The Best Data Analytics Training Institute in Bangalore

Known as India’s Silicon Valley , Bangalore stands as the nation’s leading destination for technology and innovation. With over 10,000+ IT jobs available every month, along with jobs in the analytics sector, MNCs like Infosys, Accenture, Tata Consultancy Services, Wipro, Capgemini, Flipkart, IBM, and Razorpay are constantly hiring skilled Data Analysts.

Bangalore also hosts high-level data meetups, tech conferences, and analytics communities programs, which will be beneficial in boosting your career.

Studying a Data Analytics course at Aspire Techsoft's Koramangala Bangalore branch means networking opportunities, mentorship, and a fast-track career launch.

No prior experience required to learn the Data Analytics Course in Bangalore. If you have the basic skills in Basic Mathematics & Statistics, Comfort with Computers, Basic Programming, and Working with Spreadsheets, it will added advantage. Our Data Analyst course is Open to freshers and working professionals from any stream; IT and non‑IT people; “no coding-related experience needed.

Foundational knowledge for aspiring data analytics professionals, detailing that while formal education is not strictly required, skills in mathematics and statistics, spreadsheet software like Excel, basic programming with Python or R, Power BI ( business intelligence platform), database querying with SQL, and Tableau (data visualization) are highly beneficial.

Introduction to Data Analytics

Data Collection and Cleaning

Exploratory Data Analysis (EDA)

Statistical Analysis

Data Visualization

SQL for Data Analytics

Introduction to Programming (Usually Python or R)

Predictive Analytics & Machine Learning (Introductory Level)

Business Applications of Data Analytics

Up-to-date, job-ready Industry-Relevant Data Analytics Curriculum

100% Job-Oriented Data Analytics Training

Learn from Real-Time Industry Experts

Flexible weekend & weekday batches available

Hands-on Projects and Real-Time Case Studies

100% Placement Support

Free Demo Class – Feel the Difference

At Aspire Techsoft's data analytics training institute in Bangalore, you are trained on specialized cloud platforms like Microsoft Excel, Python, Power BI, Tableau, and BigQuery, technologies used across industries today.

The data analyst training here prepares students, as well as those looking to switch careers in this field, for specific career roles like data analyst, BI developer, and marketing analyst, with a focus on Bengaluru-specific recruitment trends and placement support.

More than 87% of data analyst roles in Bengaluru now prioritize tool-specific skills, according to a recent NASSCOM 2025 report.

Whether it's a job in Koramangala's IT startups or Whitefield's multinationals and Electronic City's global distribution centers, if you have skills specific to data analytics tools, you'll gain a faster path to placement.

Top Companies Hiring Data Analytics Tools in Bengaluru that are Mu Sigma, Fractal Analytics, Tiger Analytics, Tata Consultancy Services (TCS), Infosys, LatentView Analytics, EXL Analytics, Genpact, ZS Associates, Walmart Global Tech India, Google India (Bengaluru), Amazon India, Flipkart, IBM India, Deloitte India (Bengaluru Office), etc.

You don't need any prior IT experience to work in companies as a Data Analyst. Our Data Analyst course is designed to teach you the essential data tools and techniques.

Here you will get hands-on experience by working on real-world projects with 10+ industry experts, which will also help you build a strong portfolio.

Aspire Techsoft is the best Data Analyst training institute in Bangalore, where you will also get resume building and interview preparation skills, which will give you the confidence to get good job opportunities in Bangalore.

Data Analyst

Business Analyst

Marketing Analyst

Financial Analyst

SQL Analyst

Product Analyst

Junior Data Scientist

Introduction to Excel

• Excel Interface Overview

• Navigating the Ribbon

• Understanding Worksheets and Workbooks

• Creating and Saving Workbooks

• Data Entry and Editing

• Customizing the Quick Access Toolbar

• Excel File Formats

Excel Formula

• Basic Arithmetic Formulas (SUM, SUBTRACT, MULTIPLY, DIVIDE)

• AutoSum Function, Cell References (Relative, Absolute, Mixed)

• Common Statistical Functions (AVERAGE, MEDIAN, MODE)

• Text Formulas (CONCATENATE, LEFT, RIGHT)

• Date Formulas (TODAY, NOW, DATE)

• Logical Formulas (IF, AND, OR)

