Month 1: Programming & Data Science Foundations

Module 1: Introduction to Data Science & Analytics

Module Description:
Builds strong foundational knowledge in data science concepts, analytics lifecycle, and industry applications.

• What is Data Science? Scope and career opportunities
• Data Science lifecycle (collection, cleaning, analysis, modeling, deployment)
• Types of data: structured vs unstructured
• Introduction to statistics for data science
• Mean, median, variance, standard deviation
• Probability fundamentals
• Business problem understanding & data-driven decision making
• Overview of tools used in Data Science

Module 2: Python Programming for Data Science

Module Description:
Equips learners with programming skills required for data analysis and machine learning.

• Introduction to Python
• Variables, data types, operators
• Control structures (if-else, loops)
• Functions & modules
• Lists, tuples, dictionaries, sets
• File handling
• Exception handling
• Introduction to Jupyter Notebook
• Working with NumPy & Pandas libraries

Practical:
Mini project – Data analysis using Python and Pandas

Month 2: Data Analysis & Visualization

Module 3: Data Cleaning & Exploratory Data Analysis (EDA)

Module Description:
Develops practical skills in preparing and analyzing datasets.

• Data importing & exporting
• Handling missing values
• Data transformation & normalization
• Feature engineering basics
• Outlier detection
• Correlation analysis
• Exploratory Data Analysis techniques
• Working with real-world datasets

Module 4: Data Visualization & Business Reporting

Module Description:
Focuses on creating meaningful visual insights for business decision-making.

• Data visualization principles
• Using Matplotlib & Seaborn
• Interactive dashboards using Power BI / Tableau basics
• Creating charts, graphs, and dashboards
• Business storytelling with data
• Reporting insights effectively

Practical:
Build interactive dashboard & EDA report

Month 3: Machine Learning Fundamentals

Module 5: Supervised Machine Learning

Module Description:
Introduces core predictive modeling techniques used in industry.

• Introduction to Machine Learning
• Regression (Linear & Multiple)
• Logistic Regression
• Decision Trees
• Random Forest
• Model evaluation (Accuracy, Precision, Recall, F1 Score)
• Train-test split & cross-validation

Module 6: Unsupervised Learning & Model Optimization

Module Description:
Focuses on pattern detection and improving model performance.

• Clustering (K-Means, Hierarchical)
• Dimensionality reduction (PCA basics)
• Feature selection
• Hyperparameter tuning
• Model optimization techniques
• Introduction to Scikit-Learn

Practical:
Predictive modeling project with performance evaluation

Month 4: Deep Learning & Generative AI

Module 7: Deep Learning Foundations

Module Description:
Provides understanding of neural networks and deep learning concepts.

• Introduction to Neural Networks
• Activation functions
• Backpropagation concept
• Introduction to TensorFlow / PyTorch
• Building basic neural networks
• Overfitting & regularization

Module 8: Generative AI & Large Language Models

Module Description:
Introduces cutting-edge Generative AI technologies and practical applications.

• What is Generative AI?
• Overview of Large Language Models (LLMs)
• Introduction to ChatGPT and prompt engineering
• Text generation & summarization
• Image generation tools overview
• Retrieval-Augmented Generation (RAG) basics
• Ethical AI & responsible AI practices
• Applications of Generative AI in business

Practical:
Build AI-powered content generation or chatbot prototype

Month 5: Advanced Data Science & Deployment

Module 9: Advanced Topics in Data Science

Module Description:
Enhances technical capabilities for real-world data projects.

• Time series analysis basics
• Recommendation systems overview
• Natural Language Processing (NLP) basics
• Sentiment analysis
• Working with APIs
• Data scraping basics

Module 10: Model Deployment & MLOps Basics

Module Description:
Prepares learners for production-level implementation.

• Model deployment concepts
• Introduction to Flask / FastAPI
• Cloud deployment basics
• Version control using Git
• CI/CD overview
• Monitoring & maintaining ML models

Practical:
Deploy a machine learning model as a web application

Month 6: Capstone Project & Career Development

Module 11: Industry Capstone Project

Module Description:
End-to-end data science project integrating analytics and Generative AI.

• Problem identification
• Data collection & cleaning
• Model development
• AI integration (LLM or generative tool)
• Dashboard creation
• Model deployment
• Final presentation & documentation

Module 12: Employability Skills & Career Preparation

Module Description:
Prepares learners for job placement and freelance opportunities.

• Resume building for Data Science roles
• GitHub portfolio creation
• LinkedIn profile optimization
• Interview preparation (technical + HR)
• Case study solving
• Freelancing & consulting opportunities
• Career paths: Data Analyst, Data Scientist, ML Engineer, AI Specialist

Final Practical:
Capstone project presentation & Viva

Assessment

• Monthly practical assignments
• Machine learning model evaluation test
• Generative AI project submission
• Final capstone project
• Viva voce examination

Outcome

After completing this course, learners will be able to:

• Perform data analysis using Python
• Build machine learning models
• Apply deep learning techniques
• Develop Generative AI-based solutions
• Deploy ML models to production
• Create dashboards and business insights
• Build a professional data science portfolio

Career Opportunities

• Data Analyst
• Junior Data Scientist
• Machine Learning Engineer
• AI Developer
• Business Intelligence Analyst
• NLP Engineer
• Generative AI Specialist
• Data Science Consultant