Goal: Build strong fundamentals in business data understanding, statistical tools, and visualization basics.

Week 1: Introduction to Business Analytics

• Importance of analytics in business decision-making

• Key domains (finance, marketing, operations, etc.)

• Types of analytics: descriptive, diagnostic, predictive, prescriptive

Week 2: Data Literacy and Sources

• Types of data: structured vs unstructured

• Internal vs external data sources

• Data quality, cleaning basics, and data ethics

Week 3: Basic Statistics for Business

• Mean, median, mode, variance, standard deviation

• Probability basics, distributions, and data patterns

• Application of statistics in business scenarios

Week 4: Excel for Business Analytics

• Lookup functions, logical formulas, advanced charts

• Pivot tables and dashboards

• Scenario and what-if analysis

Week 5: SQL Fundamentals

• Writing simple to advanced queries

• Joins, subqueries, aggregations

• Query optimization tips for analysts

Week 6: Introduction to BI Tools (Tableau/Power BI)

• Connecting to data sources

• Creating basic dashboards and charts

• Visual best practices and storytelling

Week 7: Data Mining and ETL Fundamentals

• ETL process: extract, transform, load

• Data wrangling with open-source tools

• Clustering, classification, and association basics

Week 8: Mini Project

• End-to-end analysis on a chosen dataset

• Build Excel dashboards, run SQL queries, create BI visualizations

• Present insights and business implications

Goal: Dive deep into predictive models, advanced tools, and statistical methods.

Week 9: Advanced Excel & SQL for Analytics

• Power Query and Power Pivot

• Window functions, CTEs, stored procedures

• Automating reports and dashboards

Week 10: Advanced Data Visualization & Storytelling

• Dynamic dashboards with Tableau/Power BI

• Drilldowns, tooltips, filters

• Narrative storytelling with data

Week 11: Statistical Modeling for Business

• Linear and logistic regression

• Hypothesis testing and confidence intervals

• Business applications: pricing, churn prediction, etc.

Week 12: Predictive Analytics & Machine Learning Basics

• Forecasting methods: ARIMA, exponential smoothing

• Intro to supervised learning: classification and regression

• Use cases in business strategy

Week 13: Python/R for Business Analytics

• Data manipulation using Pandas or dplyr

• Visualizations using matplotlib/seaborn or ggplot

• Basic machine learning models

Week 14: Data-Driven Decision Making

• Decision trees, simulations, and sensitivity analysis

• Scenario planning with business impact modeling

• Communicating insights to non-technical stakeholders

Week 15: Financial Analytics

• Revenue, profitability, and cost models

• Lifetime value (LTV), customer acquisition cost (CAC)

• Optimization and forecasting using financial data

Week 16: Mid-Term Project

• Real-world dataset analysis

• Business objective, data prep, modeling, visualization

• Executive summary + presentation

Goal: Apply all concepts to a real-world business problem and gain industry readiness.

Weeks 17–18: Capstone Planning

• Select business domain (finance, retail, logistics, etc.)

• Define business problem, objectives, and success metrics

• Identify data sources and outline data pipeline

Weeks 19–20: Execution – Data Collection & Cleaning

• Scrape, query, or ingest data from APIs or databases

• Clean, merge, and transform data using Python/R

• Document assumptions and methodology

Weeks 21–22: Modeling, Dashboards & Insights

• Develop advanced visualizations and dashboards

• Build predictive models and evaluate accuracy

• Provide recommendations and scenario forecasts

Weeks 23–24: Final Presentation & Portfolio

• Final report with methodology, business impact, and ROI

• Prepare a presentation deck and pitch to a panel

• Build a portfolio with case studies, dashboards, and GitHub code