AI+ Data (AIDATA) – Outline

Detailed Course Outline

Module 1: Foundations of Data Science

• 1.1 Introduction to Data Science
• 1.2 Data Science Life Cycle
• 1.3 Applications of Data Science

Module 2: Foundations of Statistics

• 2.1 Basic Concepts of Statistics
• 2.2 Probability Theory
• 2.3 Statistical Inference

Module 3: Data Sources and Types

• 3.1 Types of Data
• 3.2 Data Sources
• 3.3 Data Storage Technologies

Module 4: Programming Skills for Data Science

• 4.1 Introduction to Python for Data Science
• 4.2 Introduction to R for Data Science

Module 5: Data Wrangling an Preprocessing

• 5.1 Data Imputation Techniques
• 5.2 Handling Outliers and Data Transformation

Module 6: Exploratory Data Analysis (EDA)

• 6.1 Introduction to EDA
• 6.2 Data Visualization

Module 7: Generative AI Tools for Deriving Insights

• 7.1 Introduction to Generative AI Tools
• 7.2 Applications of Generative AI

Module 8: Machine Learning

• 8.1 Introduction to Supervised Learning Algorithms
• 8.2 Introduction to Unsupervised Learning
• 8.3 Different Algorithms for Clustering
• 8.4 Association Rule Learning with Implementation

Module 9: Advance Machine Learning

• 9.1 Ensemble Learning Techniques
• 9.2 Dimensionality Reduction
• 9.3 Advanced Optimization Techniques

Module 10: Data-Driven Decision-Making

• 10.1 Introduction to Data-Driven Decision Making
• 10.2 Open Source Tools for Data-Driven Decision Making
• 10.3 Deriving Data-Driven Insights from Sales Dataset

Module 11: Data Storytelling

• 11.1 Understanding the Power of Data Storytelling
• 11.2 Identifying Use Cases and Business Relevance
• 11.3 Crafting Compelling Narratives
• 11.4 Visualizing Data for Impact

Module 12: Capstone Project - Employee Attrition Prediction

• 12.1 Project Introduction and Problem Statement
• 12.2 Data Collection and Preparation
• 12.3 Data Analysis and Modeling
• 12.4 Data Storytelling and Presentation