ होती हैं उन्हें समझने में मदद करता है। यह डेटा की वास्तविकता को खोजने में मदद करके, आपके निर्णयों को स्पष्टीकरण करने में महत्वपूर्ण भूमिका निभाता है।

    चिकित्सा विश्लेषण (Diagnostic Analytics): डेटा की डॉक्टरी जांच

    आपने तो डॉक्टर की जांच करवाई है ना, जब आपको बुखार या बीमारी होती है? वैसे ही, डेटा विश्लेषण में चिकित्सा विश्लेषण का काम होता है डेटा की जांच करने का। इससे हमें पता चलता है कि क्या हुआ, क्यों हुआ और कैसे हुआ।

    चिकित्सा विश्लेषण के मुख्य पहलु:

    Identifying the Ailment:बीमारी की पहचान:

    डेटा के पैटर्न से हम यह समझ सकते हैं कि कुछ बदलता क्यों है, या किसी घटना के बाद क्या अच्छा या बुरा हो रहा है। जैसे हम खुद की चेकअप में जाकर बताते हैं कि हमारी तबियत कैसी है, वैसे ही डेटा विश्लेषण से हम देख सकते हैं कि हमारे डेटा में क्या-क्या हो रहा है।

    Probing the Cause Behind Eventsघटना के पीछे की जांच:

    डेटा विश्लेषण से हम वो असली कारण जान सकते हैं जो किसी घटना को होने पर प्रेरित करता है। जैसे हम किसी बीमारी का उपचार करते समय उसके कारण की जांच करते हैं, वैसे ही डेटा विश्लेषण से हम उस घटना के पीछे की सच्चाई को समझ सकते हैं।

    Checking for Updates and Trends: अद्यतन और प्रवृत्ति की जांच:

    डेटा विश्लेषण से हम यह देख सकते हैं कि कैसे कुछ बदलते हुए हैं और इसके पीछे का कारण क्या है। यह हमें विभिन्न समयों में क्या बदलाव हो रहा है, यह दिखा सकता है, और हमें उसके पीछे की वास्तविकता को समझने में मदद करता है।

    Offering Solution Suggestions:उपाय की सलाह:

    डेटा विश्लेषण से हम यह समझ सकते हैं कि किस तरह की क्रियाएँ लेनी चाहिए ताकि समस्या का समाधान हो सके। इससे हम उपाय की सलाह प्राप्त कर सकते हैं, जिससे कि हम समस्या का समाधान कर सकें।

    चिकित्सा विश्लेषण: आपके डेटा में छिपी हुई समस्याओं की जांच में आपकी मदद करने वाला सहायक और महत्वपूर्ण टूल।

    पूर्वानुमानात्मक एनालिटिक्स (Predictive Analytics):

     आगामी कार्यों की भविष्यवाणी


    क्या कभी किसी चीज के पैटर्न को देखकर आपने किसी घटना की पहचान करने का प्रयास किया है? बिल्कुल वैसे ही, पूर्वानुमानात्मक एनालिटिक्स डेटा का उपयोग करके आगामी घटनाओं की संभावित पहचान करता है। यह आपको बताता है कि क्या कुछ होने का संकेत हो सकता है और आपको उसके लिए तैयारी करने में मदद करता है।

    क्या आपने कभी उन लोटरी नंबरों की भविष्यवाणी की है जिन्होंने आपको जीतने में मदद की है? वैसे ही, पूर्वानुमानात्मक एनालिटिक्स डेटा का उपयोग करके आगामी कार्यों की संभावनाओं की भविष्यवाणी करता है। यह आपको बताता है कि क्या हो सकता है आने वाले समय में और आपको तैयारी करने में मदद करता है।

    पूर्वानुमानात्मक एनालिटिक्स के प्रमुख पहलु:

     

    कार्यों की संभावनाओं की पहचान: Recognizing Event Possibilities


    जैसे हम लोटरी के नंबरों में पैटर्न देखकर उनकी भविष्यवाणी करते हैं, पूर्वानुमानात्मक एनालिटिक्स डेटा के पैटर्न को देखकर आगामी कार्यों की संभावनाओं की पहचान करता है। यह आपको बताता है कि किसी निश्चित घटना का होने का कितना अच्छा मौका हो सकता है।

    तय समय में परिणामों की भविष्यवाणी: Foreseeing Outcomes at Specific Times


    पूर्वानुमानात्मक एनालिटिक्स से हम यह भी पहचान सकते हैं कि किस समय किसी घटना का परिणाम हो सकता है। यह आपको बताता है कि आने वाले समय में किसी क्रियावली का परिणाम कैसा हो सकता है और आप उसके लिए तैयारी कर सकते हैं।

    विशेषताएँ और पैटर्न की भविष्यवाणी:Forecasting Features and Trends:


    पूर्वानुमानात्मक एनालिटिक्स से हम यह समझ सकते हैं कि विशिष्ट विशेषताएँ या पैटर्न आने वाले समय में कैसे बदल सकते हैं। यह हमें यह दिखाता है कि किस तरह की परिवर्तन की संभावना है और आपको उसके लिए कैसे तैयार रहना चाहिए।

    निर्णय लेने की मदद: Aiding Decision-making


    पूर्वानुमानात्मक एनालिटिक्स से हम यह भी जान सकते हैं कि किसी कार्य का परिणाम क्या हो सकता है और आप उसके आधार पर निर्णय ले सकते हैं। यह आपको विभिन्न विकल्पों की पहचान में मदद करता है जिससे आप सटीक और समर्पित निर्णय ले सकते हैं।

    प्रेस्क्रिप्टिव एनालिटिक्स Predictive Analytics

    दोस्तों, आज हम एक बहुत ही रोचक और महत्वपूर्ण विषय पर बात करने वाले हैं – “प्रेस्क्रिप्टिव एनालिटिक्स”। यह एक ऐसी तकनीक है जिससे हम भविष्यवाणी करके नहीं, बल्कि सही निर्णय लेने में मदद करते हैं। इसे साधारण शब्दों में समझने का प्रयास करते हैं:

    प्रेस्क्रिप्टिव एनालिटिक्स क्या है?


    अगर हम इसे सिंपल शब्दों में कहें, तो यह वह तकनीक है जिसका उपयोग हम वे निर्णय लेने में करते हैं जो हमें सबसे अच्छा नतीजा प्राप्त करने में मदद करते हैं।

    इसका क्या उपयोग हो सकता है?


    चिंता न करें, हम इसे एक उदाहरण के माध्यम से समझेंगे। सोचिए, आपके पास एक सुपरमार्केट है और आपको दिन-रात दिल लगाकर काम करना होता है। अब आपके पास डेटा है जैसे कि किस समय पर सबसे ज्यादा ग्राहक आते हैं, कौनसे उत्पाद ज्यादा बिकते हैं, आदि। तो प्रेस्क्रिप्टिव एनालिटिक्स की मदद से आप उन दिनों और समय को पहचान सकते हैं जब आपको ज्यादा कर्मचारियों की आवश्यकता होती है और कौनसे उत्पादों की आपको ज्यादा स्टॉक रखने की आवश्यकता होती है। इससे आप अधिक सामग्री की बर्बादी से बच सकते हैं और ज्यादा मुनाफा कमा सकते हैं।

     

    कैसे काम करता है?


