100% Placement Assistance
Data Analytics Course In Pune With Placement Assistancee
Build a successful career in the fast-growing field of Data Analytics with industry-focused training at CJC EdTech by Kunal Sir.
This course is designed to help students develop strong analytical skills and gain practical experience using the most in-demand tools in the industry. Our comprehensive training focuses on practical learning, real-time projects, and expert mentorship, helping students build strong Angular development expertise and prepare for successful IT careers.
Free Career Counselling
Learning Journey
We provide accessible, industry-relevant education that empowers you to grow and develop your career.
Kickstart Your Journey
Orientation by Industry Experts
→
Learn Industry Skills
Hands-on projects & workshops
→
Profile Building
Resume & profile creation
→
Ready For Interview
Advanced interview techniques
→
Get Your Dream Job
Achieve career success
Data Analytics & Science Syllabus
A complete master curriculum covering Excel, MySQL, Python (NumPy, Pandas), R Language, and Tableau Data Visualization.
Module 1: Introduction to Excel
Overview of Excel Interface
Workbook, Worksheets, Rows, and Columns
Data Entry and Formatting
Basic Excel Functions (SUM, AVERAGE, MIN, MAX)
Cell Referencing (Relative, Absolute)
Module 2: Data Handling and Cleaning
Data Sorting and Filtering
Removing Duplicates
Text Functions (LEFT, RIGHT, MID, LEN, TRIM, PROPER, CONCATENATE)
Date & Time Functions (TODAY, NOW, YEAR, MONTH, DAY, DATEDIF)
Find & Replace, Go To Special
Module 3: Advanced Functions & Formulas
Logical Functions (IF, AND, OR, IFERROR)
Lookup & Reference Functions (VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH)
Mathematical & Statistical Functions (COUNT, COUNTA, COUNTIF, COUNTIFS, SUMIF, SUMIFS, AVERAGEIF)
Working with Named Ranges
Module 4: Data Visualization with Charts
Creating Basic Charts (Bar, Column, Line, Pie)
Formatting Charts (Titles, Labels, Legends, Axis Formatting)
Advanced Charts
Conditional Formatting for Data Analysis
Module 5: Data Analysis using Pivot Tables & Charts
Introduction to Pivot Tables
Creating and Customizing Pivot Tables
Sorting, Filtering, and Grouping Data in Pivot Tables
Using Calculated Fields and Value Fields
Creating Pivot Charts for Data Insights
Module 6: Power Query & Data Automation
Introduction to Power Query
Importing and Transforming Data
Data Cleaning with Power Query
Combining Data from Multiple Sources
Automating Data Refresh
Module 7: Macros and VBA
Recording and Running Macros
Introduction to VBA Editor
Automating Repetitive Tasks
Module 8 & 9: Case Studies, Projects & Certification
Data Cleaning and Transformation Case Studies
Hands-on Project on Real-World Dataset
Excel Proficiency Test
Module 1: Introduction to MySQL
Overview of Databases and SQL
Introduction to MySQL and Installation
MySQL Workbench and Command Line Interface
Understanding Relational Database Management System (RDBMS)
Creating and Managing Databases
Module 2: SQL Basics
Data Types in MySQL
Creating Tables (CREATE, DROP, ALTER)
Inserting Data (INSERT INTO)
Updating and Deleting Data (UPDATE, DELETE)
Basic Data Retrieval (SELECT, WHERE, ORDER BY)
Module 3: Data Filtering and Aggregation
Using WHERE, LIKE, IN, BETWEEN Operators
Logical Operators (AND, OR, NOT)
Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
Grouping Data using GROUP BY and HAVING
Module 4: Advanced SQL Queries
Joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN)
Subqueries and Nested Queries
Using CASE Statements for Conditional Logic
Window Functions (ROW_NUMBER, RANK, DENSE_RANK)
Module 5: Data Manipulation and Transactions
Understanding ACID Properties
Implementing Transactions (START TRANSACTION, COMMIT, ROLLBACK)
Using Indexes for Performance Optimization
Views (Creating, Modifying, and Dropping Views)
Temporary Tables and Their Uses
Module 6: Data Import & Export
Importing Data from CSV/Excel Files
Exporting Data to CSV/Excel Files
Using MySQL Workbench for Data Import/Export
Working with Large Datasets
Module 7: Stored Procedures & Functions
Introduction to Stored Procedures
Creating and Using Stored Procedures
User-Defined Functions (UDFs)
Triggers and Events in MySQL
Module 8 & 9: Real-World Analytics & Certification
Analyzing Sales & Marketing Data
Customer Segmentation and Insights
Financial Data Analysis
Hands-on Project with a Real-World Dataset
MySQL Proficiency Test
1. Introduction to Python
Overview & Features
Installation & Setup
2. Python Fundamentals
Variables & Data Types
Operators (Arithmetic, Logical, Comparison, Bitwise, etc.)
