If you are planning a career in data, one of the first questions you may have is: what will I actually study? A clear business analytics syllabus answers that question. It shows you the exact tools, topics, and skills you need, and the order in which you should learn them.
This guide breaks down the complete syllabus milestone by milestone. You will see what is taught each week, which tools you learn, and which real company projects you build along the way.
You will also find a UG-level and PG-level structure, so you know what to focus on at your stage. Finally, you get answers to the most common questions students ask before joining a course. Let us start with the basics.
What is Business Analytics?
Business analytics is the practice of using data to make better business decisions. It combines data collection, cleaning, analysis, and visualization to answer real questions such as why sales dropped, which customers are leaving, and where the company should invest next.
Professionals working in business analytics use tools such as Excel, SQL, and Power BI to turn raw data into actionable insights. These insights help managers choose the right action instead of guessing.
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Business Analytics Course Syllabus: WsCube Tech
Our business analytics course syllabus is an 18-week program built with inputs from industry hiring managers. It covers five milestones; the program includes 4+ portfolio projects, 8+ case studies, 12+ assignments, and a 4-week internship.
The structure is simple. The first 14 weeks teach you business strategy, Excel, SQL, Power BI, and GenAI. The last 4 weeks put you inside a real internship where you work as a Business Analyst Intern. You can check the complete program details on the business analytics course page.
One thing to notice before you read further. The milestones are not five separate courses stacked together. Each one feeds the next. You frame a business problem in Milestone 1, clean and explore the data for it in Milestone 2, pull the real numbers in Milestone 3, present them in Milestone 4, and then do all four steps for a live company problem in Milestone 5. That order matters, because analysts are usually rejected in interviews for weak business thinking, not weak tool knowledge.
Here is a quick view of the full business analytics course curriculum before we go deep into each milestone.
| Milestone | Focus Area | Duration |
| Milestone 1 | Understanding Business Strategy and Metrics | 4 Weeks |
| Milestone 2 | Get Started with Excel | 3 Weeks |
| Milestone 3 | Programming with SQL | 3 Weeks |
| Milestone 4 | Data Visualization and GenAI for Business Analytics | 4 Weeks |
| Milestone 5 | Business Analytics Internship | 4 Weeks |
| Bonus Modules | Tableau, Statistics, Database Design, Fabric, GA4 | Self-paced |
Milestone 1: Understanding Business Strategy and Metrics (4 Weeks)
This milestone teaches you how to think like a business analyst before you touch a single tool. You learn to understand systems, connect business goals to product outcomes, discover user needs through structured research, and turn those insights into clear requirements. It includes 13 live sessions, 1 project, multiple case studies, 4 cheat sheets, and 4 quizzes.
Week 1: Systems Thinking and Outcome Mapping for Product Strategy
- Introduction to Systems Thinking
- System Structure and Organizational Systems
- Feedback Loops and System Dynamics
- Systems Thinking in Digital Products
- Aligning Business Goals with Product Outcomes
- Product Metrics and Outcome Measurement
- Outcome Mapping and Opportunity Prioritization
Week 2: Opportunity Discovery Through User Research and Problem Framing
- Opportunity Discovery and Opportunity Solution Trees
- Opportunity vs Feature Thinking
- Problem Definition and Opportunity Sizing
- Introduction to User Research
- User Research Types and Methods
- Conducting User Research (Interviews, Surveys, Observation)
- Research Synthesis, Bias, and Ethics
Week 3: Problem Framing, SDLC, and Requirements Gathering
- SDLC phases
- Waterfall and Agile Principles
- Kanban vs Scrum
- Types of Product Requirements
- Requirement Gathering
- Product Requirement Documentation and User Stories
- Story Mapping and Requirement Structuring
- User Story Quality, Prioritization, and Stakeholder Alignment
Week 4: Jira, Product Metrics, and Analytics
- Requirements Workshop Lab (Jira)
- Introduction to Product Metrics and Measurement
- Types of Product Metrics and the AARRR Framework
- Product Analytics Fundamentals
- Funnel, Retention, and Cohort Analysis
- Feature Adoption and Experimentation (A/B Testing)
- Hypothesis-Driven Analysis and Data-Informed Decision Making
This is also the stage where you understand descriptive, diagnostic, predictive, and prescriptive analysis. If that sounds new, this guide on types of business analytics explains each one with simple examples.
Project: Strategic Product Analysis at Urban Company. You act as a business analyst at a home services platform where repeat bookings are falling, and customer acquisition cost is rising. You map the system behind booking behavior, frame the core problem, define requirements, and build a metrics framework.
Case studies: Connecting Business Outcomes at Swiggy, Opportunity Discovery at IKEA, Requirements Workshop at PhonePe, and Data-Driven Decisions at Meesho.
The milestone also includes a Data Storytelling Fest, where you turn raw data into a story using Power BI or Tableau and compete with your batch to present the sharpest insight.
By the end of these four weeks, you can take a vague business complaint such as “our repeat orders are falling” and turn it into a defined problem, a measurable outcome, a set of requirements, and a metrics framework. That is the actual day of one job as a business analyst.