    यह तकनीक डेटा को विश्लेषित करके प्राथमिक जानकारी प्रदान करती है, जिससे हम सही निर्णय ले सकते हैं। यह जानने में मदद करता है कि किस प्रकार की स्थितियों में कौन से कदम उठाने चाहिए ताकि हम अच्छे परिणाम प्राप्त कर सकें।

    क्या यह महत्वपूर्ण है?


    बिल्कुल! आप सोच सकते हैं कि यदि हम सही समय पर सही निर्णय नहीं लेते हैं, तो हमारे व्यवसाय में नुकसान हो सकता है। प्रेस्क्रिप्टिव एनालिटिक्स हमें उन स्थितियों की पहचान करने में मदद करता है जिनमें हमें सुधार की आवश्यकता है ताकि हम बेहतर परिणाम प्राप्त कर सकें।

    इस तरह से, प्रेस्क्रिप्टिव एनालिटिक्स हमें सहायता प्रदान करता है ताकि हम सही समय पर सही निर्णय लें और हमारे कार्यों को बेहतर

     

    बिना ज्यादा जानकारी के आपको प्रेस्क्रिप्टिव एनालिटिक्स को समझाते हैं, निम्नलिखित पॉइंट्स में:

    1. विश्लेषण का जादू:


    प्रेस्क्रिप्टिव एनालिटिक्स वह जादू है जो हमारे पास डेटा है और हमें बताता है कि डेटा में क्या हो रहा है।

    2. पूर्वानुमान की मदद:


    यह हमें पहले से जानकारी देता है कि कौन सी चीजें हो रही हैं और हमारे पास उन पर कैसे प्रतिक्रिया देनी चाहिए।

    3. बेहतर निर्णय:


    जब हमें डेटा के पूरे दृश्य का पता होता है, हम सही निर्णय लेने में मदद करने के लिए तैयार होते हैं।

    4. अपने व्यवसाय को समझना:


    यह हमें हमारे व्यवसाय के बारे में नए दृष्टिकोण प्रदान करता है जिससे हम अपने ग्राहकों और उत्पादों की पसंद को समझ सकते हैं।

    5. दुरुपयोग से बचाव:


    यह हमें उन क्षेत्रों की पहचान करने में मदद करता है जहाँ हम अपने संसाधनों का दुरुपयोग कर रहे हैं और उन्हें सुधार सकते हैं।

    6. बिना भविष्यवाणी:


    प्रेस्क्रिप्टिव एनालिटिक्स का मुख्य विभाग है कि यह हमें भविष्यवाणी करने के बजाय तत्ववर्गीकरण करने में मदद करता है।

    7. सरल जानकारी:


    यह डेटा को सरल और समझने में आसान तरीके से प्रस्तुत करता है जिससे हम बिना तकलीफ डेटा से सीख सकते हैं।

    8. बेहतर योजना तैयार करना:


    यह हमें बेहतर योजना बनाने में मदद करता है, क्योंकि हम डेटा के आधार पर निर्णय लेते हैं और योजना तैयार करते हैं।

    इस तरह से, प्रेस्क्रिप्टिव एनालिटिक्स हमें सहायता प्रदान करता है ताकि हम बेहतर निर्णय ले सकें, समस्याओं का समाधान कर सकें, और अपने कारोबार को सफलता की ओर बढ़ा सकें।

    Steps in Data Analytics in Hindi

    डेटा एनालिटिक्स की यात्रा आरंभ करने में थोड़ी घबराहट हो सकती है, लेकिन चिंता नहीं करे सोचिए जैसे हम एक स्वादिष्ट खाना बनाने के लिए रेसिपी के स्टेप्स का पालन करते हैं, वैसे ही डेटा एनालिटिक्स में भी कुछ आसान और शक्तिशाली कदम लेने होते हैं जो आपकी मदद कर सकते हैं डेटा के छिपे हुए रहस्यों की खोज में।

    लक्ष्य की परिभाषा:Define the Objective

    जैसे ही आप एक यात्रा शुरू करने से पहले एक लक्ष्य होता है, डेटा एनालिटिक्स भी आपके उद्देश्य की परिभाषा के साथ शुरू होता है। खुद से पूछें, “मुझे इस डेटा से क्या खोजना है?” यह आपके पूरे विश्लेषण के लिए दिशा निर्धारित करता है।

    डेटा संग्रहण:Data Collection:

    डेटा को एक पहेली के टुकड़ों को इकट्ठा करने की तरह इक्कठा करना जैसा होता है। यह स्प्रेडशीट्स, सर्वेक्षण या यहाँ तक कि सेंसर से भी हो सकता है। सुनिश्चित करें कि आपका डेटा साफ और व्यवस्थित है – इसे यहाँ तक कि उलझन से बचने के लिए पहेली के टुकड़ों को व्यवस्थीत करने के रूप में सोचें।

    डेटा की सफाई और तैयारी:Data Cleaning and Preparation

    जैसे की आप खाना पकाने से पहले सामग्री को छीलते हैं और काटते हैं,वैसे ही डेटा की सफाई और तैयारी की जरूरत होती है। डुप्लिकेट को हटाएं, त्रुटियों को ठीक करें, और सुनिश्चित करें कि संरूपता हो ताकि आपका विश्लेषण सटीक हो।

    डेटा विश्लेषण:Data Analysis

    यहाँ जादू होता है। डेटा की खोज के लिए उपकरण और तकनीकों का उपयोग करें, पैटर्न्स की पहचान करें, और दर्शन प्राप्त करें। इसे एक रहस्य को हल करने जैसा होता है जो हमें बिना डाटा एनालिटिक्स के नज़र नहीं आता है

    Data Analytics Job Profile

    क्या आपने कभी सोचा है कि डेटा से अद्भुत जानकारी कैसे प्राप्त की जा सकती है? डेटा एनालिटिक्स नौकरी के पीछे यही रहस्य होता है। यदि आपका दिल डेटा की दुनिया में है और आपको उससे जानकारी प्राप्त करने का शौक है, तो डेटा एनालिटिक्स में कैरियर एक उत्कृष्ट विकल्प हो सकता है।

    Role Overview (भूमिका अवलोकन):

    डेटा एनालिटिक्स में काम करने वाले व्यक्ति को डेटा से जानकारी प्राप्त करने और उसे समझने का काम करना होता है। यह जानकारी व्यक्तिगत, व्यावसायिक या सामाजिक प्रश्नों के समाधान में मदद कर सकती है।

    Key Responsibilities (मुख्य जिम्मेदारियां):

    डेटा संग्रहण और सफाई: डेटा एनालिटिक्स के एक महत्वपूर्ण पहलु है डेटा को संग्रहित करना और उसे साफ सुथरा करना।

    डेटा पूर्वानुमान:

    डेटा के पैटर्न और प्रावधानों के माध्यम से आगामी परिणामों की पूर्वानुमान करना।

    डेटा विश्लेषण:

    डेटा को विभिन्न तरीकों से विश्लेषण करके उसमें छिपी जानकारी की पहचान करना।

    डेटा विज़ुअलाइज़ेशन:

    डेटा को ग्राफिकल चित्रों और चार्ट्स के माध्यम से प्रस्तुत करना ताकि उसकी समझने में आसानी हो।

    उपयुक्त डेटा मॉडल चयन:

    उपयुक्त डेटा मॉडल और तकनीक का चयन करके डेटा की अधिकतम संभावित जानकारी प्राप्त करना।

    जानकारी की साझा करना:

    अपने विश्लेषण और फिन्डिंग्स को टीम के साथ साझा करके उन्हें सहायक निर्णय लेने में मदद करना।


    Conclusion (निष्कर्ष):

    डेटा एनालिटिक्स का काम डेटा से छिपी हुई महत्वपूर्ण जानकारी की पहचान करने में मदद करता है और संघर्षशीलता और समर्पण के साथ जानकारी का परिचय देता है।

    data cleaning and charting in python with pandas matplotlib and seaborn
    7Aug, 2023
    Python Data Cleaning & Charting with Matplotlib and Seaborn

    Master Python Data Cleaning and Charting with Matplotlib and Seaborn.In t

    his hands-on tutorial, we’ll guide you through the essential data cleaning techniques in Python. Learn how to effectively handle missing data, apply various strategies to fill in the gaps, and optimize your datasets for analysis.

