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.
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Milestone 2: Get Started with Excel (3 Weeks)
Excel is where hands-on analysis begins. This milestone takes you from basic formatting to full dashboards, with AI support built into the workflow. It includes 10 live sessions, 1 project, 2 case studies, 3 cheat sheets, and 3 quizzes.
Week 1: Introduction to Data Analytics and Excel I
- Introduction to Data Analytics
- Basic Features in Excel
- Formatting in Excel
- Dealing with Raw Data
- Functions in Excel
- AI Prompting for Excel formulas
- AI-assisted data cleaning
Week 2: Deep Dive with Excel II
- Data Connectors in Excel
- Cleaning in Power Query Editor
- Adding Conditional Columns using Power Query Editor
- Data Modeling and its Importance
- Cardinality and Filter Direction in Power Pivot
- Using ChatGPT or Gemini to generate Power Query M scripts
Week 3: Master Advanced Excel III
- Pivot Tables in Excel
- Charts in Excel
- Slicers in Excel
- Measures in Excel
- Creating a Dashboard in Excel
- AI Visual Recommendation
- Dashboard Summary Insight
- Narration with AI
- InVideo and Descript using AI
Project: Tata Power EV Charging Network Expansion Analysis. You build an EV infrastructure planning dashboard in Excel. You study vehicle density, mobility patterns, charging demand, corridor coverage, installation cost, and revenue potential, then recommend where the next charging stations should go.
Case studies: Analysis of Myntra Apparel and Audible Data Cleaning.
You also get an Excel interview prep session with an industry expert, where you practice the exact type of questions asked in analytics interviews.
Students often underrate this milestone because Excel feels familiar. In real analytics teams, it is still the fastest tool for a quick answer, and Excel remains an important tool for quick analysis, reporting, and many practical analytics tasks. By the end of these three weeks, you can take a messy file with blank rows and wrong data types, clean it in Power Query, model it, and hand back a dashboard that a manager can filter on their own.
Milestone 3: Programming with SQL (3 Weeks)
SQL is the language you use to pull data out of company databases. This milestone moves from basic queries to window functions and stored procedures. It includes 10 live sessions, 1 project, 1 case study, 3 cheat sheets, and 3 quizzes.
Week 1: Welcome to MySQL
- Introduction to MySQL
- Basic MySQL Syntax
- Clauses in MySQL
- Operators in MySQL
- Dealing with NULL Values in MySQL
- AI for SQL query generation
- AI debugging of SQL errors
- AI auto-documentation of queries
Week 2: Advanced SQL Queries and Functions in MySQL
- Functions in MySQL
- Case Operator in MySQL
- Group By in MySQL
- Having Clause in MySQL
- Joins in MySQL
- Query Optimization using AI
Week 3: Advanced SQL Concepts and Techniques in MySQL
- Subqueries in MySQL
- Union, Intersect, and Except in MySQL
- Stored Procedures in MySQL
- Common Table Expressions (CTE)
- Window Functions in MySQL
- Stored procedure templates using AI
- Debugging window functions with AI
Project: Amazon Sales Data Analysis Using SQL. You analyze sales transactions, customer orders, product performance, regional trends, and revenue metrics using joins, aggregations, and subqueries.
Case study: Swiggy Analysis Using SQL, where you write complex joins to support strategic decisions.
A dedicated SQL interview prep session with an industry expert closes this milestone.
SQL is the one topic in this business analytics syllabus that you cannot skip. As datasets grow beyond what is practical to manage comfortably in spreadsheets, SQL becomes essential for querying and analyzing data stored in databases. By the end of these three weeks, you can join multiple tables, rank results with window functions, and write a query that answers a business question without asking an engineer for help.
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Milestone 4: Data Visualization and GenAI for Business Analytics (4 Weeks)
This is where your analysis becomes clear, interactive, and decision-ready for business stakeholders. You learn Power BI, DAX, dashboard design, and AI-powered automation. It includes 13 live sessions, 2 projects, 1 case study, 4 cheat sheets, and 4 quizzes.
Week 1: Data Visualization with Power BI
- Introduction to Power BI
- Data Connectors
- Power Query Editor and Tools
- Append Queries and Merge Queries
- Pivoting and Unpivoting of data
- Exploring datasets with AI
Week 2: Advanced Data Modeling and DAX in Power BI
- Data Modeling and Cardinality
- Cross Filter Direction
- Measures vs Calculated Columns
- Functions in DAX
- Cumulative Sales and Moving Average Using DAX
- AI natural language to DAX
- AI anomaly detection for trends
- DAX optimization using AI
- Quick YoY, MoM, and rolling average with AI
Week 3: Visualizations and Dashboard Creation in Power BI
- ChatGPT for Measures
- Column Charts and Slicers
- Matrix vs Tables
- Cards, KPI, and Gauge Chart
- Formatting a Dashboard
- AI-generated DAX and explanations
- Smart Narratives for auto insights
Week 4: AI Automation and Workflow
- How LLMs Actually Work
- The Art of Prompt Engineering
- RAG, Fine Tuning, and Model Optimization
- AI-Powered Workflow Automation
- Setting up smart checks and alerts
- Generating insights with AI-driven flows
Projects: Olympic Games Athlete Performance and Medal Analysis, where you build a sports performance dashboard in Power BI. And Unilever Multi-Layer Workflow Automation, where you build an automated pipeline that cleans raw data, creates dashboards, and shares reports without manual work.