Formula Based Formatting

• Conditional Formatting Rules

• Highlighting Cells Based on Values

• Data Bars, Color Scales, and Icon Sets

• Custom Conditional Formatting

• Managing Conditional Formatting Rules

• Using Formulas in Conditional Formatting

• Applying Conditional Formatting to Pivot Tables

Conditional Statements & Logical Operators

• IF Statements, Nested IF Statements, AND, OR Operators, NOT Operator

• Combining Multiple Conditions

• Using IF with Text, Numbers, and Dates

• Conditional Formatting with Logical Operators

Text Functions

• CONCATENATE (or CONCAT), LEFT, RIGHT, MID, LEN, and TRIM

• UPPER, LOWER, PROPER

• SUBSTITUTE and REPLACE

• FIND and SEARCH

• TEXT Function for Formatting Numbers and Dates

Date and Time Functions

• TODAY and NOW

• DATE and TIME

• DAY, MONTH, YEAR, HOUR, MINUTE, SECOND

• DATEDIF, NETWORKDAYS, EDATE, and EOMONTH

Excel Tables

• Creating and Formatting Excel Tables

• Table Styles and Options

• Sorting and Filtering Tables

• Adding and Removing Table Rows/Columns

• Structured References

• Table Calculations and Totals

• Converting Table to Range

Basic and Advanced Table Operations

• Creating Dynamic Tables

• Using Table Formulas

• Advanced Filtering Techniques

• Using Slicers with Tables

• Advanced Sorting Techniques

• Working with Subtotals

• Merging and Splitting Tables

Data Pivoting with Different Examples

• Creating Pivot tables, PivotTable Fields and Areas

• Grouping Data in PivotTables

• Pivotable Calculations (Sum, Count, Average)

• Creating Pivot Charts

• Filtering Pivot tables with Slicers and Timelines

• Advanced Pivotable Techniques (Calculated Fields, Custom Calculations)

Cell Reference Functions

• Understanding Cell References

• Using Relative References

• Using Absolute References

• Mixed References

• INDIRECT Function

• OFFSET Function

• Linking Cells Between Sheets and Workbooks

LookUp and VLookUp

• Introduction to LookUp Functions

• VLOOKUP Basics

• HLOOKUP Basics

• Using VLOOKUP with Exact and Approximate Match

• Combining VLOOKUP with Other Functions

• Common VLOOKUP Errors and Fixes

• Alternatives to VLOOKUP (INDEX and MATCH)

Excel Data Visualization

• Creating Basic Charts (Column, Line, Pie)

• Customizing Chart Elements (Titles, Legends, Labels)

• Using Sparklines for Miniature Charts

• Creating Combo Charts

• Using Conditional Formatting for Data Visualization

• Creating Heat Maps

• Using Data Bars and Color Scales

Introduction to Power Query

• What is a Power Query?

• Power Query Interface Overview

• Loading Data into Power Query

• Basic Data Transformation (Filter, Sort, Remove Columns)

• Combining Data from Multiple Sources

• Data Profiling in Power Query

• Saving and Loading Data to Excel

Power Query Editor

• Navigating the Power Query Editor

• Applying Basic Transformations (Replace Values, Remove Duplicates)

• Advanced Transformations (Pivot/Unpivot, Group By)

• Creating Custom Columns

• Merging Queries

• Appending Queries

• Managing Query Steps

Formatting Charts

• Creating Column Charts

• Creating Line Charts

• Creating Pie Charts

• Creating Bar Charts

• Creating Area Charts

• Creating Scatter Plots

• Customizing Chart Types and Style

Creating Reports in Excel with Dataset

• Data Preparation and Cleaning

• Using PivotTables for Reports

• Combining Multiple Data Sources

• Adding Visual Elements (Charts, Sparklines)

• Creating Interactive Dashboards

• Automating Report Updates

• Distributing and Sharing Reports

Introduction to Power BI

• Overview of Power BI and its components

• Power BI service vs. Power BI Desktop

• Benefits of using Power BI for data analytics

• Key features and capabilities of Power BI

• Understanding the Power BI ecosystem

• User interface and navigation

• Basic concepts and terminology in Power BI

Installation of Power BI Desktop

• System requirements for Power BI Desktop

• Step-by-step installation guide

• Initial setup and configuration

• Updating Power BI Desktop

• Troubleshooting common installation issues

• Licensing and subscription options

• Importing sample datasets for practice

Connecting Power BI with Other Sources

• Supported data sources in Power BI

• Connecting to databases, cloud services, and files

• Data import methods and best practices

• Managing data connections

• Data refresh and scheduling

• Handling data privacy and security

• Using Power BI gateways

Basic Visualization in Power BI

• Types of visualizations available in Power BI

• Creating basic charts and graphs

• Customizing visualizations (colors, labels, titles)