    Next, unleash the power of data visualization using Matplotlib and Seaborn. Discover how to create captivating charts, interactive plots, and stunning visualizations to convey complex insights with ease.

    Whether you’re a beginner or an experienced data scientist, this video will equip you with the skills to clean and visualize your data like a pro. Join us on this exciting journey of Python data cleaning and charting and take your data analysis skills to the next level!”

    Step 1: Import Pandas

    After installing Pandas, you need to import it into your Python script or Jupyter Notebook before using it. Importing it as “pd” is a common convention: 
    import pandas as pd

    Click to download data set

    data set for data cleaning in python

    Step 3: Read Data

    Pandas can read data from various sources like CSV files, Excel files, databases, etc. For this example, let’s assume you have a CSV file named “data.csv” with your data: 
    import pandas as pd


df = pd.read_excel(r'C:\Users\yogesh\Desktop\excel files for practice\messeydataset.xlsx')

# Display the DataFrame.
print(df)

    Step 3: Explore the Data 

    print(df.columns)
    In Python Pandas, the df.columns attribute is used to retrieve the column labels (column names) of a DataFrame. It returns a Pandas Index object containing the column names of the DataFrame. 
    Index(['Name', 'Age', 'Gender', 'City', 'Salary'], dtype='object')

    Step 4 :Fill the missing values in the 'Name

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo. 
    df['Name'].fillna('Unknown', inplace=True)
    The line of code df[‘Name’].fillna(‘Unknown’, inplace=True) is used to fill the missing values in the ‘Name’ column of the DataFrame df with the value ‘Unknown’. 
    print(df.Name)
    Output 
    0        John
1        Alex
2         Bob
3        Mary
4        Alex
5     Unknown
6         Tim
7     Unknown
8       Alice
9         Sam
10       navi
11      Sarah
12       roja
13       Mike
14     Olivia
15    Unknown
16       maya
17        Zoe
18    Unknown
Name: Name, dtype: object

    Step 5: Filling age missing value

    mean_age = df['Age'].mean()
df['Age'].fillna(mean_age, inplace=True) 
print(df.Age)

    The lines of code mean_age = df[‘Age’].mean() and df[‘Age’].fillna(mean_age, inplace=True) are used to calculate the mean of the ‘Age’ column and then fill the missing values in the ‘Age’ column with this mean value, directly modifying the original DataFrame df.

    OUTPUT below

    0     30.000000
1     25.000000
2     29.071429
3     32.000000
4     28.000000
5     29.071429
6     29.000000
7     31.000000
8     27.000000
9     29.071429
10    35.000000
11    33.000000
12    29.071429
13    26.000000
14    24.000000
15    28.000000
16    29.071429
17    30.000000
18    29.000000
Name: Age, dtype: float64

    Step 6: Fill most frequent Gender

    mode_gender = df['Gender'].mode().values[0]

# Fill missing 'Gender' values with the mode (most frequent) gender
df['Gender'].fillna(mode_gender, inplace=True)

# Print the DataFrame to see the updated values
print(df)

    Here’s a step-by-step explanation of the code:

    • mode_gender = df[‘Gender’].mode().values[0]: This line calculates the mode (most frequent value) of the ‘Gender’ column and stores it in the variable mode_gender.
    • df[‘Gender’].fillna(mode_gender, inplace=True): This line fills the missing values in the ‘Gender’ column with the value stored in the mode_gender variable. The fillna method is used to replace the missing values with the specified value. The inplace=True argument ensures that the changes are applied directly to the DataFrame df.
    • print(df): Finally, this line prints the updated DataFrame that now has the missing ‘Gender’ values filled with the mode (most frequent) gender.
    • After executing this code, you should see the DataFrame with the missing ‘Gender’ values replaced by the mode value, and all missing values in the ‘Gender’ column should be filled.

    Output

        0        Male
1      Female
2        Male
3      Female
4      Female
5      Female
6        Male
7      Female
8      Female
9      Female
10       Male
11     Female
12     Female
13       Male
14     Female
15    Seattle
16       Male
17     Female
18     Female
Name: Gender, dtype: object

     

    Step 7: Change 'Seattle' in the 'Gender' column with 'Female'

    It looks like there is a small error in your code. Assuming df is a pandas DataFrame and you want to replace the value ‘Seattle’ with ‘Female’ in the ‘Gender’ column, the correct code would be: 
    df['Gender'].replace({'Seattle': 'Female'}, inplace=True)
print(df.Gender)
    Output 
    0       Male
1     Female
2       Male
3     Female
4     Female
5     Female
6       Male
7     Female
8     Female
9     Female
10      Male
11    Female
12    Female
13      Male
14    Female
15    Female
16      Male
17    Female
18    Female
Name: Gender, dtype: object

    Step 8: Fill missing 'City' values with the mode (most frequent)

    mode_city = df['City'].mode().values[0]

# Fill missing 'City' values with the mode (most frequent) city
df['City'].fillna(mode_city, inplace=True)

# Print the DataFrame to see the updated values
print(df.City)
    In this code snippet, it appears that the DataFrame df contains a column named ‘City’, and there are some missing values in that column. The goal is to fill the missing ‘City’ values with the mode (most frequent) city in the DataFrame.

    Here’s a step-by-step explanation of what the code does:

    1. mode_city = df[‘City’].mode().values[0]: This line calculates the mode (most frequent) value of the ‘City’ column in the DataFrame df using the .mode() function. The result is stored in the variable mode_city.
    2. df[‘City’].fillna(mode_city, inplace=True): This line fills the missing values in the ‘City’ column of the DataFrame with the value stored in mode_city. The fillna() function is used for this purpose, and inplace=True means the changes are made directly to the DataFrame df without creating a copy.
    3. print(df.City): This line prints the ‘City’ column of the DataFrame df after filling the missing values with the mode city. The updated values will be displayed on the screen.
    As a result, the ‘City’ column will have no missing values, as they have been replaced with the mode city. OUTPUT 
    0           LA
1     San Fran
2           LA
3      Chicago
4      Houston
5      Chicago
6           LA
7      Houston
8       Boston
9        Miami
10       Miami
11     Houston
12    new york
13     Houston
14     Houston
15     Houston
16     Houston
17      Dallas
18      Dallas
Name: City, dtype: object
     

    Step 9: Fill missing 'Salary' values with the mean salary

    mean_salary = df['Salary'].mean()

