Course Prerequisites:
Basic knowledge of computer science and statistics (beneficial but not mandatory) Keen interest in data analysis Willingness to learn programming languages such as Python and R
Modules:
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 and 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 Refresher
8.1 Introduction to Supervised Learning Algorithms
8.2 Introduction to Unsupervised Learning
8.3 Different Algorithms for Clustering
8.4 Association Rule Learning
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