Case study: Financial Performance Analysis Using Power BI and DAX, covering running totals, moving averages, YTD, MTD, YoY growth, and variance analysis.
This milestone ends with a second Data Storytelling Fest, where you present your dashboard as a business narrative to mentors and peers.
The GenAI week is what makes this business analytics course curriculum different from an older syllabus. Companies are no longer hiring people to build reports. They want analysts who can automate the boring half of the job and spend their time on the thinking half. By the end of these four weeks, you have a working automated pipeline in your portfolio, not just a set of charts.
Milestone 5: Business Analytics Internship (4 Weeks)
The final milestone is a 4-week internship where you work as a Business Analyst Intern at WsCube Tech. You apply everything from the earlier milestones to live projects that actually influence decisions.
Week 1: Business Discovery and Stakeholder Analysis
- Conduct stakeholder interviews and requirement-gathering sessions
- Apply systems thinking to map real business problems
- Use Excel to organize, clean, and structure raw business data
- Identify key metrics and define the North Star for the assigned project
Week 2: Data Extraction and Process Analysis
- Write SQL queries to extract and analyze real business datasets
- Identify patterns, gaps, and inconsistencies in company data
- Perform opportunity discovery using structured research methods
- Prepare data-backed reports and gap analysis for stakeholders
Week 3: Dashboard Design and Requirements Documentation
- Build interactive dashboards in Power BI or Tableau for business teams
- Translate findings into clear functional requirements and user stories
- Design visuals for non-technical audiences with clarity and relevance
- Align solution design with business outcomes and stakeholder needs
Week 4: Insights and Stakeholder Presentation
- Consolidate work from all previous weeks into a unified business report
- Craft a data-driven business narrative and recommendation deck
- Present findings and dashboards to WsCube Tech stakeholders
- Demonstrate end-to-end BA skills across research, data, visuals, and strategy
This milestone is what separates a certificate from real experience. By the end of it, you have stakeholder conversations, SQL work, dashboards, and a final presentation on your resume. These are the exact business analyst skills recruiters looking for in a fresher.
Bonus Modules
Along with the core milestones, you get bonus modules that keep you current with the tools hiring teams to keep adding to job descriptions.
1. Data Visualization with Tableau:
- Overview of Tableau Software
- Installation and Setup
- Tableau Workspace and User Interface
- Connecting to Data Sources (Excel, CSV, Database)
- Bar Charts, Line Charts, and Pie Charts
- Histograms and Scatter Plots
- Understanding Marks
- Using Rows, Columns, Filters, and Pages
- Sorting, Grouping, and Filtering Data
- Using Colors, Labels, and Tooltips
An advanced Tableau module then takes you into data storytelling, so you can present a dashboard as a narrative and not just a screen full of charts.
2. Maths and Applied Statistics:
- Introduction to Statistics
- Descriptive Statistics
- Hypothesis Testing
- AB Testing
- Fundamentals of Probability
- AI sample test scenarios
- AI-driven simulations for visualization
3. Database Design and Architecture (MySQL):
- Introduction to Database Design
- Understanding Data Models (Conceptual, Logical, Physical)
- Entity-Relationship (ER) Diagram Basics
- Normalization and Denormalization Concepts
- Primary Keys, Foreign Keys, and Relationships
- Designing a Simple Database Schema in MySQL
4. Microsoft Fabric:
- Unified analytics with the Microsoft Fabric ecosystem
- Integrating Power BI, Lakehouse, and Data Factory
- Modern Fabric architecture and Dataflow Gen2
- Building and managing a Lakehouse with Delta Tables
- Running a real-world Fabric project end-to-end
- Migrating legacy dashboards to Fabric
- Automating pipelines and data refresh workflows
- Enabling real-time insights with Power BI and Fabric
5. Google Analytics 4 (GA4) for Data Analysts:
- Introduction to Google Analytics 4
- Setting Up GA4 Property and Data Streams
- Understanding Events, Parameters, and User Data
- Exploring Reports and Key Metrics (Engagement, Retention, Traffic)
- Connecting GA4 with BigQuery and Power BI
- Building Dashboards and Interpreting Insights
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Business Analytics Syllabus: UG Program
The PG-level business analytics syllabus focuses on advanced analytics, business decision-making, and practical applications through industry-relevant tools and projects. Present below are the tables that will assist you in knowing the syllabus better.
