📅 Week-by-Week Timetable (45–60 Hours) Data Analytics Program semester program for college students

Week 1: Data Foundations (10 hrs)

Day 1 – Introduction to Data Analysis (2 hrs)

• What is Data Analysis? Importance in business.

• Real-life use cases (Netflix, Amazon, Banks).

Day 2 – Data Preparation Basics (2 hrs)

• Cleaning data, handling missing values, duplicates.

• Hands-on in Excel.

Day 3 – Common Data Problems (2 hrs)

• Errors in HR, banking, retail datasets.

• Mini-exercise: fix inconsistencies.

Day 4 – Tools for Data Analysis (2 hrs)

• Excel, Power BI, Tableau, SQL, Python.

• Hands-on demo: pivot & charts in Excel.

Day 5 – Evolution of Analytics (2 hrs)

• From descriptive → AI-driven analytics.

• Case studies: Finance, Healthcare, Retail.

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Week 2: Analytics Frameworks & Statistics (10 hrs)

Day 6 – Types of Analytics (2 hrs)

• Descriptive, Diagnostic, Predictive, Prescriptive.

• Hands-on: sales dataset analysis.

Day 7 – CRISP-DM Framework (2 hrs)

• Steps in analytics project lifecycle.

• Example: Telecom churn.

Day 8 – Univariate Statistics (2 hrs)

• Mean, Median, Mode, Variance, Std Dev.

• Hands-on: student marks dataset.

Day 9 – Probability for Data Analysis (2 hrs)

• Probability basics, applications in business.

• Mini-exercise: forecast product demand.

Day 10 – Hypothesis Testing (2 hrs)

• Null vs Alternative hypothesis.

• t-test, chi-square basics.

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Week 3: Data Visualization (10 hrs)

Day 11 – Excel Visualizations (2 hrs)

• Charts, PivotTables, Conditional Formatting.

Day 12 & 13 – Power BI Basics (4 hrs)

• Importing data, relationships, DAX basics.

• Build an interactive dashboard.

Day 14 – Advanced Power BI (2 hrs)

• Drill-through, forecasting, custom visuals.

Day 15 – Tableau Introduction (2 hrs)

• Connecting data, building dashboards.

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Week 4: Advanced Analytics (10 hrs)

Day 16 – Correlation Analysis (2 hrs)

• Finding relationships between variables.

• Hands-on: advertising vs revenue.

Day 17 – Regression Analysis (2 hrs)

• Simple & multiple regression.

• Hands-on: temperature vs ice cream sales.

Day 18 – Time Series Forecasting (2 hrs)

• Trends, moving averages, seasonal patterns.

• Hands-on: sales forecasting in Power BI.

Day 19 – Classification Models (2 hrs)

• Logistic regression, decision trees basics.

• HR attrition dataset.

Day 20 – Advanced Visualization with Python (2 hrs)

• Matplotlib & Seaborn basics.

• Hands-on: Salary distribution plots.

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Week 5: Backend for Analytics (10 hrs)

Day 21 & 22 – SQL for Data Analytics (4 hrs)

• SELECT, WHERE, GROUP BY, JOIN queries.

• Hands-on: employee & sales databases.

Day 23 & 24 – Python for Data Analysis (4 hrs)

• Pandas, NumPy for data wrangling.

• Cleaning & analyzing CSV files.

Day 25 – Python Data Wrangling & Export (2 hrs)

• Merge, filter, group data.

• Export to visualization tools.

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Week 6: Capstone & Industry Application (5–10 hrs)

Day 26 – Project Kickoff (2 hrs)

• Form groups, choose dataset (HR, Retail, Finance, Healthcare).

• Define problem statement.

Day 27 & 28 – Project Work (4 hrs)

• Data cleaning, analysis, visualization.

• Use SQL, Power BI, Python (as relevant).

Day 29 – Project Completion & Review (2 hrs)

• Final dashboards & insights.

• Faculty/peer feedback.

Day 30 – Final Presentations & Certification (2 hrs)

• Teams present findings.

• Certificates + feedback session.

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✅ Total Hours = ~55 Hours

(can extend to 60 with extra SQL/Python labs or industry guest lectures)

🎯 Final Outcomes for Students:

• Strong foundation in statistics & analytics concepts.

• Practical knowledge of Excel, SQL, Power BI, Tableau, Python.

• Hands-on experience with real datasets.

• A capstone project portfolio to showcase in placements.