Business Analytics

Curriculum

1. Week 1

   Introduction to Analytics

   Understand Business Analytics fundamentals, data-driven decision-making, business problems, KPIs, types of analytics and how to identify valuable datasets.

2. Statistical Foundations

   Learn statistics fundamentals, statistical thinking, probability concepts and how to identify patterns and trends to support better business decisions.

3. Data Collection and Management

   Learn how to collect data from different sources, organize datasets, manage business data, understand data warehousing and identify useful datasets for analysis.

4. Week 2

   Data Cleaning and Preprocessing

   Clean and prepare raw datasets, manipulate data, handle inconsistent information and transform datasets into analysis-ready formats.

5. Exploratory Data Analysis (EDA)

   Explore datasets using Python, NumPy and Pandas, perform data manipulation, create visualizations with Matplotlib and extract meaningful patterns and insights.

6. Predictive Analytics and Modeling

   Learn predictive analytics, supervised and unsupervised learning, classification, regression, model training, fine-tuning and forecasting future outcomes.

7. Week 3

   Prescriptive Analytics and Optimization

   Move from predictions to actions by applying optimization techniques to improve revenue, customer outcomes, markets and business decision-making.

8. Data Visualization and Storytelling

   Create dashboards and reports, visualize business data effectively and communicate analytical insights through clear and compelling data stories.

9. Programming and Analytics Tools

   Work with Python, NumPy, Pandas, Matplotlib, Excel and SQL to analyze datasets, manipulate data and build practical business intelligence solutions.

10. Week 4

    Big Data and Cloud Analytics

    Understand big-data concepts, large-scale datasets, data warehousing, business intelligence and the fundamentals of cloud-based analytics.

11. Domain-Specific Applications

    Apply analytics to finance, marketing, operations, product and customer analytics while tracking business performance and KPIs.

12. Ethics, Data Governance and Security

    Learn responsible data usage, data privacy, governance, data quality, data security and best practices for protecting business information.

13. Real-World Case Studies and Projects

    Solve practical business problems involving sales forecasting, customer churn, financial risk, HR analytics, marketing optimization, fraud detection, recommendations and logistics.

What you walk away with

• A portfolio of practical analytics projects using real-world datasets, dashboards and business case studies
• Hands-on experience with Excel, SQL, Python, NumPy, Pandas, Matplotlib and business intelligence tools
• Industry-ready skills in data analysis, visualization, predictive analytics, business storytelling and data-driven decision-making