semester-wise syllabus for an M.Tech in Data Science and Engineering

 

Semester 1: Core Foundations

Courses:

1. Advanced Mathematics for Data Science 

   - Linear algebra, probability, statistics, optimization, and calculus for ML. 

2. Machine Learning Fundamentals 

   - Supervised/unsupervised learning (regression, SVM, clustering), evaluation metrics, and bias-variance tradeoff. 

3. Big Data Technologies 

   - Hadoop, Spark, HDFS, MapReduce, and distributed computing frameworks. 

4. Data Visualization 

   - Tools (Tableau, Power BI), storytelling with data, and exploratory data analysis (EDA). 

5. Programming for Data Science 

   - Python/R, SQL, and libraries (NumPy, Pandas, Scikit-learn). 

Labs: 

- Python/R Programming Lab (Jupyter, RStudio) 

- Big Data Lab (Spark, Hadoop, AWS/Google Cloud) 

 

Semester 2: Specialization & Electives 

Core Courses:

1. Deep Learning 

   - Neural networks, CNNs, RNNs, transformers, and frameworks (TensorFlow, PyTorch). 

2. Advanced Statistical Modeling 

   - Bayesian methods, time series analysis, and experimental design. 

 

Electives (Examples): 

- Natural Language Processing (NLP) 

- Computer Vision 

- Cloud Computing for Data Science (AWS, Azure, GCP) 

- Business Analytics (decision trees, A/B testing, optimization) 

- IoT and Sensor Data Analytics 

Labs:

- Deep Learning Lab (TensorFlow/PyTorch projects) 

- NLP Lab (NLTK, spaCy, Hugging Face) 

 

Semester 3:

Advanced Electives & Project Work 

Electives (Examples): 

- Reinforcement Learning 

- AI Ethics and Responsible AI

- Graph Analytics (network analysis, GNNs) 

- Time Series Forecasting (ARIMA, Prophet, LSTM) 

- Blockchain and Data Security 

Project/Dissertation:

- Phase 1: Topic selection (e.g., fraud detection, recommendation systems, predictive maintenance), literature review, and proposal. 

- Seminars: Presentations on trends like MLOps, AutoML, or generative AI (e.g., GPT, diffusion models). 

 

Semester 4: Thesis/Project Completion

Thesis/Project: 

- Full-time focus on end-to-end implementation (data collection, model training, deployment). 

- Final documentation, viva voce defense, and deployment (e.g., Flask/Django API, cloud deployment). 

Additional Components: 

- Industrial Internship (optional, with tech firms, startups, or analytics consultancies). 

- Workshops: Training in *MLOps tools* (MLflow, Kubeflow), *Docker/Kubernetes, or **AI deployment platforms* (Sagemaker, Vertex AI). 

 

Elective Tracks (Specializations): 

1. AI/ML Engineering 

   - Model deployment, MLOps, and scalable ML systems. 

2. Big Data Analytics

   - Distributed systems, real-time analytics (Kafka, Spark Streaming). 

3. Business Intelligence 

   - Dashboarding, prescriptive analytics, and decision science. 

4. Domain-Specific Analytics 

   - Healthcare, finance, retail, or social media analytics.