Data Analytics Course in Pune – Online Interactive Training with Real-World Projects
Join JustAcademy’s Data Analytics Bootcamp in Pune and learn Excel, SQL, Python, Power BI, Tableau, and Machine Learning through live online interactive classes, hands-on projects, mentorship, and placement assistance.
4.4(86 ratings)
Last updated 6/2026
This course includes:
Live Instructor-Led Sessions
Recorded Video Access
Certificate of Completion
Lifetime Access
Downloadable Resources
Code Repository Access
Project Files
Doubt Support
Job Assistance
Interview Preparation
Portfolio Review
Industry Expert Sessions
Real-time Projects
What you'll learn
Business Intelligence
Dashboard Creation
Data Analysis
Data Cleaning
Data Interpretation
Data Visualization
Excel Analytics
Power BI
Python for Data Analysis
SQL Queries
Statistics
Tableau
Description
Data Analytics Career Bootcamp in Pune – Interactive Online Learning Program
The Data Analytics Career Bootcamp is an advanced online interactive training program created for students, freshers, graduates, and working professionals who want to build practical skills in data analytics and business intelligence. This career-focused bootcamp combines live online classes, industry-oriented projects, analytics tools training, and placement preparation to help learners become job-ready for the modern data-driven industry.
Organizations today rely heavily on analytics to improve decision-making, understand customer behavior, optimize business operations, and increase profitability. This online data analytics training program helps students understand how to work with real datasets, perform business analysis, create interactive dashboards, and generate valuable business insights.
Unlike traditional classroom programs that focus mostly on theory, this Data Analytics Bootcamp emphasizes practical implementation through assignments, live projects, case studies, and interactive mentor guidance.
Students will learn data analytics concepts from beginner to advanced level using popular technologies such as Excel, SQL, Python, Power BI, Tableau, Pandas, and data visualization tools.
By the end of the bootcamp, learners will be able to analyze business data, build dashboards, create reports, and confidently apply for Data Analyst and Business Intelligence roles.
Skills Covered in This Data Analytics Program
Students will learn both technical and analytical concepts required for modern data analytics jobs.
Core Learning Modules
Data Analytics Fundamentals
Data Collection Techniques
Data Cleaning & Transformation
SQL Query Writing & Database Analysis
Python for Data Analytics
Exploratory Data Analysis (EDA)
Dashboard Design & Reporting
Power BI Dashboard Development
Tableau Visualization Techniques
Statistical Analysis Concepts
Business Intelligence Fundamentals
Data Interpretation & Storytelling
Real-Time Project Development
Technologies & Tools Covered
This online data analytics training provides practical experience with leading analytics technologies used by companies worldwide.
Analytics Tools
Purpose
Microsoft Excel
Data Analysis & Reporting
SQL
Database Querying
Python
Data Processing & Automation
Pandas & NumPy
Data Manipulation
Power BI
Dashboard Creation
Tableau
Data Visualization
Jupyter Notebook
Analytics Development
Google Sheets
Cloud-Based Reporting
Matplotlib
Data Visualization
Git & GitHub
Project Management
Interactive Online Learning Experience
The bootcamp is designed around practical live online sessions where students actively participate in analytics exercises and project implementation.
Training Features
Live Instructor-Led Classes
Real-Time Practical Sessions
Weekly Assignments
Interactive Dashboard Building
Mentor Support & Guidance
Doubt Solving Sessions
Industry Use Cases & Case Studies
Recorded Sessions for Revision
This learning approach helps students build confidence in handling real business datasets and analytics workflows.
Industry Projects Included
Students work on practical analytics projects to build strong portfolios and gain hands-on experience.
Project Name
Skills Learned
Sales Performance Dashboard
KPI Tracking & Visualization
Customer Analytics Project
Customer Segmentation
HR Analytics Dashboard
Employee Data Analysis
Financial Reporting Project
Business Reporting
E-Commerce Analytics
Consumer Behavior Analysis
Marketing Campaign Dashboard
ROI & Performance Tracking
These projects help learners demonstrate practical analytics skills during interviews.
Career Opportunities After Completion
After successfully completing this Data Analytics Bootcamp, students can apply for multiple analytics and business intelligence roles.
Career Roles
Data Analyst
Business Intelligence Analyst
Reporting Analyst
Data Visualization Specialist
Junior Data Scientist
Operations Analyst
Marketing Data Analyst
Financial Data Analyst
Data Analytics professionals are in demand across industries including finance, healthcare, e-commerce, IT, education, retail, and digital marketing.