3. Control Flow
Conditional Statements (if, elif, else)
Loops (for, while)
4. Functions in Python
Function Definition & Calling
Return Statement
Lambda Functions
5. Data Structures
Lists & Tuples
Sets & Dictionaries
1. Introduction to NumPy
Installing NumPy & Anaconda
Jupyter Notebook Basics
2. Arrays in NumPy
Creating Arrays
Arithmetic Operations on Arrays
Homogeneous Nature of Arrays
3. Working with Arrays
Prefilled Arrays (zeros, ones, full, etc.)
Dimensional Arrays (1D, 2D, 3D, ND)
Reshaping & Flattening Arrays
4. Advanced Array Operations
Using linspace for Evenly Spaced Values
Generating Random Numbers (random.random, random.randint)
Accessing Elements in ND Arrays
5. Data Handling with NumPy
Importing & Exporting Data
Saving & Loading Arrays
Working with Datetime (Retrieve Date & Time)
1. Introduction to Pandas
Overview & Installation
Series & DataFrame Basics
2. Working with Series and Dataframes
Typecasting Data
Converting Structures to Series
Custom Indexing
Using squeeze() Method
3. Reading & Writing Files
Reading Excel, CSV, and JSON Files
4. Data Aggregation & Analysis
Aggregate Functions on Series & DataFrames
Basic Methods (head(), tail(), sample(), value_counts(), sort_values(), sort_index())
5. Handling Missing Data
isna(), fillna(), dropna()
drop_duplicates(), isnull()
6. Data Selection & Grouping
iloc & loc for Data Selection
Conditional Selection in Series
groupby() for Aggregations
7. Data Visualization
Matplotlib - Basic Plotting (Line, Bar, Scatter, Histogram, Pie)
Seaborn - Advanced Visualization & Customizing Graphs
8. Basics & Fundamentals
Intro to R - Overview & Features | Installation & Setup
R Fundamentals - Syntax, Variables & Data Types | Operators
Control Flow - Conditional Statements | Loops
Functions in R - Definition & Calling | Return Statement
9. Data Structures in R
Vectors in R - Creating & Manipulating | Vector Operations
Lists in R - Creating & Accessing Elements | Modifying
Matrices in R - Creating & Indexing | Matrix Operations
Arrays in R - Multi-Dimensional Arrays
Data Frames & Factors in R - Categorization
10. File Handling & Visualization
Reading & Writing Files (CSV, Excel, JSON)
Data Manipulation & Cleaning (Handling Missing Data)
Data Visualization in R - Plotting Basics (Line, Scatter, Pie, Bar)
11. Advanced Topics in R
Statistical Analysis in R - Working with Datasets
Computing Max, Min, Mean, Median, Mode
1. Introduction to Tableau
What is Tableau?
Why Data Visualization?
Excel vs BI Tools: Understanding the differences and when to use each.
Top BI Tools: An overview of popular business intelligence tools.
2. Tableau Products
Live vs Extract: The difference between live connections & extract data in Tableau.
File Types: Types of files used in Tableau (e.g., .twb, .twbx)
Desktop & Server Architecture: The architecture of Tableau Desktop vs Tableau Server.
3. Setting Up Tableau
Install Tableau Public & Create Account
Get Datasets, Publish First Viz: How to import datasets and create your first visualization.
Tableau Interface Overview: Understanding the different parts of the Tableau interface.
4. Combining Data
Data Modeling: How to structure & connect different datasets.
Joins, Unions, Relationships: Methods for combining multiple data sources.
Data Blending: Combining data from different sources when needed.
5. Tableau Metadata & Products
Data Types: Understanding different data types in Tableau.
Dimensions & Measures: Differences & usage.
Discrete vs Continuous: The distinction & when to use each.
Development & Sharing Products: Overview of Tableau Desktop, Server, Public.
6. Data Organization
Renaming, Aliases: How to organize and label data fields.
Hierarchy: Creating hierarchical structures within data.
Groups, Sets, Bins: Creating groups, sets, & bins for better analysis.
7. Filtering & Sorting
Creating & Customizing Filters: How to apply filters & customize them.
Sorting: Organizing data in a meaningful order.
