Data Science and Analytics specialization course in Jaipur

• Machine learning applications
• ML vs DL

• Basics of Python [Syntax]
• Working with Pandas, Numpy & Matplotlib
  ■ Working with Missing Data
  ■ Data Grouping
  ■ Data Subsetting
  ■ Merging & Joining Data Frames
• Importing Libraries & Datasets
• Munging & handling missing data
• Splitting the dataset into Training set & Test set

• Univariate Analysis – Study the distribution, spread, and pattern of a single column using histograms and boxplots.
• Bivariate Analysis – Examine the relationship between two variables using scatter plots and correlation.
• Multivariate Analysis – Analyze multiple variables together to uncover deeper patterns and interactions.
• Real Dataset EDA – Apply end-to-end analysis on actual messy real-world data to generate business-ready insights.

• Business Problem Understanding – Translate a company’s real-world problem into a clear, solvable data question.
• Data Cleaning – Fix missing values, duplicates, and inconsistent formats to make data analysis-ready.
• EDA – Discover patterns, trends, and anomalies from the cleaned data.
• Insights Generation – Convert raw numbers into actionable business recommendations.
• Project Review – Present the complete analytics workflow, just like it happens in a real job.

Project: App Reviews Sentiment Analysis – In this project, we extract the sentiment (positive, negative, neutral) from app reviews and analyze the results using the Seaborn data visualization library

• Project Planning – Decide what data is needed, which tools to use, and how the timeline should look.
• Dataset Analysis – Understand the structure, quality, and gaps in a raw dataset.
• Reporting – Present findings as dashboards/reports that are easy for non-technical stakeholders to understand.
• Insights Generation – Extract decision-making insights from business metrics like stock and inventory data.
• Project Review – Walkthrough and feedback on the complete analytics pipeline.

• Project: Inventory Management Analysis – In this project, we analyze stock levels, sales patterns, and inventory data to generate insights that help optimize restocking and reduce wastage.

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• Standardization – Rescale data to have mean = 0 and standard deviation = 1 so the model trains without bias.
• Normalization – Bring values into a fixed range (0–1), especially useful when features have very different scales.
• Standardization vs Normalization – Decide which technique to use based on the algorithm and data distribution.
• Scaling Techniques – Different scalers (MinMax, Robust, etc.) chosen based on outliers and data distribution.
• Label Encoding – Convert categorical text values into numbers (e.g., “Yes/No” → 1/0).
• Ordinal Encoding – Assign numbers to ranked categories (Low/Medium/High) based on their order.
• One Hot Encoding – Split categories into multiple binary columns so the model doesn’t assume a false ordering.
• Get Dummies – A quick Pandas method to easily implement One Hot Encoding.
• ColumnTransformer – Apply different preprocessing steps (scaling, encoding) to different columns simultaneously.
• Pipeline – Combine all preprocessing and modeling steps into a single, reusable workflow.
• Outlier Handling – Detect and treat extreme or incorrect values so they don’t distort model accuracy.
• End-to-End Preprocessing Pipeline – Build a complete automated flow that takes raw data to model-ready data.

• Linear Regression – The most fundamental algorithm for predicting continuous values like price or salary.
• Model Building – The standard ML workflow of splitting, training, and evaluating a model.
• Interview Questions – Conceptual and coding questions commonly asked in real placement interviews.
• Case Studies – Apply regression to real business scenarios to build practical understanding
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• Ridge Regression – Uses an L2 penalty to reduce overfitting, especially when features are correlated.
• Lasso Regression – Uses an L1 penalty to shrink unimportant features to zero, also performing feature selection.
• Regularization Techniques – Prevent a model from becoming too complex so it generalizes well to new data.
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• Logistic Regression – The core classification algorithm used to predict binary outcomes like Yes/No.
• Classification Modeling – The complete process of building models that predict categories.
• Evaluation Metrics – Accuracy, Precision, Recall, F1-score, ROC-AUC — used to judge a model’s real-world performance.

• Decision Tree Regressor – Predicts continuous values using an if-else style tree structure.
• Decision Tree Classifier – Uses the same tree logic to classify categories, making it an easily interpretable model.
• Random Forest Regressor – Averages predictions from multiple decision trees for more stable regression results.
• Random Forest Classifier – Combines the votes of multiple trees to reduce overfitting and improve accuracy.
• Naive Bayes – A fast, probability-based algorithm especially popular for text classification like spam detection.
• K-Nearest Neighbors (KNN) – Makes predictions by looking at the closest, most similar data points — simple and intuitive.
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• Support Vector Machine (SVM) – Draws the best possible boundary (margin) between classes.
• Kernel Trick – Projects non-linear data into a higher dimension to make it linearly separable.
• End-to-End ML Workflow – The complete production-style process from raw data to a deployed model.
• Model Deployment Preparation – Making a trained model ready for use inside a real application.

Project: COVID Prediction System – In this project, we build a machine learning model to predict COVID risk/outcome based on patient data, export it using Pickle, and deploy it as a REST API using Flask, tested via Postman.

• KMeans Clustering – Groups data into a fixed number of clusters based on similarity.
• DBSCAN Clustering – Forms clusters based on data density, capable of handling irregular shapes and detecting outliers/noise.

Project: Customer Segmentation using KMeans – In this project, we group customers into distinct segments based on their purchasing behavior and characteristics using the KMeans clustering algorithm.

• Principal Component Analysis (PCA) – Reduces the number of dimensions in high-dimensional data without losing important information.
• Apriori Algorithm – An association rule mining technique (market basket analysis) that finds “customers who buy X also buy Y” patterns.