Average Salary of Data Analytics Professionals
Data Analytics offers excellent career growth and high-paying opportunities.
Experience Level
Average Salary Range
Entry-Level Analyst
INR 4 LPA – INR 8 LPA
Mid-Level Data Analyst
INR 9 LPA – INR 16 LPA
Senior Analytics Professional
INR 18 LPA – INR 30 LPA
Salary growth depends on technical expertise, project experience, and business understanding.
Who Can Join This Bootcamp?
This online Data Analytics Bootcamp is suitable for:
Beginners with no technical background
Engineering and commerce students
Graduates looking for analytics careers
Working professionals switching domains
Marketing & business professionals
Entrepreneurs and freelancers
Anyone interested in data-driven decision-making
No prior coding experience is required to join this bootcamp.
Placement & Career Support
Students receive complete career assistance throughout the training program.
Career Support Includes
Resume Building Sessions
LinkedIn Profile Optimization
Mock Technical Interviews
HR Interview Preparation
Portfolio Building Guidance
Internship Support
Job Assistance & Referrals
The goal of this Data Analytics Bootcamp is to help learners gain practical skills, industry exposure, and career confidence required to succeed in the analytics industry.
Course content
Introduction to Data Analytics
What is Data Analytics and Its Importance in the Modern Business World
Types of Data Analytics: Descriptive, Diagnostic, Predictive and Prescriptive Analytics
Understanding the Data Analytics Lifecycle: From Collection to Insight
Difference Between Data Analytics, Data Science and Business Intelligence
Key Industries Using Data Analytics: Finance, Healthcare, E-commerce and Marketing
Overview of Tools Used in Data Analytics: Excel, SQL, Python, Power BI and Tableau
Understanding Structured vs Unstructured Data
Introduction to Data-Driven Decision Making in Organizations
Career Opportunities and Job Roles in Data Analytics
Setting Up Your Analytics Workspace: Installing Required Tools and Software
Excel for Data Analysis
Introduction to Microsoft Excel as a Data Analysis Tool
Navigating the Excel Interface: Ribbons, Worksheets and Workbooks
Data Entry, Formatting Cells and Organizing Raw Data in Excel
Essential Excel Formulas for Data Analysis: SUM, COUNT, AVERAGE, IF and IFERROR
Advanced Excel Functions: VLOOKUP, HLOOKUP, INDEX-MATCH and XLOOKUP
Working with Named Ranges and Absolute vs Relative Cell References
Sorting and Filtering Data for Quick Analysis
Using Conditional Formatting to Highlight Key Data Patterns
Data Validation: Controlling Input and Reducing Errors in Spreadsheets
Building Pivot Tables to Summarize and Analyze Large Datasets
Creating Pivot Charts for Visual Data Representation
Excel Charts and Graphs: Bar, Line, Pie and Scatter Plots for Reporting
What-If Analysis: Goal Seek, Data Tables and Scenario Manager
Introduction to Power Query for Automating Data Import and Transformation
Best Practices for Organizing and Presenting Data in Excel for Business Reports
SQL for Data Analytics
Introduction to SQL and Its Role in Data Analytics
Understanding Relational Databases: Tables, Rows, Columns and Keys
Setting Up MySQL or PostgreSQL for Data Analysis Practice
Writing Your First SQL Query: SELECT, FROM and WHERE Clauses
Sorting Query Results Using ORDER BY and Limiting Output with LIMIT
Grouping Data with GROUP BY and Filtering Groups Using HAVING
Aggregate Functions for Business Analysis: COUNT, SUM, AVG, MIN and MAX
SQL Joins Explained: INNER JOIN, LEFT JOIN, RIGHT JOIN and FULL OUTER JOIN
Writing Subqueries and Nested SELECT Statements for Complex Analysis
Using String Functions, Date Functions and Mathematical Functions in SQL
Creating and Managing Views for Reusable Query Logic
Introduction to Window Functions: ROW_NUMBER, RANK and PARTITION BY
SQL for Real-World Business Scenarios: Sales, Customer and Product Analysis
Optimizing SQL Queries for Faster Data Retrieval and Performance
Python for Data Analysis
Introduction to Python and Why It Is the Most Popular Data Analytics Language
Installing Python, Anaconda and Setting Up Jupyter Notebook
Python Basics for Data Analysts: Variables, Data Types, Operators and Conditionals
Working with Python Lists, Tuples, Dictionaries and Sets
Writing Loops and Functions to Automate Repetitive Data Tasks
