Master Program In
Data Analytics Course Training Institute in Pune
Build a successful career in the fast-growing field of Data Analytics with industry-focused training at CJC EdTech by Kunal Sir.
This course helps students develop strong analytical skills and gain practical experience with 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 data analytics expertise and prepare for successful IT careers.
OUR RESULT-DRIVEN METRICS
Avg Salary Hike
45%+
Avg CTC of Alumni
₹12 LPA+
Highest Placement
₹32 LPA
TOP RECRUITING PARTNERS
Alumni working at top tech companies
Free Career Counselling
Learning Journey
We provide accessible, industry-relevant education that empowers you to grow and develop your career.
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Kickstart Your Journey
Orientation by Industry Experts
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Learn Industry Skills
Hands-on projects & workshops
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Profile Building
Resume & profile creation
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Ready For Interview
Advanced interview techniques
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Get Your Dream Job
Achieve career success
Complete Course Syllabus
From Excel and MySQL fundamentals to advanced Python, NumPy, Pandas, and Data Visualization, our comprehensive curriculum is designed to make you an industry-ready Data Analyst.
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
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.
- Attrs, 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 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:
- Junior Data Analyst – up to ₹6 LPA
- Data Analyst – up to ₹9 LPA
- Business Analyst – up to ₹12 LPA
- Senior Data Analyst – up to ₹16 LPA
- Data Analytics Manager – up to ₹22 LPA
Why Choose Us
- Interactive Online & Offline Batches CJC EdTech
- Latest Market Technology & Practical Training CJC EdTech
- Multiple Projects With Hands-on Experience CJC EdTech
- Resume Building Session & Job Portals Training CJC EdTech
- Specialized Soft Skills & Personality Building Sessions CJC EdTech
- Interview Calls Assistance & Mock Sessions CJC EdTech
- Specialized Pocket Friendly Programs CJC EdTech
- Stand Out with an Impressive Certificate CJC EdTech
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 & Power Query
- SQL & MySQL
- Python, NumPy & Pandas
- Power BI & DAX
- Tableau Desktop
- Matplotlib & Seaborn
- Generative AI & LLM APIs
- Streamlit (App Building)
- Git & GitHub
- Statistics & Hypothesis Testing
- Data Collection & Cleaning
- Exploratory Data Analysis (EDA)
- Data Visualization & Reporting
- Predictive Analytics & ML
- Business Intelligence Dashboards
Batch Details
Upcoming batches — book your seat now!
Data Analytics Course Key Features
Real Business Datasets
Work on hands-on learning through real business datasets, case studies, and practical analytics projects.
Data Visualization Mastery
Build interactive reports and business intelligence dashboards using Power BI and Tableau.
Generative AI in Analytics
Integrate prompt engineering, LLM APIs, and Streamlit app development into your workflows.
SQL & Python Proficiency
Master data manipulation, querying relational databases, and automating tasks using Python.
Lifetime Placement Assistance
Dedicated career support from resume building and LinkedIn optimization to mock interviews.
Practical Project Learning
Gain industrial experience working on customer churn, sales forecasting, and sentiment analysis.
Dedicated Mentorship
Personalized 1:1 mentorship sessions, doubt clinics, and assignment reviews with experts.
Comprehensive Access
Flexible weekday, weekend, or fast-track batches with recorded sessions and offline lab access.
FAQs
No prior coding experience is strictly required. Our course starts from the fundamentals (Advanced Excel and basic statistics) before moving into SQL and Python.
You will master industry-standard tools including Advanced Excel, SQL, Python (Pandas/NumPy), Power BI, Tableau, and Generative AI tools (LLM APIs, Streamlit).
We offer flexible training formats including online, offline, and hybrid batches (weekday or weekend options) designed for students and working professionals.
Yes. You will work on real-world datasets involving customer segmentation, sales forecasting, sentiment analysis, and interactive dashboard creation.
Absolutely. We offer dedicated career support, covering resume building, portfolio creation, mock HR and technical interviews, and direct placement calls to our hiring network.
Elevate Your Career to New Heights!
Unwrap new professional capabilities & kick-start your career goals with our comprehensive Data Analytics training program.
- Customized Career Pathways
- Skill Gap Assessments
- Job Market Insights
