Degree Level Lab
Data Science and AI Lab
This is a hands-on course designed to bridge the gap between academic knowledge and industry needs in Data Science and AI. The course revisits essential data science concepts and includes contemporary AI technologies. Each week introduces a new concept and accompanies an end-to-end assignment, simulating real-world workflows.
Course ID: BSDA4001
Course Credits: 4
Course Type: Elective
Pre-requisites: BSCS3004 - Deep Learning
Course structure & Assessments
For details of standard course structure and assessments, visit Academics page.
| WEEK 1 | Week 1: Data Science & Python Stack Refresher Key tools: NumPy, Pandas, Matplotlib, Seaborn, Jupyter, Git |
| WEEK 2 | Week 2: Machine Learning with Scikit-learn Core models: regression, classification, clustering Pipelines, hyperparameter tuning, evaluation metrics |
| WEEK 3 | Week 3: Deep Learning with PyTorch and TensorFlow Building and training deep neural networks Model saving/loading, GPU training |
| WEEK 4 | Week 4: Computer Vision and Image Processing Using OpenCV, PIL, PyTorch/TensorFlow for image loading, preprocessing Transfer learning with CNNs (ResNet, MobileNet) |
| WEEK 5 | Week 5: NLP and LLMs using HuggingFace Using BERT, RoBERTa for classification/generation Tokenization, fine-tuning, and inference |
| WEEK 6 | Week 6: Prompt Engineering, LangChain & LLM APIs Working with LangChain, templates, and memory modules Prompt tuning best practices Using OpenAI, Gemini, and Ollama APIs for inference |
| WEEK 7 | Week 7: Retrieval-Augmented Generation (RAG) & Vector DBs Concepts of semantic search & embedding spaces Use of FAISS, Pinecone, ChromaDB |
| WEEK 8 | Week 8: Agents and Autonomous AI (Langchain, LangGraph) Concepts of tool use, planning, multi-step task agents Implementing basic agents using LangChain |
| WEEK 9 | Week 9: Data Visualization & Dashboards Tools: PowerBI, Tableau, Streamlit, Plotly Dashboard design, storytelling with data |
| WEEK 10 | Week 10: Time Series Forecasting Exploratory Analysis of Time Series Forecasting using Facebook Prophet and DeepAR Trend, seasonality, holidays, anomaly detection |
| WEEK 11 | Week 11: Big Data and Cloud Platforms - Google Cloud Platform Build and run an end-to-end pipeline in GCP Working with GCP, BigQuery, Vertex AI Concepts of Spark, Hadoop, Airflow, and model deployment |
| WEEK 12 | Week 12: Model Explainability, Ethics & Mini-project Tools: SHAP, LIME for understanding predictions Concepts: data privacy, fairness, AI ethics Mini-project |
Please use only the above methods for program queries. Response time: 3 working days. During peak periods, Google Meet links will be shared. Call wait times may be longer.