Introduction to NumPy: Arrays, Array Operations and Numerical Computing
Working with NumPy for Statistical Calculations on Large Datasets
Introduction to Pandas: Series and DataFrames for Data Manipulation
Loading Data into Pandas from CSV, Excel and SQL Sources
Exploring Datasets with head(), info(), describe() and value_counts()
Selecting, Filtering and Slicing Data Using Pandas loc and iloc
Handling Missing Data: Detecting, Filling and Dropping Null Values in Pandas
Merging, Joining and Concatenating Multiple DataFrames
GroupBy Operations and Aggregation Functions in Pandas
Creating Data Visualizations with Matplotlib: Line, Bar and Scatter Charts
Building Statistical Charts with Seaborn: Heatmaps, Boxplots and Pairplots
Automating Repetitive Data Analysis Tasks Using Python Scripts
Data Cleaning & Data Preparation
What is Data Cleaning and Why Dirty Data Costs Businesses Millions
Understanding Common Data Quality Issues: Inconsistencies, Errors and Gaps
Identifying and Handling Missing Values Using Python and Excel
Detecting and Removing Duplicate Records from Datasets
Standardizing Data Formats: Dates, Phone Numbers, Currency and Text Case
Fixing Inconsistent Category Labels and Typos in Text Data
Outlier Detection Techniques: IQR Method, Z-Score and Visual Identification
Strategies for Treating Outliers: Removal, Capping and Transformation
Data Type Conversion: Changing Strings to Numbers, Dates and Booleans
Feature Engineering Basics: Creating New Columns from Existing Data
Normalization and Standardization of Numerical Data for Analysis
Encoding Categorical Variables: Label Encoding and One-Hot Encoding
Building a Data Cleaning Pipeline for Repeatable and Scalable Workflows
Validating Cleaned Data: Checking Completeness, Accuracy and Consistency
Preparing Final Analysis-Ready Datasets for Visualization and Reporting
Data Visualization with Power BI
Introduction to Power BI: What It Is and Why Businesses Use It
Installing Power BI Desktop and Understanding the Interface Layout
Connecting Power BI to Data Sources: Excel, CSV, SQL Databases and Web
Importing and Transforming Data Using Power Query Editor
Understanding Data Models: Tables, Relationships and Star Schema in Power BI
Introduction to DAX (Data Analysis Expressions): Calculated Columns and Measures
Commonly Used DAX Functions: SUM, CALCULATE, FILTER, DIVIDE and IF
Building Your First Power BI Report: Charts, Tables and Cards
Creating Bar Charts, Line Charts, Donut Charts and Maps in Power BI
Using Slicers and Filters for Interactive Data Exploration
Designing Multi-Page Reports with Navigation and Bookmarks
Building Executive-Level Dashboards in Power BI Desktop
Publishing Reports to Power BI Service and Sharing with Teams
Row-Level Security and Access Control in Power BI
Best Practices for Designing Clean, Professional Power BI Dashboards
Data Visualization with Tableau
Introduction to Tableau and Its Importance in Data Analytics and BI
Installing Tableau Public and Understanding the Tableau Interface
Connecting Tableau to Data Sources: Excel, CSV, SQL and Google Sheets
Understanding Dimensions vs Measures and Discrete vs Continuous Fields
Building Basic Visualizations: Bar Charts, Line Graphs and Pie Charts in Tableau
Creating Geographic Maps and Spatial Analysis Dashboards in Tableau
Using Filters, Quick Filters and Context Filters for Dynamic Analysis
Calculated Fields and Table Calculations for Custom Metrics in Tableau
Building Parameters for User-Controlled Interactive Reports
Creating Dual-Axis Charts and Combined Chart Types in Tableau
Designing Interactive Dashboards with Actions and Navigation in Tableau
Building Data Stories Using Tableau Story Points
Publishing and Sharing Dashboards on Tableau Public
Tableau vs Power BI: Key Differences and When to Use Each Tool
Best Practices for Effective Data Storytelling with Tableau
Statistics for Data Analysis
Why Statistics is the Foundation of Data Analytics
Types of Data: Nominal, Ordinal, Interval and Ratio Scales
Descriptive Statistics: Summarizing Data with Mean, Median and Mode
Measures of Dispersion: Range, Variance, Standard Deviation and IQR
Understanding Data Distributions: Normal, Skewed and Bimodal Distributions
Probability Distributions: Binomial, Poisson and Normal Distribution
Sampling Techniques: Random, Stratified and Systematic Sampling