# Fill missing 'Salary' values with the mean salary
df['Salary'].fillna(mean_salary, inplace=True)

# Print the DataFrame to see the updated values
print(df.Salary)
    Output 
    0     50000.0
1     60000.0
2     55000.0
3     50375.0
4     52000.0
5     45000.0
6     50375.0
7     48000.0
8     52000.0
9     42000.0
10    49000.0
11    51000.0
12    54000.0
13    47000.0
14    50375.0
15    52000.0
16    49000.0
17    51000.0
18    49000.0
Name: Salary, dtype: float64

    Step 10: Bar chart of Gender distribution:

    #Bar chart of Gender distribution:
import matplotlib.pyplot as plt
import seaborn as sns
sns.countplot(x='Gender', data=df)
plt.title('Gender Distribution')
plt.show()

    Here’s a step-by-step explanation of what the code does:

    1. import matplotlib.pyplot as plt: This line imports the pyplot module from the matplotlib library, which allows you to create visualizations like plots and charts.
    2. import seaborn as sns: This line imports the seaborn library, which is a powerful data visualization library built on top of matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
    3. sns.countplot(x=’Gender’, data=df): This line creates a count plot using seaborn’s countplot function. The x parameter specifies that the ‘Gender’ column should be represented on the x-axis, and the data parameter specifies the DataFrame from which the data will be taken.
    4. plt.title(‘Gender Distribution’): This line sets the title of the plot to ‘Gender Distribution’.
    5. plt.show(): This line displays the plot.

    Make sure that you have the correct data in the ‘Gender’ column of the DataFrame ‘df’ before running this code. If everything is set up correctly, this code will generate a bar chart showing the distribution of genders in your dataset.

    bar chart in distribution

    Step 11:Bar chart of Salary distribution:

    plt.hist(df['Salary'], bins=10, edgecolor='black')
plt.xlabel('Salary')
plt.ylabel('Count')
plt.title('Salary Distribution')
plt.show()

    Here’s a step-by-step explanation of the code:

    1. plt.hist(df[‘Salary’], bins=10, edgecolor=’black’): This line creates the histogram using the ‘Salary’ column from the DataFrame ‘df’. The ‘bins=10’ parameter specifies that the data will be divided into 10 equally spaced bins. The ‘edgecolor=’black” parameter sets the color of the edges of the bars in the histogram to black.
    2. plt.xlabel(‘Salary’): This line sets the label for the x-axis as ‘Salary’.
    3. plt.ylabel(‘Count’): This line sets the label for the y-axis as ‘Count’, representing the number of occurrences in each bin.
    4. plt.title(‘Salary Distribution’): This line sets the title of the plot to ‘Salary Distribution’.
    5. `plt.show:This line displays the histogram plot on the screen.

    Step 12:Bar chart of Age distribution:

    plt.hist(df['Age'], bins=10, edgecolor='black')
plt.xlabel('Age')
plt.ylabel('Count')
plt.title('Age Distribution')
plt.show()

    Here’s a breakdown of the code:

    1. plt.hist(df[‘Age’], bins=10, edgecolor=’black’): This line creates the histogram. It takes the ‘Age’ column from the DataFrame ‘df’ and plots it using the plt.hist function from the matplotlib library. The ‘bins’ parameter is set to 10, which means the histogram will have 10 bars, representing different age ranges. The ‘edgecolor’ parameter sets the color of the edges of the bars to black.
    2. plt.xlabel(‘Age’): This line sets the label for the x-axis to ‘Age’.
    3. plt.ylabel(‘Count’): This line sets the label for the y-axis to ‘Count’, representing the frequency of each age group.
    4. plt.title(‘Age Distribution’): This line sets the title of the histogram to ‘Age Distribution’.
    5. plt.show(): This line displays the histogram plot
    bar chart in python to show age distribution.

    Step 13:Pie chart of City distribution 

    city_counts = df['City'].value_counts()
plt.pie(city_counts, labels=city_counts.index, autopct='%1.1f%%', startangle=90)
plt.title('City Distribution')
plt.axis('equal')
plt.show()
    1. city_counts = df[‘City’].value_counts(): This line calculates the count of occurrences of each unique city in the ‘City’ column of the DataFrame df. The result is stored in the city_counts variable, which is a Pandas Series containing city names as index and their corresponding counts as values.
    2. plt.pie(city_counts, labels=city_counts.index, autopct=’%1.1f%%’, startangle=90): This line creates the pie chart. The plt.pie() function takes several parameters:
    3. city_counts: The data for the pie chart, which is the city_counts Series containing city counts.
    4. labels: The labels for each pie slice, which are the city names (obtained from city_counts.index).
    5. autopct=’%1.1f%%’: This parameter formats the percentage values displayed on each pie slice. ‘%1.1f%%’ formats the percentage with one decimal place.
    6. startangle=90: This parameter sets the starting angle for the first slice in degrees. In this case, it’s set to 90 degrees, which means the first slice will start from the 12 o’clock position.
    7. plt.title(‘City Distribution’): This line sets the title for the pie chart.
    8. plt.axis(‘equal’): This line ensures that the pie chart is displayed as a circle (i.e., an equal aspect ratio).
    9. plt.show(): This line displays the pie chart.

    FAQ

    Data cleaning, also known as data preprocessing, is the process of identifying and correcting errors or inconsistencies in datasets. It is crucial in data analysis because clean data ensures the accuracy and reliability of your analysis results.

    Common data cleaning tasks in Python include handling missing values, removing duplicates, converting data types, and dealing with outliers.

    You can handle missing values by using methods like dropna() to remove rows with missing values, fillna() to fill missing values with specific values, or interpolate() to fill missing values with interpolated values.

    Outliers can significantly affect statistical analysis and visualization. Removing or transforming outliers can help in producing more accurate and meaningful insights from the data.

     

    Some popular Python libraries for data cleaning are Pandas and NumPy. Pandas provides powerful tools for data manipulation, while NumPy is useful for numerical operations.

     

    Matplotlib and Seaborn are Python libraries used for data visualization. Matplotlib is a versatile library for creating various types of plots, while Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive statistical graphics.

     

    With Matplotlib, you can create line plots, bar plots, scatter plots, histograms, and more. Seaborn specializes in statistical visualization and offers functions for creating heatmaps, pair plots, and distribution plots.

     

    You can customize plots by modifying parameters such as color, style, labels, and titles. Matplotlib and Seaborn provide extensive customization options to tailor the visualizations to your specific needs.

     

    Unlocking the Hidden Mysteries of Data with Seaborn’s Visual Storytelling
    Reasons why skills are becoming more important in 2023
    8Jun, 2023
    Top Data Analytics beginner Jobs An overview 2023

    You want to work as a data analyst, then. You’ve done extensive study and come to the conclusion that it’s the right career for you. Alternatively, you may keep hearing about this exciting job title and want to find out more.

    In either case, you want to fully understand what working as a data analyst involves and, even more important, how to go about doing it.

    You may be interested in learning more about the market for new and aspiring data analysts in 2023 after experiencing a global epidemic over the past two years.

    Do employers still need data analysts?

    You’re in the correct place, I suppose. Consider this article to be your comprehensive guide to becoming a data analyst.

    What will the data analytics market look like in 2023?

    Is it a wise career decision to become a data analyst, if that’s what you’re considering? Even without a global epidemic, that is a challenging question to answer. You’re right to be inquisitive (and possibly anxious) about what 2023 has in store because for many, the previous two years have been fairly awful.

