Data Science Workshop

Guidelines Event: 2-Day Offline Data Science Workshop Date: 17th–18th December Venue: Lecture Hall Complex, IIT Bombay Campus Duration: 12–16 hours Mode: Offline Format: Concept sessions, live demonstrations, hands-on practice, and an end-to-end Data Science project/capstone. Eligibility: The workshop is designed for learners from technology, engineering, mathematics, business, and analytics backgrounds. Prerequisites: Participants should bring a laptop/PC with at least 4 GB RAM, internet connectivity, and a notepad and pen. Certificate: Participants must attend all sessions to receive the certificate. A Certificate of Training from Techfest, IIT Bombay will be provided to eligible participants. Exclusive Benefit: Participants receive a free access pass to Techfest, IIT Bombay, including events such as EDM Night, Robowars, exhibitions, keynote sessions, and more. Topics The workshop covers the complete Data Science lifecycle, from problem formulation and data preparation to machine learning, evaluation, and modern AI applications. Day 1 – Data Foundations, Python & Exploration Introduction to Data Science and its applications Data Science vs Data Analytics vs AI vs Machine Learning Structured, semi-structured, and unstructured data Data Science project lifecycle and problem formulation Python fundamentals NumPy arrays and numerical computing Pandas DataFrames and data manipulation SQL fundamentals including SELECT, WHERE, GROUP BY, JOIN, ORDER BY, and aggregations Data cleaning and wrangling Missing-value imputation Duplicate and inconsistent data Outlier detection and treatment Data transformation and normalization Exploratory Data Analysis Univariate, bivariate, and multivariate analysis Descriptive statistics and basic inferential statistics Data visualization using Matplotlib and Seaborn Data storytelling and actionable insights Day 2 – Machine Learning, AI & Capstone Feature engineering and feature transformation Categorical encoding Feature scaling and normalization Feature selection Principal Component Analysis (PCA) Supervised and unsupervised learning Linear Regression Logistic Regression Decision Trees K-Means Clustering Ensemble methods: Random Forest and Gradient Boosting Train-test split and validation Overfitting and underfitting Confusion matrix Accuracy, precision, recall, and F1-score MAE, MSE, RMSE, and R² Cross-validation Hyperparameter tuning and model comparison Introduction to Big Data and distributed computing Neural networks and Deep Learning Natural Language Processing (NLP) Generative AI Self-supervised and multimodal learning Emerging AI-driven Data Science applications End-to-end Data Science project/capstone