Short answer: The ASTI Data Analytics course in Bangalore has 9 modules plus projects and interview preparation. You start with business statistics, Excel and SQL, then build dashboards in Tableau and Power BI, and finish with Python for data analysis. It takes about 4 months, in the BTM 1st Stage classroom or live online, and you do not need prior programming experience.
This page explains what each module covers, why the modules come in this order, and what you should be able to do at the end. It follows the syllabus published on the Data Analytics course page. If the two ever differ, the course page is the current version.
Data analytics syllabus at a glance
| Module | Topic | What you will be able to do |
|---|---|---|
| M1 | Business Statistics & Foundations | Describe data with averages, spread and distributions, and explain what a sample can and cannot tell you |
| M2 | Introduction to Data Analytics | Tell descriptive, diagnostic, predictive and prescriptive analytics apart, and follow an analysis from question to answer |
| M3 | Excel for Data Analysis | Clean data, use lookups and pivot tables, and build quick charts for a business question |
| M4 | SQL for Data Analytics | Write SELECT queries with filters, sorting, grouping and joins |
| M5 | Advanced SQL & Data Handling | Use subqueries and more complex joins, and prepare clean tables for reporting |
| M6 | Tableau: Data Visualisation | Connect Tableau to data and choose the right chart for the question |
| M7 | Tableau Dashboards & Server | Combine charts into interactive dashboards and share them |
| M8 | Power BI | Load and shape data, build a data model, write basic DAX and publish a report |
| M9 | Python for Data Analytics | Use Python to load, clean and summarise data when spreadsheets stop scaling |
| Final | Projects & Interview Preparation | Complete an end-to-end project you can explain in an interview |
Why the modules come in this order
Each module uses the one before it. The order is the same one a junior analyst follows at work.
- Statistics first. Before you build any chart, you need to know whether a difference in the numbers is real or just noise. M1 and M2 give you that judgement.
- Excel, then SQL. A lot of business data still passes through spreadsheets, so Excel is where you practise cleaning and summarising. SQL comes next because company data usually lives in databases, and SQL is how analysts get it out.
- Two visualisation tools. Analyst job posts commonly name Tableau, Power BI or both. Learning both keeps more roles open, and the concepts carry over from one tool to the other. Check current posts from the employers you are targeting to see which they use.
- Python last. Python is the step up for larger or messier datasets and for repeatable analysis. By the time you reach M9 you already know what you want the code to do.
- Projects to finish. The final project puts every stage together, from a raw dataset to a dashboard and a short explanation of what it shows.
- AskDefine the business question
- Get dataExcel and SQL
- CleanExcel, SQL and Python
- AnalyseStatistics
- ShowTableau and Power BI
Module details
M1–M2: Statistics and analytics foundations
You learn types of data, measures of central tendency and spread, distributions and the basics of sampling. You also learn the four types of analytics and how an analysis moves from a question to a recommendation.
M3: Excel for data analysis
This module covers sorting and filtering, text and date cleaning, conditional formatting, lookup functions, named ranges and tables, and pivot tables and charts.
M4–M5: SQL
You write queries with WHERE, GROUP BY, HAVING and ORDER BY, combine tables with joins, and use subqueries. You also practise turning a business question into a query and checking that the result is correct.
M6–M7: Tableau
You connect to data sources, build common chart types, add calculated fields and filters, and design dashboards for a specific audience. You also learn how dashboards are shared through Tableau Server.
M8: Power BI
You use Power Query to get and transform data, build a data model with relationships, write DAX measures, design reports and publish them to the Power BI service. These skills overlap with the Microsoft PL-300 exam. Our PL-300 certification guide covers the exam itself.
M9: Python for data analytics
You learn Python basics, then use Python to load, clean, group and summarise data. The aim is practical analysis, not software development.
Projects and interview preparation
You complete practical assignments throughout the course and a final project. Interview preparation covers explaining your project, SQL and Excel questions, and dashboard walkthroughs.
Who this syllabus suits
- Students and fresh graduates from any stream, including non-IT
- Working professionals who already handle reports in Excel
- Career switchers who want an entry point into data roles
If you are unsure whether to choose data analytics or data science, read data analytics vs data science first. For the career path itself, see how to become a data analyst.
What changed in this syllabus
We rewrote this page in October 2026 to match the current course. The older version listed 28 short modules, and some of them were mislabelled. It also described features that are no longer part of Power BI, such as the Cortana integration. The current course groups the same subjects into 9 modules and adds Python and a final project.
Frequently asked questions
How long is the ASTI data analytics course?
About 4 months. Weekday and weekend batches are offered, in the classroom at BTM 1st Stage or live online. A career advisor can confirm the dates of the next batch.
Do I need coding experience?
No. The course starts with statistics and Excel. SQL and Python are taught from the basics.
Does the course cover both Power BI and Tableau?
Yes. Tableau is covered in M6 and M7, and Power BI in M8.
Will I get a certificate?
You receive an ASTI course completion certificate. Vendor certifications such as Microsoft PL-300 are separate exams that you book with the vendor.
Is placement guaranteed?
No. ASTI provides placement assistance, including resume building, mock interviews and job opportunities. Hiring decisions are made by employers. Placement assistance does not guarantee a job.