    So, what can you anticipate for the coming year? Let’s look at it.

     

    What will the data analytics market look like in 2023?

    Consider the larger context in which you’ll be working while thinking about a job as a data analyst. Every day, as people, we produce enormous amounts of data that are attractive to businesses and organisations because it provides them with information about how we behave in respect to their goods and services. Data generation increases as we rely more on digital tools and services, making it crucial for businesses to understand this data.

    The big data market is expected to be valued $229.4 billion USD by 2025, to give you an indication of its size. How does that affect you? Well, as the demand for data analysts rises dramatically, so does the market for data.

    This idea is supported by the Jobs of Tomorrow study from the World Economic Forum (released in 2020), which lists data science and artificial intelligence as two of the seven high-growth emerging professions. The largest growth rate of the seven recognised vocations, at 41% annually, is shown by data and AI. Currently, there aren’t enough data specialists to fulfil this need; a recent research found that companies anticipate data science and analytics to be one of the hardest fields to fill.

    Does remote work impact careers for data analysts?

    Without taking into account the recent past, it is impossible to discuss the data industry in 2023. The data sector has also seen significant transformation as a result of the pandemic.

    The rise of remote work, which has expanded by 300% since the epidemic, is one of the biggest and most evident changes. If the epidemic persists in 2023, you should plan on working remotely at least occasionally if not full-time as a data analyst. You’ll be in a better position to convince companies that you can operate independently and remotely if you’re learning data analytics through an online programme.

    How can a newly certified analyst like you stand out?

    Continuously Enhance Your Skills


    Keep up with the newest data analytics tools, technologies, and approaches. Through online courses, workshops, certifications, and attendance at industry conferences, always learn new things and improve your abilities. Develop your skills in areas like SQL, machine learning, statistical analysis, Python, R, and other computer languages.


    Showcase Your Technical Abilities:


    Showcase your expertise with industry-standard data analytics tools and platforms, such as Tableau, Power BI, or Python packages like Pandas and NumPy. In interviews and on your CV, be sure to emphasise your technical expertise. You should also include examples of projects or analyses where you used your technical expertise well.


    Emphasize Domain Knowledge


    Gain a thorough understanding of the field or sector you are working in. Whether you work in marketing, finance, healthcare, or another industry, having domain-specific expertise can enable you to offer more insightful analysis and suggestions. Keep up with the latest market developments, difficulties, and rules.


    Develop Strong Communication Skills


    As an analyst, it’s essential to be able to express your conclusions and observations clearly. Build your writing and spoken communication abilities to express complicated ideas succinctly and clearly. To effectively communicate your insight to stakeholders, practise developing visually appealing and informative data visualisations and presentations.


    Be a Problem Solve


    Consider how you can solve problems while you go about your work. Recognise the goals and issues facing the company, and concentrate on using data analysis to offer workable answers. Seek out opportunities to proactively identify and address problems or areas that need development, and show how your analytical talents can benefit the organisation.


    Collaborate and Network


    Engage in active collaboration with colleagues to gain knowledge from their viewpoints and experiences, both inside and outside of your team. Making connections with industry experts can open up new doors and opportunities. Expand your professional network by attending industry events, signing up for associations, taking part in online forums, and having talks.

    Use your status as a “newcomer” to your advantage. Even while the job market may be particularly competitive right now, you as a novice to the industry have many distinct insights and advantages. In your applications, be sure to draw attention to these. You can get assistance with this from a career coach, so pick a course that provides it. Here, you may do top data analytics certification courses.

    What kind of salary can data analysts in India expect in 2023?

    Data analysts’ salaries in India can vary depending on their level of expertise, region, industry, and size of their employer. However, based on past data and patterns, I can provide you some broad views on the predicted compensation range for data analysts in India. Note that these numbers are approximations and subject to change:

    Entry-level Data

    Analyst: For those with less than 2 years of experience, the salary range can vary from around INR 3.5 lakh to INR 6 lakh per annum.

    Mid-level Data Analyst:

    With 2 to 5 years of experience, data analysts can expect a salary range of approximately INR 6 lakh to INR 12 lakh per annum.

    Senior Data Analyst:

    Professionals with 5 or more years of experience may earn a salary ranging from INR 12 lakh to INR 20 lakh per annum or higher, depending on their expertise and responsibilities.

    It’s important to note that these figures are approximate and can vary significantly based on factors such as the candidate’s qualifications, skills, industry demand, company size, and location. Salaries tend to be higher in metropolitan cities like Mumbai, Bangalore, and Delhi compared to smaller cities or rural areas.

    Additionally, it’s worth mentioning that the salary ranges provided here are for data analysts specifically. Data scientists or professionals with more advanced skills and experience in areas such as machine learning and AI may command higher salaries.

    To get the most accurate and up-to-date salary information, it is advisable to refer to industry surveys, job portals, and consult with professionals working in the field or a human resources department.

    2. What does a data analyst do?

    SQL COMMAND FOR MASTERING DATA ANALYTICS
    26May, 2023
    TOP 10 SQL COMMAND FOR MASTERING DATA ANALYTICS

    This blog shows the importance of data analytics and the prospect of improving analytical abilities by using the provided SQL commands.

    The core of data analytics is SQL, which enables experts to mine huge amounts of data for insightful information. The top 10 SQL commands for data analytics provide a potent toolkit that gives analysts the ability to precisely manipulate, filter, sort, aggregate, and compute data. While WHERE filters data based on specific criteria, SELECT permits the retrieval of specific columns or full datasets. Data can be sorted in ascending or descending order with the help of ORDER BY. Data can be grouped for aggregations using GROUP BY, and data from several tables can be combined using JOIN.

    DISTINCT finds singular values, COUNT calculates the number of rows or non-null values, and HAVING filters grouped data. The highest and lowest values are extracted, respectively, by MAX and MIN. Data analysts may maximize the value of their datasets, find significant trends, make wise decisions, and promote company success by understanding these top 10 SQL methods.

    SELECT:

    Data from a database table is retrieved using the SELECT query. You can choose which columns to retrieve or pick all columns by using the asterisk (*). SELECT * FROM sales in the example fetches every column and row from the “sales” table.

    WHERE

    Rows can be filtered using the WHERE clause based to certain criteria. You can define a requirement that must be satisfied in order for the rows to be included in the result set. In the given example, the statement SELECT * FROM sales WHERE category = ‘Electronics’ only returns rows with the word ‘Electronics’ in the “category” column.

    ORDER BY:

    The result set is sorted using the ORDER BY clause according to one or more columns, either ascending or descending. It always sorts in ascending order by default. In the above example, the statement SELECT * FROM sales ORDER BY price DESC arranges the result set according to the “price” column in decreasing order.

    GROUP BY

    Rows can be grouped based on one or more columns using the GROUP BY clause. Calculations on each group usually involve using it in combination with aggregate functions. In the given example, grouping the rows by the “category” column and computing the total quantity for each category is done by using the statements “SELECT category, SUM(quantity) as total_quantity FROM sales GROUP BY category.”

    JOIN

    In order to merge rows from two or more tables based on a shared column, use the JOIN clause. You can use it to get information from numerous tables with only one query. The statement JOIN categories c ON s.category = c.category_id joins the tables “sales” and “categories” based on the “category” and “category_id” columns in “sales” and “categories,” respectively.

