Sales performance dashboard
Plan the problem, build the solution, review your work and practice explaining it.
Career-focused program
Data Analytics Course
Learn Python, MySQL, Power BI, Tableau and Advanced Excel for data cleaning, analysis, dashboards and business insight projects.
Who it is for
A structured path for learners who want to build.
- Students exploring data careers
- Graduates and professionals who work with business data
- Career switchers targeting analyst roles
BY THE END, YOU SHOULD BE ABLE TO
- Clean and organize business data
- Write SQL queries for analysis
- Build Power BI dashboards with KPIs
- Use Python/Pandas for repeatable analysis
- Explain findings with business context
Tools covered
Learn a connected technology stack.
PythonNumPyPandasMatplotlibSeabornMySQLPower BITableauAdvanced Excel
Detailed syllabus
Learn in modules, not a random list of topics.
Each module combines concepts, practice and a milestone. Exact lesson order can change by batch as the curriculum is updated.
- 5Core modules
- 65+Topics
- 1Capstone project
01Data Analytics FoundationsIntroduction, data collection, cleaning and exploration3 topics
- Introduction to Data Analytics
- Data Collection & Cleaning
- Data Exploration
02Python ProgrammingCore Python, OOP, files, exceptions and data libraries40 topics
Core Python10
- What is Python and its advantages
- Basic Python syntax
- Data types & variables
- Typecasting
- Operators in Python
- input() & output with print()
- Single-line & multi-line comments
- Conditional statements (if, elif, else) & nested conditions
- Loops (while loop & for loop)
- break, continue and pass in loops
Collections4
- List
- Set
- Tuple
- Dictionary
Functions5
- Defining functions
- The def keyword
- Parameters & return values
- Function arguments
- Lambda functions
Strings2
- String indexing, slicing & operations
- String methods & formatting
Object Oriented Programming5
- Classes & objects
- Defining methods within a class
- Inheritance
- Encapsulation & abstraction
- Polymorphism
Exception Handling5
- Understanding errors and exceptions
- Syntax errors vs runtime errors
- Handling exceptions
- Custom exception classes
- Best practices in exception handling
File Handling5
- Working with files
- Opening & closing files using open()
- Reading & writing text files
- File operations
- File modes & manipulation (r, w, a etc.)
Python Libraries4
- NumPy
- Matplotlib
- Pandas
- Seaborn
03MySQL DatabaseQueries, joins, window functions and interview problems7 topics
- Difference between DBMS & RDBMS
- DDL and DML queries
- GROUP BY, ORDER BY & subqueries
- Joins (inner, left outer & right outer)
- Window functions & views
- Constraints & data types in MySQL
- Real interview-oriented SQL problems
04Power BI & TableauData modeling, Power Query, DAX and corporate dashboards8 topics
- Power BI architecture
- Data modeling concepts
- Power Query for data cleaning
- DAX functions
- Measures vs calculated columns
- Interactive dashboards
- Real corporate-style dashboards
- Dashboard design
05Advanced ExcelLookups, pivots, dynamic dashboards and macros7 topics
- Excel for analytics vs normal Excel
- Data cleaning techniques
- IF, nested IF, VLOOKUP, HLOOKUP, XLOOKUP & INDEX-MATCH
- Logical, statistical & text functions
- Pivot tables & pivot charts
- Dynamic dashboards in Excel
- Macros
06Capstone AnalysisEnd-to-end dataset, dashboard and insight presentationProject
- Choose a real-world dataset
- Clean and analyse it with Python & SQL
- Build an interactive dashboard
- Present insights and recommendations
Project work
Turn modules into portfolio evidence.
Customer/order SQL analysis
Plan the problem, build the solution, review your work and practice explaining it.
End-to-end business analytics capstone
Plan the problem, build the solution, review your work and practice explaining it.
Career trajectories
Roles this learning path can support.
Data Analyst TraineeMIS / Reporting AnalystBI Analyst TraineeJunior Business Data Analyst
Role suitability depends on your completed skills, projects, prior background, interview performance and employer requirements. These are learning-path examples, not job guarantees.
Schedule & fees
Get the current batch details.
Batch scheduleAsk admissions for current weekday/weekend availability.
Fees & EMICurrent fees and EMI availability are confirmed before admission.
Learning modeConfirm current classroom/online options for your batch.
Frequently asked
Data Analytics course FAQs
The listed level is Beginner. Admissions can help you compare your current level with the program prerequisites before you enroll.
Yes, the learning design is project-based. Ask for the current batch project list because project scope can evolve with the curriculum.
Career guidance can include resume, portfolio, interview and job-search support. Employment should not be treated as guaranteed unless the academy provides a specific written guarantee.
Course guidance
Want the current syllabus, fee and batch schedule?
Speak with admissions before enrolling so you can confirm the latest curriculum and learning format.