UG Business Analytics Program — Syllabus by Year
Year 1
| Semester | Subjects Taught (Typical) | Skills Taught (Typical) |
| Semester 1 | Introduction to Business Analytics; Business Mathematics; Programming for Analytics (Python) | Analytical thinking; basic Python; Excel; problem framing |
| Semester 2 | Business Statistics I; Data Management & SQL; Communication for Business | Descriptive stats; SQL queries; data cleaning; business communication |
Year 2
| Semester | Subjects Taught (Typical) | Skills Taught (Typical) |
| Semester 3 | Business Statistics II; Predictive Analytics I (Regression); Marketing Analytics | Inferential stats; regression modeling; segmentation; campaign analysis |
| Semester 4 | Predictive Analytics II (Classification/Clustering); Operations Analytics; Elective I | ML basics; process/KPI analytics; domain specialization (e.g., finance/HR) |
Year 3
| Semester | Subjects Taught (Typical) | Skills Taught (Typical) |
| Semester 5 | Financial & Risk Analytics; Data Visualization (Tableau/Power BI); Elective II | Financial modeling; dashboard design; domain-depth |
| Semester 6 | Capstone Project; Internship/Industry Project; Ethics & Data Governance | End-to-end analytics project; stakeholder presentation; responsible AI/data |
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Business Analytics Syllabus: PG Program
The PG-level business analytics syllabus combines advanced analytics, strategic decision-making, and practical skills with industry-relevant tools and hands-on projects. Given below is a table-wise structure that helps you to understand easily.
PG Business Analytics Program — Syllabus by Year
Year 1
| Semester | Subjects Taught (Typical) | Skills Taught (Typical) |
| Semester 1 | Advanced Business Statistics; Data Management & SQL for Analytics; Python for Data Science | Advanced stats; complex SQL; Python (pandas, numpy, scikit-learn) |
| Semester 2 | Machine Learning for Business; Marketing & Customer Analytics; Elective I | Supervised/unsupervised ML; RFM/churn; domain focus (marketing/ops/finance) |
Year 2
| Semester | Subjects Taught (Typical) | Skills Taught (Typical) |
| Semester 3 | Financial & Risk Analytics; Operations & Supply Chain Analytics; Elective II | Forecasting, credit/fraud risk; inventory/process optimization |
| Semester 4 | Data Visualization & Storytelling; Capstone Project; Business Research & Ethics | Tableau/Power BI dashboards; end-to-end project; research design & governance |
FAQs About Business Analytics Syllabus
If you're wondering is business analytics hard to learn, the answer is no. Begin with fundamentals and slowly learn analytics tools. With hands-on practice, business analytics becomes manageable.
Key subjects include business strategy, requirements gathering, Excel, SQL, data visualization, Power BI, statistics, and GenAI. These subjects build both analytical and business decision-making skills.
Yes. Excel is a core part of the syllabus and covers data cleaning, formulas, Power Query, Pivot Tables, charts, data modelling, and dashboard creation. The curriculum also introduces AI-assisted Excel workflows.
Yes. The SQL module starts with MySQL fundamentals and progresses to joins, subqueries, CTEs, stored procedures, and window functions. You also work on a practical SQL project using sales data.
Yes. The curriculum covers Power BI, data connectors, Power Query, data modelling, DAX, interactive visualizations, dashboards, and AI-assisted analytics. Tableau is also included as a bonus module.
Yes. Statistics is covered through the bonus modules, including descriptive statistics, probability, hypothesis testing, and A/B testing. These concepts also support experimentation and data-driven decision-making.
Yes. AI and GenAI are integrated throughout the curriculum rather than being taught as a separate topic only. Students learn AI-assisted Excel, SQL generation and debugging, DAX assistance, prompt engineering, RAG, and workflow automation.
Yes. The curriculum includes practical projects based on business scenarios such as Urban Company, Tata Power, Amazon, Olympic Games, and Unilever. These projects help learners apply the tools and concepts covered in each milestone.
Yes. Prior technical experience is not required for the program. The syllabus introduces Excel and other concepts from the basics before progressing to SQL, Power BI, data analysis, and AI workflows.
The core tools include Excel, Power Query, MySQL, Power BI, DAX, and Jira. Bonus modules add Tableau, Microsoft Fabric, GA4, database design, and statistics to the learning path.
Yes. The curriculum combines analytical tools with business skills such as problem framing, opportunity discovery, user research, requirements gathering, stakeholder alignment, product metrics, and data storytelling.
Depending on your skills and experience, you can explore roles such as Business Analyst, Data Analyst, Business Intelligence Analyst, Product Analyst, Operations Analyst, Marketing Analyst, and Analytics Consultant.
Yes. Working professionals can use the curriculum to strengthen analytical, stakeholder, and decision-making skills while building practical projects. The PG structure also places greater emphasis on strategy, business context, and AI.
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