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Detailed Syllabus & Weekly Breakdown

Term 1: Data Science & Python Foundations (Weeks 1–12)

Focus: Building a strong foundation in programming, statistics, and data manipulation.

• Python for Data Science (NumPy, Pandas).
• Statistics and Probability for Data Science.
• Data Wrangling and Exploratory Data Analysis (EDA).
• Data Visualization with Matplotlib & Seaborn.

Term 2: Machine Learning & Statistical Modeling (Weeks 13–24)

Focus: Mastering core machine learning algorithms and evaluation techniques.

• Supervised Learning: Linear/Logistic Regression, Decision Trees, SVM.
• Unsupervised Learning: K-Means Clustering, PCA.
• Model Evaluation and Performance Metrics.
• Feature Engineering and Selection.

Term 3: Advanced AI & Big Data Technologies (Weeks 25–36)

Focus: Exploring deep learning and tools for handling large-scale data.

• Introduction to Artificial Intelligence & Deep Learning.
• Natural Language Processing (NLP) Fundamentals.
• Big Data Technologies (Hadoop, Spark).
• SQL and NoSQL Databases for Data Science.

Term 4: Specialization & Capstone Project (Weeks 37–48)

Focus: Applying knowledge to a real-world problem and building a portfolio.

• Advanced Data Visualization with Tableau/Power BI.
• Time Series Analysis and Forecasting.
• Model Deployment and MLOps Basics.
• Industry-grade Capstone Project.

Comprehensive Learning Outcomes

• Perform end-to-end data science projects, from data collection and cleaning to model deployment.
• Apply statistical and machine learning techniques to extract actionable insights from data.
• Build a strong portfolio of data science projects to showcase to potential employers.

Assessment Weightage

Assessment Type	Weightage	Focus Area
Term-End Projects	40%	Hands-on projects at the end of each term.
Quizzes & Assignments	20%	Continuous evaluation of concepts.
Final Capstone Project	40%	Demonstration of end-to-end data science project execution.

The "WhiteCollar" Career Advantage

This Master Program is your launchpad into the booming field of Data Science and AI. You will gain the practical skills and theoretical knowledge required to solve complex business problems using data. Graduates are prepared for high-growth roles such as Data Scientist, ML Engineer, Data Analyst, and AI Specialist.