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Syllabus

Data Analyst Course in Nagpur — full syllabus

Every module, every topic, every tool and the hours behind each. Published in full so you can compare it against any other institute in Nagpur before you pay anyone.

Modules
7
Topics
36
Tools
16
Projects
4
Duration
6 months
Prerequisites
None. The course opens with SQL and Excel and introduces Python gradually.

Tool stack

Everything you will touch

Named in full, because a syllabus that says “BI tools” is hiding something.

  • MySQL
  • MySQL Workbench
  • Microsoft Power BI
  • Tableau Desktop
  • Python
  • pandas
  • NumPy
  • Jupyter
  • Excel
  • Microsoft Excel
  • Power Query
  • VBA
  • AWS / Azure
  • GitHub
  • LinkedIn

Module by module

The whole thing, in order

Modules run in this sequence deliberately. Each one assumes the last, which is why the Excel or foundations block comes before anything harder.

01

Introduction to data analysis

Weeks 1–2

Topics

  • What data and data analytics are, and the global scope
  • Analytics across domains, and the BI landscape
  • Understanding stakeholder vision and asking effective questions
  • Data Analyst vs Business Analyst, and the analyst road map
  • Types and methods of data analysis, and the analysis process

Tools

You finish with

Frame a real business question and design the analysis around it

02

SQL and databases

Weeks 3–7

Topics

  • DBMS vs RDBMS, MySQL setup, DDL/DML/DQL/TCL/DCL
  • Constraints, keys, operators and set operators
  • All join types, subqueries and correlated subqueries
  • CTEs, recursive CTEs, and window functions (rank, lead/lag, running totals)
  • Stored procedures, functions, views, cursors and triggers
  • Data modelling, ER diagrams and normalisation (1NF–BCNF)

Tools

  • MySQL
  • MySQL Workbench

You finish with

Answer a set of business questions against a normalised retail database

03

Power BI and Tableau

Weeks 8–12

Topics

  • Power Query editor, connections and transformations
  • Full visual library, data modelling and relationships
  • DAX: aggregation, filter, relationship, date and time-intelligence functions
  • Report filters, drill-through, row-level security and publishing
  • Tableau: connections, in-built and advanced charts, calculations and filters

Tools

  • Microsoft Power BI
  • Tableau Desktop

You finish with

Build a stakeholder-ready executive dashboard and present it live

04

Python for data analysis

Weeks 13–16

Topics

  • Python fundamentals, data structures, loops and functions
  • Exception handling, classes and objects
  • NumPy arrays, broadcasting and universal functions
  • pandas for reading, cleaning and manipulating data
  • Matplotlib and Seaborn for statistical and exploratory plots

Tools

  • Python
  • pandas
  • NumPy
  • Jupyter

You finish with

End-to-end exploratory analysis of a public dataset, written up as a notebook

05

Applied statistics

Weeks 17–19

Topics

  • Descriptive and inferential statistics
  • Population vs sample, variables and distributions
  • Hypothesis testing, Type I and Type II errors
  • T-test, ANOVA and Chi-square
  • Covariance and correlation

Tools

  • Python
  • Excel

You finish with

Design and evaluate a hypothesis test on a real dataset

06

Advanced Excel

Weeks 20–22

Topics

  • Lookup family — VLOOKUP, HLOOKUP, XLOOKUP, INDEX/MATCH, OFFSET
  • Data validation, PivotTables and slicers
  • Dashboard creation and formatting
  • Power Query and Power Pivot
  • Macros and VBA — subs, ranges, conditions and loops

Tools

  • Microsoft Excel
  • Power Query
  • VBA

You finish with

Build an interactive Excel dashboard from a raw export

07

Cloud bonus, capstone and corporate grooming

Month 6

Topics

  • Introduction to cloud services (AWS / Azure)
  • End-to-end capstone project
  • Resume and LinkedIn rebuild, GitHub portfolio
  • Three recorded mock interviews and aptitude practice
  • Salary negotiation

Tools

  • AWS / Azure
  • GitHub
  • LinkedIn

You finish with

A defended end-to-end capstone with written feedback

Portfolio

The four projects you leave with

Documented, reviewed and defended out loud. This is the part of the syllabus that gets you interviews.

Retail sales performance dashboard

Clean a messy multi-region sales export, model it, and build a Power BI dashboard a regional manager could actually run a meeting from.

Tools
Power Query, Power BI, DAX
Dataset
Synthetic retail transactions, 40k rows

Customer churn exploration

Work out which customers leave and why, using SQL to segment and Python to visualise. Ends in a written recommendation, not just a chart.

Tools
MySQL, Python, pandas
Dataset
Telecom churn, public

Statistical analysis report

Read a dataset properly — sample, significance, and the honest answer when the result is inconclusive.

Tools
Python, Excel
Dataset
Public survey data

End-to-end capstone

Take one problem from raw data through SQL, Python, statistics and a Power BI dashboard, documented and defended.

Tools
MySQL, Python, Power BI
Dataset
Your choice, guided

Compare this syllabus against anyone

Take it to another institute and ask for theirs in the same detail. That comparison is the whole reason we publish it.