    HAVING

    Groups produced by the GROUP BY clause are filtered using the HAVING clause. You can define a prerequisite that a group must meet in order to be included in the result set. According to the example, the groups are filtered such that only those with an average price higher than 100 are included.

    DISTINCT:

    To get distinct values from a column, use the DISTINCT keyword. Duplicate values are removed from the result set. In the given example, the statement SELECT DISTINCT category FROM sales finds unique values from the column labeled “category”.

    COUNT

    Count the number of rows or non-null values in a column using the COUNT function. To determine counts per group, it is frequently used in conjunction with the GROUP BY clause. In the given example, the number of rows for each category is counted using the SELECT category, COUNT(*) as count FROM sales GROUP BY category.

    MAX

    The MAX function is used to get a column’s highest value. It is frequently used with date-and-time or numerical columns. In the given example, selecting the maximum price from the “price” column using the command SELECT MAX(price) as max_price FROM sales.

    MIN:

    The MIN function is used to get a column’s lowest value. It is frequently used with date-and-time or numerical columns. The example finds the lowest price from the “price” column by using the statement SELECT MIN(price) as min_price FROM sales.

    Create a table and insert data

    -- Create the table
CREATE TABLE Books (
    BookID INT PRIMARY KEY,
    Title VARCHAR(255),
    Author VARCHAR(255),
    PublicationYear INT,
    Genre VARCHAR(50)
);

-- Insert data into the table
INSERT INTO Books (BookID, Title, Author, PublicationYear, Genre)
VALUES
    (1, 'Example Book 1', 'Author A', 2020, 'Fiction'),
    (2, 'Example Book 2', 'Author B', 2018, 'Mystery'),
    (3, 'Example Book 3', 'Author A', 2022, 'Science Fiction'),
    -- ... (more data)
    (10, 'Example Book 10', 'Author C', 2015, 'Thriller');

    ORDER BY

    SELECT * FROM Books ORDER BY PublicationYear DESC LIMIT 10;

    COUNT:

    SELECT Genre, COUNT(*) AS GenreCount FROM Books GROUP BY Genre;

    SUM

    SELECT Author, SUM(PublicationYear) AS TotalPublicationYears FROM Books GROUP BY Author;

    AVG:

    SELECT AVG(PublicationYear) AS AvgPublicationYear FROM Books;

    Min and Max

    SELECT MAX(PublicationYear) AS MaxPublicationYear, MIN(PublicationYear) AS MinPublicationYear FROM Books;

    Group by

    SELECT Genre, AVG(PublicationYear) AS AvgPublicationYear FROM Books GROUP BY Genre;

    DISTINCT

    SELECT DISTINCT Author FROM Books;

    LIKE

    SELECT * FROM Books WHERE Title LIKE '%Example%';

    Having

    SELECT Genre, AVG(PublicationYear) AS AvgPublicationYear FROM Books GROUP BY Genre HAVING AVG(PublicationYear) > 2000;
    Top Job Duties and Qualifications for a Data Analyst in Hindi readers
    21May, 2022
    Top Job Duties and Qualifications for a Data Analyst in Hindi

    What is data Analytics hindi

    डेटा एनालिटिक्स व्यक्तियों और संगठनों को डेटा की समझ बनाने में मदद करता है। डेटा विश्लेषक आमतौर पर अंतर्दृष्टि और रुझानों के लिए कच्चे डेटा का विश्लेषण करते हैं। वे विभिन्न टेक्नोलॉजी का उपयोग करते हैं

    व्यक्तियों और व्यवसायों को डेटा विश्लेषण से लाभ हो सकता है। डेटा विश्लेषक कच्चे डेटा में पैटर्न और अंतर्दृष्टि की तलाश करते हैं। वे तरह-तरह के टूल्स का इस्तेमाल करते हैं।

    The future of data analytics in Hindi

     

    स्वास्थ्य सेवा उद्योग पर केंद्रित मैकिन्से की एक रिपोर्ट के अनुसार, “अगले पांच वर्षों में डेटा विश्लेषण और सूचना सेवाओं की विकास दर सबसे तेज 16% से 18% होगी, जबकि मुख्य प्रशासनिक सेवाओं में ऑटोमेशन automation के कारण बहुत अधिक वृद्धि देखने की संभावना नहीं है।” अपनी कंपनी की भविष्य की पहल की योजना बनाते समय आपको यह जानने की आवश्यकता है।

    डेटा विशेषज्ञों की कमी समस्या ।

    Shortages of data specialists will pose problems.

    आज के उद्योग में योग्य डेटा विश्लेषकों और डेटा वैज्ञानिकों की कमी दिखाई दे रही है, जो निकट भविष्य में बिगड़ने का अनुमान है। इसे हल करने के लिए अभी से योजनाएँ बनाना शुरू करें, चाहे वह आपकी कंपनी की बाज़ार प्रतिस्पर्धात्मकता को बढ़ावा देने के लिए अद्वितीय प्रोत्साहन की पेशकश करके हो या आंतरिक संभावनाओं की पहचान करने और उनके प्रशिक्षण का भुगतान करने के लिए एक कार्यक्रम स्थापित करके। अभी से शुरू करो।

    Wider use by business

    व्यवसाय द्वारा व्यापक रूप से अपनाना

    बीआई और एनालिटिक्स टूल उपयोगिता और बढ़ती नेचुरल  भाषा पर ध्यान केंद्रित करना जारी रखेंगे जो व्यापार उपयोगकर्ताओं को डेटा निकालने और  एल्गोरिदम को समझने की आवश्यकता के बिना रिपोर्ट बनाने में सक्षम बनाता है। यह न केवल दक्षता में वृद्धि करेगा और कंपनियों में और अधिक अपनाने का निर्माण करेगा, बल्कि यह डेटा वैज्ञानिक की कमी से उत्पन्न कुछ समस्याओं को कम करने में भी मदद करेगा।

    बड़े डेटा नेटवर्क अधिक महत्वपूर्ण होते जा रहे हैं।

    Large Data Networks are becoming more important.

    बड़े डेटा रिपॉजिटरी तक पहुंच, जिसे उन्नत डेटा नेटवर्क के रूप में भी जाना जाता है, व्यवसायों के लिए तेजी से मूल्यवान हो जाएगा। इसमें निहित उपभोक्ता डेटा की विशाल मात्रा का उपयोग कंपनी के मौजूदा ग्राहक डेटा को बढ़ाने के लिए किया जा सकता है, जिससे वे अधिक अनुरूप सेवाएं प्रदान कर सकते हैं और संभावित रूप से अधूरी इच्छाओं और आकांक्षाओं को पूरा करने के लिए नई सेवाएं स्थापित कर सकते हैं।

    मशीन लर्निंग तेजी से बढ़ेगी।

    Machine learning will grow as faster rate

    machine learning

    मशीन लर्निंग और आर्टिफिशियल इंटेलिजेंस (एआई) असीमित संभावनाएं प्रदान करते हैं, और व्यवसाय अपनी शक्ति का उपयोग करने और नई सेवाओं को विकसित करने के लिए प्रतिस्पर्धा करेंगे जो नए तरीकों से मूल्य प्रदान करते हैं। कई उद्योग विशेषज्ञों के अनुसार, मशीन लर्निंग जल्द ही अधिकांश ग्राहक सेवा व्यवसायों को अपने कब्जे में ले लेगा।

