Module 1 – Introduction to Data Analytics (8 hours)
- What is Data Analytics? Types: Descriptive, Diagnostic, Predictive, Prescriptive
- Data Analytics vs Data Science vs Business Intelligence
- AI-powered analytics (Copilot in Excel, ChatGPT for BI)
- Real-time analytics with cloud data warehouses
- Augmented Analytics
- The Data Analytics workflow: Data → Analysis → Visualization → Decision-making
- Tools overview: Excel, SQL, Python, Power BI, Tableau, Google Looker
Hands-on:
- • Load & explore datasets in Excel and Python
- • Simple pivot tables & basic visualizations
Module 2 – Data Cleaning & Preparation (10 hours)
- Importance of clean data for analytics
- Handling missing, duplicate, and inconsistent data
- Data transformation: formatting, parsing, type conversions
- Merging & joining datasets (Excel & SQL)
- Latest trend: Automated data cleaning tools (OpenRefine, AI-based cleaning in Power BI)
Hands-on:
- • Clean a raw dataset in Excel and Python (Pandas)
- • Create a reproducible cleaning pipeline
Module 3 – Exploratory Data Analysis (EDA) & Visualization (12 hours)
- Understanding data distributions, trends, and outliers
- Basic statistics for analytics (mean, median, mode, variance)
- Visualization best practices: chart selection, colour usage
- Interactive dashboards (Power BI, Tableau)
- Latest trend: Natural Language to Visualization (Power BI Copilot, Tableau Ask Data)
Hands-on:
- • Build an interactive sales dashboard in Power BI
- • Analyse and visualize trends with Python (Matplotlib, Seaborn, Plotly)
Module 4 – Business Analytics & SQL for Data Analysis (12 hours)
- Role of SQL in analytics: CRUD operations, filtering, joins
- Aggregations, GROUP BY, HAVING, subqueries
- Window functions for advanced analytics
- Latest trend: Cloud SQL analytics (BigQuery, Snowflake)
- KPI creation & tracking in business dashboards
Hands-on:
- • Query a database for business metrics
- • Create a SQL-based monthly performance report
Module 5 – Advanced Analytics & Predictive Insights (12 hours)
- Introduction to predictive analytics & forecasting
- Time series analysis (Excel, Power BI, Python Prophet)
- Basic Machine Learning for analytics (regression, classification in Power BI & Python)
- Generative AI in analytics
- AI-powered recommendations in dashboards
Hands-on:
- • Forecast sales trends using Power BI and Python Prophet
- • Build a churn prediction dashboard with simple ML models
Module 6 – Capstone Project & Reporting (18 hours)
Project Theme:
- "Data-Driven Insights for Business Growth" – Students pick a domain (retail, finance, healthcare, etc.) to:
- 1. Gather & clean data (multiple sources allowed)
- 2. Perform EDA & statistical analysis
- 3. Create business KPIs
- 4. Build an interactive dashboard with storytelling elements
- 5. Present findings as actionable insights to stakeholders
Examples:
- • Retail sales performance & trend forecasting
- • Financial portfolio risk & return dashboard
- • Healthcare patient analytics & service optimization
Final Deliverables:
- • Dashboard + SQL scripts + PDF report
- • Presentation to class or panel
Assessment & Certification
- • Weekly Quizzes: 20%
- • Assignments: 30%
- • Capstone Project: 50%
- • Minimum 60% score for certification
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