Detailed Syllabus & Weekly Breakdown

Module 1: Intro to Data Science (Weeks 1–4)

• Week 1: Overview of Data Science landscape
• Week 2: Python/R programming basics
• Week 3: Introduction to Data Analytics
• Week 4: Real-world case studies

Module 2: Statistical Foundations (Weeks 5–8)

• Week 5: Descriptive and inferential statistics
• Week 6: Data manipulation libraries (Pandas)
• Week 7: Cleaning and processing missing data
• Week 8: Data wrangling projects

Module 3: Exploratory Data Analysis (Weeks 9–12)

• Week 9: Visualization principles
• Week 10: Matplotlib and Seaborn
• Week 11: Creating interactive dashboards
• Week 12: Storytelling with data

Module 4: Predictive Modeling (Weeks 13–16)

• Week 13: Basic predictive modeling techniques
• Week 14: Evaluating model performance
• Week 15: Capstone project planning
• Week 16: Final project presentation

Learning Outcomes

• Strong foundation in data processing and analysis
• Ability to build and evaluate predictive models
• Practical experience with industry-standard tools
• End-to-end understanding of the data science lifecycle

Assessment Weightage

Assessment Type	Weightage
Strategic Simulations	20%
Leadership Portfolio	20%
Quizzes & Presentations	20%
Final CXO Pitch (Capstone)	40%

The "WhiteCollar" Executive Perks

• CXO Mentorship: Direct access to industry leaders
• Investor Meets: Opportunity to pitch innovation ideas