    सफलता के लिए इंटरकनेक्टिविटी तेजी से महत्वपूर्ण होती जा रही है।

    डेटा विश्लेषण और बीआई के लिए नए आंतरिक उपकरणों पर बढ़ती निर्भरता के साथ-साथ बाहरी डेटा रिपॉजिटरी, नेटवर्क और IoT उपकरणों तक पहुंचने की बढ़ती आवश्यकता को देखते हुए, इंटरकनेक्टिविटी आपके व्यवसाय के लिए एक सुसंगत डेटा एनालिटिक्स मशीन विकसित करने की कुंजी होगी।

    अगले वर्षों में प्रतिस्पर्धी बने रहने के लिए, एक प्रतिभा अधिग्रहण विकसित करना और रणनीतिक निवेश के लिए पहले से बजट बनाना आवश्यक होगा। सभी प्लेटफार्मों पर स्वच्छ डेटा को सिक्योर करने के लिए प्रक्रिया तकनीक विकसित करने की आवश्यकता भी महत्वपूर्ण होगी।

    यदि आप ऊपर दिए गए किसी भी विषय के बारे में अधिक जानना चाहते हैं या डाटा एनालिटिक्स के टूल्स को सीखना कहते है तो हमसे संपर्क करे

    Job Description for a Data Analyst: Roles and Responsibilities

    Data Analytics jobs

    डेटा विश्लेषक के लिए नौकरी का विवरण: भूमिकाएं और जिम्मेदारियां

    • स्वचालित तकनीकों का उपयोग करके प्राइमरी और सेकेंडरी स्रोतों से डेटा निष्कर्षण
      क्षतिग्रस्त डेटा से छुटकारा पाना और कोडिंग दोषों और अन्य प्रॉब्लम्स की मरम्मत करना।
    • डेटाबेस और डेटा सिस्टम बनाना और मैनेज करना, साथ ही डेटा को एक प्रयोग योग्य प्रारूप में पुनर्व्यवस्थित करना
      इसकी गुणवत्ता और अर्थ निर्धारित करने के लिए डेटा का विश्लेषण करना
    • कोड गलतियों को खोजने और सुधारने के लिए, रिपोर्ट और प्रदर्शन मीट्रिक का मूल्यांकन करके डेटा फ़िल्टर करना ।
    • बड़े डेटा सेट में पैटर्न और रुझानों को खोजने, विश्लेषण करने और व्याख्या करने के लिए सांख्यिकीय टूल्स का उपयोग करना जो निदान और भविष्यवाणी में सहायता कर सकते हैं
    • महत्वपूर्ण व्यावसायिक कार्यों को एक संख्यात्मक मान देना ताकि व्यवसाय के प्रदर्शन को मापा जा सके और समय के साथ तुलना की जा सके।
    • कंपनी और उद्योग को प्रभावित करने वाले स्थानीय, राष्ट्रीय और अंतर्राष्ट्रीय रुझानों का विश्लेषण करना
    • प्रबंधन रिपोर्ट बनाना जिसमें प्रवृत्तियों, पैटर्नों और पूर्वानुमानों का उपयोग करना शामिल है

    Essential Skills for Data Analysts

    statistics for data analyticsSQL

    उद्योग-मानक डेटाबेस भाषा है और डेटा विश्लेषकों के पास सबसे महत्वपूर्ण क्षमता हो सकती है। भाषा को अक्सर एक्सेल के “स्नातक” संस्करण के रूप में संदर्भित किया जाता है क्योंकि यह विशाल डेटासेट को संभाल सकता है जो एक्सेल नहीं कर सकता।

    लगभग हर कंपनी को किसी ऐसे व्यक्ति की आवश्यकता होती है जो SQL को जानता हो, चाहे वह डेटा को प्रबंधित और संग्रहीत करना हो, विभिन्न डेटाबेस कनेक्ट करना हो (जैसे कि अमेज़ॅन आपके द्वारा पसंद की जाने वाली चीज़ों की अनुशंसा करने के लिए उपयोग करता है), या डेटाबेस आर्किटेक्चर को पूरी तरह से बनाएं या अपडेट करें। SQL योग्यताओं की आवश्यकता वाले हज़ारों जॉब पोस्ट हर महीने बनाए जाते हैं।

    Excel एक्सेल (माइक्रोसॉफ्ट)

    जब आप स्प्रेडशीट के बारे में सोचते हैं तो एक्सेल शायद पहली चीज है जो दिमाग में आती है, लेकिन इसमें हुड के पीछे बहुत अधिक विश्लेषण क्षमता है। जबकि आर या पायथन जैसी प्रोग्रामिंग भाषा विशाल डेटा सेट से निपटने के लिए बेहतर अनुकूल है, उन्नत एक्सेल दृष्टिकोण जैसे मैक्रोज़ बनाना और वीबीए लुकअप को नियोजित करना अभी भी छोटे लिफ्टों और त्वरित विश्लेषण के लिए बड़े पैमाने पर उपयोग किया जाता है। यदि आप किसी छोटे व्यवसाय या स्टार्टअप के लिए काम करते हैं, तो आपके डेटाबेस का पहला संस्करण एक्सेल हो सकता है। यह उपकरण पिछले कुछ वर्षों में हर उद्योग में फर्मों के लिए एक मुख्य आधार रहा है, इसलिए इसमें महारत हासिल करना आवश्यक है।

    गंभीर रूप से सोचना critical thinking

    critical thinking data analytics

    अपने प्रश्नों के उत्तर खोजने के लिए डेटा का उपयोग करने के लिए पहले यह निर्धारित करना आवश्यक है कि कौन से प्रश्न पूछने हैं, जो कठिन हो सकते हैं। एक विश्लेषक के रूप में सफल होने के लिए, आपको एक जैसा सोचना चाहिए। एक डेटा विश्लेषक का काम उन रिश्तों को खोजना और संश्लेषित करना है जो हमेशा स्पष्ट नहीं होते हैं। हालांकि यह प्रतिभा आंशिक रूप से जन्मजात है, लेकिन कुछ रणनीतियाँ हैं जिनका उपयोग आप अपनी महत्वपूर्ण सोच क्षमताओं को बढ़ाने के लिए कर सकते हैं। उदाहरण के लिए, आवश्यकता से अधिक परिष्कृत स्पष्टीकरण के साथ बह जाने के बजाय, अपने आप से समस्या के बारे में मौलिक प्रश्न पूछने से आपको समाधान की तलाश में जमीन पर बने रहने में मदद मिल सकती है।

    R or Pythonमें सांख्यिकीय प्रोग्रामिंग

     

    R या Python वह कर सकता है जो Excel कर सकता है—और 10 गुना तेज। R और Python, SQL की तरह, वह प्रबंधित कर सकते हैं जो Excel नहीं कर सकता। वे बड़े डेटा सेट पर उन्नत विश्लेषण और भविष्य कहनेवाला विश्लेषण करने के लिए उन्नत सांख्यिकीय प्रोग्रामिंग भाषाएं हैं। वे दोनों उद्योग मानक भी हैं। डेटा विश्लेषक के रूप में प्रभावी ढंग से काम करने के लिए, आपको SQL के अलावा इनमें से कम से कम एक भाषा जानने की आवश्यकता होगी।

