📊 Data Analytics Professional Program (45–60 Hours) crash course for college students

Phase 1: Foundations of Data Analysis (6 hours)

Day 1 – Introduction to Data Analysis (2 hrs)

• What is Data Analysis? Why is it important?

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

• Types of data: Structured vs Unstructured.

Day 2 – Data Preparation (2 hrs)

• Data cleaning basics: missing values, duplicates, inconsistencies.

• Standardization & normalization.

• Hands-on with Excel dataset.

Day 3 – Common Data Problems (2 hrs)

• Issues in HR, Finance & E-commerce data.

• Best practices for handling categorical & numerical data.

• Mini-project: Clean raw student data file.

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Phase 2: Tools & Evolution of Analytics (6 hours)

Day 4 – Tools Overview (2 hrs)

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

• When to use what?

Day 5 – Evolution of Analytics (2 hrs)

• From descriptive dashboards → AI-powered insights.

• Industry case studies.

Day 6 – Classification of Analytics (2 hrs)

• Descriptive, Diagnostic, Predictive, Prescriptive.

• Hands-on with sales dataset (Excel pivot, what-if analysis).

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Phase 3: CRISP-DM & Statistics (10 hours)

Day 7 – CRISP-DM Framework (2 hrs)

• Business understanding, data understanding, modeling, deployment.

• Example: Telecom churn analysis.

Day 8 – Univariate Statistics (2 hrs)

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

• Hands-on with exam scores dataset.

Day 9 – Probability Concepts (2 hrs)

• Basic probability rules.

• Applications in forecasting & risk.

Day 10 – Hypothesis Testing (2 hrs)

• Null vs Alternative Hypothesis.

• t-tests, chi-square basics.

• Hands-on: Testing difference between two groups (student marks).

Day 11 – Data Distribution (2 hrs)

• Normal distribution, skewness, kurtosis.

• Real-world business applications.

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Phase 4: Data Visualization (12 hours)

Day 12 – Excel Visualization Basics (2 hrs)

• Charts, PivotTables, Conditional Formatting.

Day 13 & 14 – Power BI Essentials (4 hrs)

• Importing data, building dashboards.

• Filters, slicers, DAX basics.

Day 15 & 16 – Advanced Power BI (4 hrs)

• Custom visuals, relationships, drill-through.

• AI-powered forecasting.

Day 17 – Tableau Basics (2 hrs)

• Connecting data sources.

• Creating interactive dashboards.

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Phase 5: Bi-Variate & Predictive Analytics (8 hours)

Day 18 – Correlation Analysis (2 hrs)

• Relationship between two variables.

• Hands-on: Advertising budget vs sales revenue.

Day 19 – Regression Analysis (2 hrs)

• Simple & multiple regression.

• Hands-on: Temperature vs ice cream sales.

Day 20 – Time Series Forecasting (2 hrs)

• Moving average, trend lines.

• Forecasting sales using Power BI & Python.

Day 21 – Classification Models (2 hrs)

• Intro to Logistic Regression & Decision Trees.

• HR Attrition Prediction exercise.

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Phase 6: Backend for Data Analytics (10 hours)

Day 22 & 23 – SQL for Data Analysis (4 hrs)

• SQL basics: SELECT, WHERE, GROUP BY, JOIN.

• Hands-on queries with employee & sales datasets.

Day 24 & 25 – Python for Data Analytics (4 hrs)

• Pandas, NumPy basics.

• Cleaning & analyzing CSV files.

Day 26 – Python Visualization (2 hrs)

• Matplotlib, Seaborn.

• Hands-on: Salary distribution visualization.

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Phase 7: Capstone Project & Presentation (6–8 hours)

Day 27 – Mini-Project Workshop (2 hrs)

• Students select dataset (HR, Retail, Finance, Health).

Day 28 – Hands-On Project Work (2 hrs)

• Cleaning, analysis, visualization.

Day 29 – Project Completion & Review (2 hrs)

• Final dashboards & predictions.

Day 30 – Presentations & Certification (2 hrs)

• Teams present insights to peers.

• Q&A + Feedback.

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✅ Total Hours: ~54 Hours (extendable to 60 with deeper Python/SQL labs)

🎯 Outcomes:

• Students master Excel, SQL, Power BI, Tableau, and Python basics.

• They understand statistics, probability, regression, and forecasting.

• They build real dashboards & predictive models.

• Industry-ready portfolio with a capstone project.