Data Science and Business Analytics

Session 1: Overview of Data Science and Business Analytics

Session 2: Introduction to R Programming

Session 3: Introduction to Python Programming

Session 4: Essential Data Structures and Libraries

Session 5: Statistical Inference and Hypothesis Testing

   Session 1: Principles of Statistical and Machine Learning

   Session 2: Linear Regression and Model Evaluation

  Session 3: Classification Techniques

  Session 4: Decision Trees and Clustering Methods

  Session 5: Treatment Effects and Experimental Design

   Session 1: Web Scraping and Data Retrieval Techniques

   Session 2: Handling Missing Data and Data Imputation

  Session 3: Model Validation and Performance Metrics

  Session 4: Feature Engineering and Dimensionality Reduction

  Session 1: Advanced ML techniques

  Session 2: Text Mining and Natural Language Processing

  Session 3: Neural network and Introduction to LLM

  Session 4: Association Rule Mining, Sentiment Analysis and Recommendation Systems

  Session 1: Data Analysis and Visualization – Fundamentals

  Session 2: Data Analysis and Visualization with Power BI

  Session 3: Data Analysis and Visualization with Tableau

  Session 4: Data modeling and SQL

   Session 1: Time Series Analysis (Exponential Methods) and Anomaly Detection

   Session 2: Time Series Analysis (Stationary, Curve Fitting and NN Methods)

  Session 3: End-to-End Machine Learning (Hands on Session)

  Session 4: Overview of MLOps