    डेटा का विज़ुअलाइज़ेशन data visualization

    अपने संदेश को प्रसारित करने और अपने दर्शकों को जोड़े रखने के लिए, आपको तथ्यों के साथ एक मनोरम कहानी बनाने में सक्षम होने की आवश्यकता है। यदि आपके निष्कर्षों को सरलता से और तुरंत पहचाना नहीं जा सकता है, तो आपको अपना संदेश दूसरों तक पहुँचाने में कठिनाई होगी। परिणामस्वरूप, जब आपके डेटा के प्रभाव की बात आती है, तो डेटा विज़ुअलाइज़ेशन इसे बना या बिगाड़ सकता है। विश्लेषक आकर्षक, उच्च-गुणवत्ता वाले चार्ट और ग्राफ़ का उपयोग करके अपने निष्कर्षों को स्पष्ट और संक्षिप्त तरीके से संप्रेषित करते हैं।

    डेटा एनालिटिक्स में शीर्ष 5 सॉफ्ट स्किल्स

    डेटा एनालिटिक्स में शीर्ष 5 सॉफ्ट स्किल्स को हिंदी में निम्नलिखित रूप में प्रस्तुत किया गया है:

    समस्या समाधान कौशल Problem Solving skills:

    डेटा एनालिटिक्स में अच्छे प्रोफेशनल्स को समस्याओं को समझने और उन्हें समाधान करने की क्षमता होनी चाहिए। वे डेटा को विश्लेषण करके समस्याओं के साथ संबंधित नए तरीके खोजने में सक्षम होते हैं।

    टीम वर्क Team work:

    एक अच्छा डेटा एनालिटिक्स पेशेवर किसी भी टीम के साथ सहयोग करने का योग्य होता है। वे अन्य सदस्यों के साथ मिलकर काम करते हैं, जानकारी साझा करते हैं और एक साथ समस्याओं का समाधान करते हैं।

    टाइम मैनेजमेंट Time Management:

    डेटा एनालिटिक्स में काम करते समय, प्रोफेशनल्स को अपने कार्य को समय पर पूरा करने के लिए कुशल होना चाहिए। वे काम को प्राथमिकता देने और समय के प्रबंधन में सक्षम होते हैं।

    संवेदनशीलता: डेटा एनालिटिक्स में काम करते समय, पेशेवर्षिता को दूसरों के विचारों को समझने और मानने की क्षमता होनी चाहिए। वे अन्य लोगों की बातों को सुनते हैं और उनके दृष्टिकोण को समझते हैं।

    संवेदनशीलता: एक अच्छा डेटा एनालिटिक्स पेशेवर किसी भी स्थिति में धैर्य और संवेदनशीलता बनाए रखने की क्षमता होती है। जब वे नई समस्याओं का सामना करते हैं, तो वे सक्रिय रूप से खोज करते हैं और सही समाधान प्रस्तावित करते हैं।

    Skill Development

    Data Analytics skill development

    यह जानना कि आपको एनालिटिक्स में किस कौशल की आवश्यकता होगी और डेटा के साथ काम करना शुरू करना आपके डेटा एनालिटिक्स करियर की उन्नति के लिए महत्वपूर्ण है। बिग डेटा व्यवसाय में एक गर्म विषय है, और कंपनियां ऐसे लोगों की तलाश कर रही हैं जिनके पास इन-डिमांड, हार्ड-टू-फाइंड क्षमताएं हैं। अपने डेटा विश्लेषिकी ज्ञान में सुधार करने से आपको भविष्य में अतिरिक्त अवसर—और अधिक धन— प्रदान होंगे।

    यदि आप एनालिटिक्स पर स्विच करने के बारे में गंभीर हैं, तो इन सात प्रतिभाओं को निखारने के कई तरीके हैं। इन कौशलों को कैसे सुधारें इस पर आपका अंतिम निर्णय आपके पूर्व अनुभव, आपके पास उपलब्ध समय और संसाधनों और आपके अपने उद्देश्यों पर आधारित होगा।

    अधिक जानकारी के लिए आप हमें विस्टा अकादमी में संपर्क कर सकते हैं

    How to build a perfect resume for entry level data analyst?
    DATA SCIENCE FUTURE JOBS
    20Apr, 2022
    What will the future of Data Science jobs look like?

    In the future, data science will be one of the most in-demand professions all around the world. In fact, every industry vertical need a data scientist who can collect, analyse, and analyse raw data in order to draw conclusions and make business decisions.

    In the future, data science will be an ever-evolving job field that will require applicants to have master’s level knowledge in programming languages, machine learning, data science abilities, and business understanding to stay competitive.

    When you have a master’s degree, for example, you can receive a variety of higher-paying positions, such as:

    • Data Analyst
    • Engineer in Machine Learning and Data Analysis
    • Engineer for Deep Learning
    • Data Engineer 

    According to Statista, about 50 billion IoT devices will be in use around the world by 2030, with each device producing 90 percent of the world’s data in just a few years.

    Data analysts can use this information to gather helpful business information and assist businesses in growing. Similarly, similar developments entice today’s millennials and Gen Z to pursue a career in data science. Let’s look at 5 more Data Science trends.

     

    There are numerous applications for data science.

    Above all, everyone uses data science. Data Science is used not only in B2B or B2C industries like retail, manufacturing, and financial services, but also in small enterprises like farming and non-profits.

    Finally, this technological competence contributes to the improvement of operations, revenue growth, and business development across a wide range of industries, from healthcare to FMCG.

    Data analytics offers a wide range of job options.

    Businesses all throughout the world can benefit greatly from the information gathered. Data scientists create novel algorithms, while data engineers provide the underlying infrastructures needed to exploit data, as we all know. A Data Analyst, on the other hand, uses such algorithms to assist business leaders in making informed decisions that help their companies develop.

    Specializing in Data Analytics can help you advance in your profession.

    A working professional can specialise in Data Analytics to improve his or her production. That is to say, this information will assist them in addressing real-world issues and improving the quality of their work. We can deliver corporate insights, construct systematic models, conduct various research and analytics operations, and generate intelligent reports using Data Analytics tools.

    Data can be used for storytelling

    Anything that requires an analytical explanation, whether it’s a business presentation or an article. Importantly, statistics can be used to tell your story in a variety of ways. Data is a type of visual information that can be presented in the form of graphs or designs. Second, as the world has grown more data-driven, it is critical to make your data more readable or visually appealing. On the contrary, having a data-driven storytelling approach is essential.

    Data literacy’ is equally crucial

    Data literacy is the ability to read, work with, analyze and communicate with data. It’s a skill that empowers all levels of workers to ask the right questions of data and machines, build knowledge, make decisions, and communicate meaning to others.

    Degrees and qualifications for data scientists

    Many of the best data scientists have advanced degrees in math or statistics and are problem-solving experts. Others have studied computer science, astrophysics, or a variety of other areas.

    “Do I feel that data scientists are required to have such degrees? No. Certainly not, “Kathleen Featheringham, director of AI strategy and training at Booz Allen Hamilton, a management and IT technology consulting firm, agreed. “There have been many classifications, but it’s basically someone who is curious,” says the author.

    A data scientist, like any other job, may grow into something different, and there are a few signs that this will happen.

    Data Science Job in Future

    You can think about the data increase from IoT or from social data at the edge. If we look a little bit more ahead, the US Bureau of Labor Statistics predicts that by 2026—so around six years from now—there will be 11.5 million jobs in data science and analytics.

    BEST DATA SCIENCE COURSE IN UTTARAKHAND

    Thanks, I’m not interested