Tableau Parameters: How parameters work & how to use them.
Tableau Actions: Actions like highlight, filter, & URL actions.
8. Tableau Calculations
Functions: Using number, string, date, logical, & aggregate functions.
ATTR(), Fixed, Exclude, Include: Advanced calculation techniques.
9. Charts & Dashboards
Overview of various chart types: Bar, Line, Pie, etc.
Building Dashboards: How to combine charts & create effective dashboards for storytelling.
WHY CJC EdTech?
Why Learn Data Analytics & Science at CJC EdTech?
Interactive Online & Offline Batches
Latest Market Technology & Practical Training
Multiple Projects With Hands-on Experience
Resume Building & Job Portals Training
Soft Skills & Personality Building Sessions
Interview Calls Assistance & Mock Sessions
Specialized Pocket-Friendly Programs
Stand Out with an Impressive Certificate
Career Opportunities
After completing this course, you can apply for:
- Data Analyst (Excel & SQL) – up to ₹8 LPA
- BI & Tableau Developer – up to ₹12 LPA
- Python Data Analyst – up to ₹15 LPA
- Data Scientist – up to ₹20 LPA
- Sr. Data Analytics Consultant – up to ₹25+ LPA
Why Choose Us VS Others
CJC EdTech
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CJC EdTech
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CJC EdTech
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CJC EdTech
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CJC EdTech
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CJC EdTech
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CJC EdTech
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CJC EdTech
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Career Services
Get ready for your dream job with comprehensive industry readiness training.
Online & Offline Flexibility
Live training through online and offline batches
360° Knowledge Building
Practical skills through real-world projects
Industry-Relevant Syllabus
Latest tools, techniques & trends
1:1 Dedicated Mentorship
Personalized learning from experienced professionals
Wide Range Of Tools & Modules Covered
Advanced Excel
MySQL Workbench
Python & Jupyter
RStudio
Tableau Public
Power Query & VBA
NumPy & Pandas
Matplotlib & Seaborn
R Data Structures
Pivot Tables & Charts
Complex SQL Joins
Statistical Analysis
Data Cleaning
Interactive Dashboards
Exploratory Data Analysis
Tableau Calculations
Stored Procedures
Real-World Projects
Batch Details
Upcoming Data Analytics & Science batches — book your seat now!
15/04/2026
Data Analytics Foundation (Excel & SQL) - Regular Batch (Mon-Fri)
18/04/2026
Python & R for Data Science - Weekend Batch (Sat-Sun)
22/04/2026
Advanced Data Visualization (Tableau) - Regular Batch (Mon-Fri)
25/04/2026
Data Analytics Masterclass - Weekend Fast Track
Data Analytics & Science Key Features
Advanced Data Manipulation
Master data cleaning, formatting, Power Query, and advanced formulas using Microsoft Excel.
Relational Databases (SQL)
Write powerful MySQL queries, handle complex joins, and create stored procedures for data extraction.
Python Programming
Learn Core Python, Data Structures, and leverage libraries like NumPy and Pandas for deep analysis.
Statistical Analysis with R
Perform robust statistical analysis, array indexing, and detailed matrix operations using R Language.
Interactive Dashboards
Transform raw datasets into compelling, interactive visual stories and dashboards using Tableau.
Real-World Projects
Work on real-world datasets, complete diverse case studies, and build a comprehensive analytics portfolio.
Dedicated Mentorship
Get personalized guidance from industry experts to master complex analytical concepts and formulas.
Lifetime Placement Support
Comprehensive resume building, mock interviews, and placement assistance for Data Analyst roles.
FAQs
Data Analytics is one of the fastest-growing fields globally. Companies rely on data to make strategic decisions. By mastering tools like Excel, SQL, Python, R, and Tableau, you become a highly valuable asset capable of translating raw data into actionable insights, leading to lucrative career opportunities.
The course covers Python fundamentals, Data Structures, and libraries like NumPy and Pandas for data manipulation. In R, you will learn statistical analysis, file handling, arrays, data frames, and graphical visualization.
No prior coding experience is required! We start from the absolute basics of Excel and SQL before moving into Python and R programming, ensuring a smooth learning curve for beginners and professionals transitioning into data roles.
Yes. The Tableau module focuses entirely on Data Visualization. You will learn to connect datasets, apply filters, use advanced calculations, and build interactive, professional dashboards to effectively tell stories with data.
Absolutely. We offer comprehensive lifetime placement support. This includes resume building, portfolio creation showcasing your real-world data projects, mock interviews, and direct referrals to top tech and analytics companies.
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