Semester-wise Syllabus for an MSc in Data Science

Semester 1: Foundations of Data Science

1. Programming for Data Science (Python/R)

   • Python basics (NumPy, Pandas), R (tidyverse)

   • Data structures, loops, functions, and OOP concepts

2. Mathematics for Data Science

   • Linear algebra (vectors, matrices, eigenvalues)

   • Calculus (gradients, optimization), Probability (distributions, Bayes’ theorem)

3. Statistics for Data Science

   • Descriptive/inferential statistics, hypothesis testing

   • Regression analysis, ANOVA, non-parametric tests

4. Data Wrangling & Visualization

   • Data cleaning (missing values, outliers)

   • Visualization tools (Matplotlib, Seaborn, ggplot2, Tableau)

5. Database Management (SQL/NoSQL)

   • SQL queries (joins, subqueries), MongoDB basics

   • ETL processes, data pipelines

Semester 2: Machine Learning & Big Data

1. Machine Learning Fundamentals

   • Supervised learning (Linear Regression, Decision Trees, SVM)

   • Unsupervised learning (Clustering, PCA, K-means)

   • Model evaluation (cross-validation, ROC curves)

2. Big Data Technologies

   • Hadoop ecosystem (HDFS, MapReduce)

   • Spark (PySpark, Spark SQL), distributed computing

3. Advanced Statistics

   • Bayesian methods, time series analysis (ARIMA)

   • Experimental design (A/B testing)

4. Cloud Computing for Data Science

   • AWS/GCP/Azure for data storage & processing

   • Serverless architectures (Lambda, BigQuery)

5. Domain Elective (Choose 1)

   • Healthcare Analytics: EHR data, predictive modeling

   • Financial Data Science: Risk modeling, algorithmic trading

Semester 3: Advanced Topics & Specializations

1. Deep Learning

   • Neural networks (CNNs, RNNs, Transformers)

   • Frameworks: TensorFlow, PyTorch

2. Natural Language Processing (NLP)

   • Text preprocessing, sentiment analysis, BERT

   • Topic modeling (LDA), chatbots

3. Data Engineering

   • Airflow for workflow automation

   • Kafka for real-time data streaming

4. Electives (Choose 2–3)

   • Computer Vision: Image classification, YOLO

   • Reinforcement Learning: Q-learning, Deep Q Networks

   • Graph Analytics: Network analysis, GNNs

   • Ethics in AI: Bias, fairness, GDPR compliance

5. Industry Case Studies

   • Capstone project kickoff (problem statement, data sourcing)

Semester 4: Capstone Project & Deployment

1. Scalable Machine Learning

   • Model deployment (Flask, FastAPI, Docker)

   • MLOps (MLflow, Kubeflow)

2. Business Intelligence & Storytelling

   • Dashboarding (Power BI, Dash)

   • Communicating insights to stakeholders

3. Capstone Project

   • End-to-end project (e.g., recommendation engine, fraud detection)

   • GitHub portfolio, research paper (optional)

4. Internship (Optional)

   • 6–8 weeks with industry partners (tech firms, startups)