Recommended Prerequisites:
- Basic Understanding of Computer Science: Familiarity with programming and statistics (beneficial but not mandatory).
- Interest in Data Analytics: A keen passion for analyzing data trends and solving real-world problems.
- Willingness to Learn Python and R: Basic programming skills help, but the program is designed to support beginners.
Course Outline:
Lesson 1: Foundations of Data Science
- 1.1 Introduction to Data Science
- 1.2 Data Science Life Cycle
- 1.3 Applications of Data Science
Lesson 2: Foundations of Statistics
- 2.1 Basic Concepts of Statistics
- 2.2 Probability Theory
- 2.3 Statistical Inference
Lesson 3: Data Sources and Types
- 3.1 Types of Data
- 3.2 Data Sources
- 3.3 Data Storage Technologies
Lesson 4: Programming Skills for Data Science
- 4.1 Introduction to Python for Data Science
- 4.2 Introduction to R for Data Science
Lesson 5: Data Wrangling and Preprocessing
- 5.1 Data Imputation Techniques
- 5.2 Handling Outliers and Data Transformation
Lesson 6: Exploratory Data Analysis (EDA)
- 6.1 Introduction to EDA
- 6.2 Data Visualization
Lesson 7: Generative AI Tools for Deriving Insights
- 7.1 Introduction to Generative AI Tools
- 7.2 Applications of Generative AI
Lesson 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
Lesson 9: Advance Machine Learning
- 9.1 Ensemble Learning Techniques
- 9.2 Dimensionality Reduction
- 9.3 Advanced Optimization Techniques
Lesson 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
Lesson 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
Lesson 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
Optional Lesson: AI Agents for Data Analysis
- Understanding AI Agents
- Case Studies
- Hands-On Practice with AI Agents