Introduction to Inferential Statistics and Population vs Sample
Confidence Intervals and Margin of Error Explained
Hypothesis Testing: Null Hypothesis, Alternate Hypothesis and P-Value
t-Tests, Chi-Square Tests and ANOVA for Comparing Groups
Correlation Analysis: Pearson and Spearman Correlation Coefficients
Simple Linear Regression for Predicting Business Outcomes
Applying Statistical Concepts to Real Business Datasets Using Python and Excel
Business Intelligence Concepts
What is Business Intelligence and How It Powers Data-Driven Organizations
Difference Between Business Intelligence, Data Analytics and Data Science
BI Architecture Overview: Data Sources, ETL, Data Warehouse and Reporting Layer
Introduction to ETL (Extract, Transform, Load) Processes and Tools
Understanding Data Warehouses vs Data Lakes vs Data Marts
Key Performance Indicators (KPIs): Defining, Measuring and Tracking Business Metrics
Designing BI Reports Tailored for Different Business Stakeholders
Introduction to OLAP (Online Analytical Processing) and Multidimensional Analysis
Data Governance: Ensuring Data Quality, Security and Compliance in Organizations
Real-World BI Use Cases: Retail Sales Analysis, Customer Churn and Financial Reporting
Introduction to Cloud-Based BI Tools: Google Looker Studio and Microsoft Fabric
How Business Intelligence Supports Strategic Decision Making in Companies
Data Analytics Project Development
Understanding Real-World Project Requirements and Defining the Business Problem
Identifying the Right Dataset: Public Datasets, Company Data and Web Scraping Basics
Conducting Exploratory Data Analysis (EDA) to Understand Your Dataset
Formulating Analytical Questions That Drive Meaningful Business Insights
Applying Data Cleaning and Preparation Techniques on a Raw Dataset
Performing In-Depth Analysis Using Python, SQL and Excel Together
Building Visualizations That Tell a Clear and Compelling Data Story
Structuring an End-to-End Data Analytics Project from Problem to Presentation
Writing Project Documentation: Objectives, Methodology, Findings and Recommendations
Presenting Data Insights Effectively to Technical and Non-Technical Audiences
Adding Completed Projects to GitHub and Your Data Analytics Portfolio
Mock Technical Interview Practice with Real Datasets and Case Study Questions
Understanding Salary Ranges and How to Negotiate Your First Data Analyst Offer
Top Job Portals and Platforms to Find Data Analyst Jobs in India and Abroad
How to Approach Referrals, Networking and LinkedIn Outreach for Job Placement
Bootcamp FAQ's
Data Analytics involves collecting, cleaning, analyzing, and visualizing data to help businesses make informed decisions. It is one of the fastest-growing career fields across industries.
Students, graduates, working professionals, career switchers, entrepreneurs, and beginners with no prior coding experience can join this bootcamp.
The program covers Excel, SQL, Python, Pandas, NumPy, Power BI, Tableau, Statistics, Git, GitHub, and data visualization tools.
No. The course starts with fundamentals and gradually introduces SQL, Python, and analytics concepts, making it beginner-friendly.
Yes. Students work on projects such as sales dashboards, customer analytics, HR analytics, financial reporting, and marketing performance analysis.
You can apply for roles such as Data Analyst, Business Intelligence Analyst, Reporting Analyst, Operations Analyst, Marketing Data Analyst, and Junior Data Scientist.
Entry-level Data Analysts typically earn between ₹4 LPA and ₹8 LPA, with salaries increasing significantly as experience and technical expertise grow.
Yes. The program includes resume building, LinkedIn optimization, mock interviews, portfolio guidance, and job assistance.
Yes. Flexible schedules and live online interactive sessions make the bootcamp suitable for working professionals and career changers.
Organizations rely on data to improve decision-making, optimize operations, understand customer behavior, and drive business growth, creating strong demand for analytics professionals.
This course includes:
Full Lifetime Access
Certificate of Completion
Direct Instructor Access
This course includes:
Full Lifetime Access
Certificate of Completion
Direct Instructor Access
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