Detailed Syllabus & Weekly Breakdown

Module 1: Python & Statistics Foundations (Weeks 1–6)

• Week 1-2: Python Basics (Variables, Loops, Data Structures)
• Week 3-4: Descriptive Statistics (Mean, Median, Variance, Distributions)
• Week 5-6: Inferential Statistics & Hypothesis Testing (A/B Testing)

Module 2: Data Wrangling & EDA (Weeks 7–12)

• Week 7-8: SQL for Data Analytics (Joins, Aggregations, Window Functions)
• Week 9-10: Pandas & NumPy for Data Manipulation
• Week 11-12: Exploratory Data Analysis (EDA) & Feature Engineering

Module 3: Machine Learning Basics (Weeks 13–18)

• Week 13-14: Scikit-Learn: Linear & Logistic Regression
• Week 15-16: Decision Trees & Random Forests
• Week 17-18: Unsupervised Learning: K-Means Clustering & PCA

Module 4: Advanced Analytics & Visualization (Weeks 19–24)

• Week 19-20: Python Visualization (Matplotlib, Seaborn)
• Week 21-22: Building Dashboards with Tableau or Power BI
• Week 23-24: Capstone: End-to-end Predictive Analytics Business Case

Learning Outcomes

• Clean, process, and analyze complex datasets using Python and SQL.
• Apply statistical methods to extract actionable business insights.
• Train and evaluate essential machine learning models for predictive analytics.
• Create compelling visual dashboards and data narratives.

Term and Credit Structure

Term	Module	Weeks	Credits
Term 1 (Foundations)	Module 1: Python & Statistics Foundations	1–6	4
Term 1 (Foundations)	Module 2: Data Wrangling & EDA	7–12	4
Term 2 (Analytics)	Module 3: Machine Learning Basics	13–18	4
Term 2 (Analytics)	Module 4: Advanced Analytics & Visualization	19–24	4
Total		24 Weeks	16 Credits

Assessment Weightage

• Data Wrangling & SQL Assignments: 30%
• Machine Learning Mini-Projects: 25%
• Mid-Term Statistics Assessment: 15%
• Final Capstone Project & Dashboard Presentation: 30%