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Data Scientist
Machine Learning Engineer
Data Analyst (Advanced)
Business Intelligence (BI) Analyst
AI / Data Science Engineer (Entry Level)
Module 1 – Python Programming Core – 48 Hrs
- Introduction to Python: Python’s role in data analysis
- Data Types and Variables: Integers, floats, strings, and booleans
- Operators and Expressions: Arithmetic, logical, comparison, and assignment
- Control Flow: If-else statements, for and while loops
- Data Structures: Strings, tuples, dictionaries
- File Handling Basics: Reading and writing text files
- Functions and Modules: Writing reusable code and using built-in modules
- Error Handling: Managing errors with try-except blocks
Module 2 – Python Advanced: Key Libraries – 24 Hrs
- NumPy: Array creation, mathematical operations, indexing, reshaping
- Pandas: DataFrames, Series, importing/exporting, data cleaning
- Matplotlib: Bar charts, scatter plots, histograms, line graphs etc.
- Working with CSV and TXT Files: Reading, writing, and modifying structured data
Module 3 – SQL (Structured Query Language) – 24 Hrs
- Database Basics: Relational database concepts, RDBMS fundamentals
- SQL Queries: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Joins and Relationships: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
- Data Manipulation: INSERT, UPDATE, DELETE
- Constraints: Primary Key, Foreign Key, Unique, Check, Default, Not Null
- Subqueries and Set Operations: Subqueries, IN, EXISTS, UNION, INTERSECT
- Window (Analytic) Functions: ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG
- Administrative Commands: CREATE USER, ALTER USER, DROP USER, GRANT, REVOKE, COMMIT, ROLLBACK
Module 4 – Basic & Advanced Excel – 18 Hrs
- Basic Excel
- Formulas and Functions: SUM, AVERAGE, IF, VLOOKUP, HLOOKUP
- Data Formatting: Conditional formatting, tables, sorting
- Basic Charts: Bar, line, and pie charts
- Advanced Excel
- Pivot Tables: Creating and customizing pivot tables
- Data Analysis ToolPak: Regression, correlation, statistical analysis
- Macros and VBA: Automating tasks with simple scripts
Module 5 – Power BI – 10 Hrs
- Introduction to Power BI: Dashboards and reports overview
- Connecting Data Sources: Excel, SQL, APIs
- Data Transformation: Using Power Query
- Building Visualizations: Bar charts, pie charts, maps
- DAX (Data Analysis Expressions): Calculated columns, measures
- Interactive Dashboards: Creating dynamic, user-driven reports
- Publishing and Sharing: Making your reports accessible
Module 6: Python Libraries for Machine Learning (40 hrs)
- NumPy: Arrays, vectorized operations, indexing, reshaping
- Pandas: Series, DataFrames, filtering, groupby, merging, data cleaning
- Matplotlib: Line, bar, scatter, histogram plots
- Seaborn: Heatmaps, pairplots, distribution plots
- Scikit-learn: Overview of ML workflow
- Reading datasets from CSV files
- Basic data inspection using head(), info(), describe()
- Preparing data for ML models
Module 7: Introduction to ML , Data Preprocessing & Feature Engineering
Showcase Your Skills
- What is Machine Learning?
- AI vs ML vs Deep Learning
- Types of ML: Supervised, Unsupervised, Reinforcement
- Real-world ML applications
- Importing data using Pandas
- Exploratory Data Analysis (EDA)
- Missing value detection (.isnull())
- Statistical analysis (describe, correlation)
- Handling missing values: dropna(), fillna()
- Encoding categorical variables
- Feature scaling using StandardScaler
- Creating and selecting relevant features
Module 8 – Supervised Machine Learning
- Linear Regression (Simple & Multiple)
- Polynomial Regression
- Decision Tree Regressor
- Random Forest Regressor
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Decision Tree Classifier
- Random Forest Classifier
Module 9 – Unsupervised Machine Learning
- K-Means clustering
- Finding optimal number of clusters
- Cluster visualization
- Customer segmentation use case
- Hierarchical clustering
- Dendrogram visualization
- PCA (dimensionality reduction – intro)
- Apriori algorithm (association rules)
Module 10 – Model Evaluation & Validation& Model Deployment
- Train-test split
- Regression metrics: MAE, MSE, RMSE
- Classification metrics: Accuracy, Precision, Recall, F1-Score
- Confusion Matrix, ROC-AUC Curve
- Model score using .score()
- Overfitting & Underfitting
- Introduction to ML model deployment
- Saving & loading models (joblib / pickle)
- Handling user inputs & predictions
- Creating interactive ML apps using Streamlit& Flask
- Model testing & validation after deployment
Module 11 – Real-world Projects & Case Studies
- Netflix dataset analysis
- Data cleaning & visualization
- Outlier detection
- Titanic Prediction System
- Loan prediction system
- Diabetes prediction model
- Placement & salary prediction
- Customer segmentation project