• Using slicers and filters

• Creating and managing dashboards

• Adding interactivity to visualizations

• Publishing and sharing reports

Advanced Visualization in Power BI

• Using advanced chart types (waterfall, funnel, gauge)

• Creating and using bookmarks

• Implementing drill-through functionality

• Using custom visuals from the marketplace

• Creating dynamic visualizations with DAX

• Advanced interactivity techniques

• Combining multiple visualizations in a single report

Advanced DAX

• Introduction to DAX: Calculated Columns, Measures, and Tables

• Time Intelligence Functions

• Filtering Functions

• Advanced Calculations and Complex DAX Queries

• Optimizing DAX Queries for Performance

• Practical Applications of DAX in Data Models

Introduction to Power Query

• Overview of Power Query and its interface

• Connecting to various data sources

• Understanding the M language

• Basic data import and transformation

• Using the query editor for data preparation

• Managing and combining queries

• Best practices for efficient data queries

SQL Basics for Data Analysis

• Introduction to SQL

• Setting up the SQL environment

• Basic SQL Commands

• Creating and Deleting Databases and Tables

• Importing and Exporting Data from CSV Files

Fundamentals of SQL Query

• Anatomy of SQL Query

• SQL Data Types and Operators

• Filtering and Sorting Data in SQL

• Aggregate functions in SQL

Dealing With Multiple Tables

• Grouping Data – GROUP BY

• HAVING

• Subqueries

• Joining tables using INNER JOIN, LEFT JOIN, RIGHT JOIN & FULL OUTER JOIN

• Alias in SQL queries

• Working with Multiple Tables Using Subqueries

• Using Set Operators

• Aggregating Data from Multiple Tables using GROUP BY and HAVING.

Advanced SQL Joins

• Advanced Join Techniques

• Joining multiple tables

• Handling duplicate records and eliminating duplicates

• Using UNION and UNION ALL to combine data from multiple tables

Type Casting & Math Functions

• Mathematical Functions

• Type Conversion Functions

• Using CASE Statements to Perform Conditional Operations

DateTime & String Functions

• Working with date/time data in SQL

• Date/time functions

• Formatting date/time data

• String manipulation functions (e.g. UPPER, LOWER, LEFT, RIGHT, etc.)

• Regular expressions in SQL for string operations

• Using CONCAT_WS to concatenate strings with a separator

Window Functions

• Syntax of Windows Function

• Ranking functions (e.g. ROW_NUMBER, RANK, DENSE_RANK, etc.)

• Aggregate functions using windows (e.g. SUM, AVG, MAX, MIN, etc.)

• Partitioning data for window functions

• Understanding the difference between row-based and aggregate-based window functions

Complex queries using CTE & Pivoting

• Common Table Expressions(CTE)

• Recursive CTEs for Hierarchical Data

• Combining CTEs with Window Functions and Subqueries

• Understanding the Performance Implications of CTEs

Database Management & Schema Design

• Understanding The Relational Model and Database Schema Design

• Normalization and Denormalization of Database Tables

• Database Administration Tasks

• Implementing Indexes and Constraints for Data Integrity

• Designing Efficient Database Queries for Performance Optimization

Introduction to Alteryx

• Overview of Alteryx

• Key Features and Benefits

• Use Cases and Applications

• Comparison with Other Data Analytics Tools

• Alteryx Designer Interface

• Licensing and Pricing

• Alteryx Community and Resources

Installation of Alteryx

• System Requirements

• Step-by-Step Installation Guide

• Post-Installation Configuration

• Licensing and Activation

• Troubleshooting Common Installation Issues

• Updating Alteryx

• Uninstalling Alteryx

In/Out Tools

• Input Data Tool

• Output Data Tool

• Directory Tool

• Text Input Tool

• Download Tool

• SharePoint Input Tool

• Blob Input Tool

Preparation Tools

• Data Cleansing Tool

• Filter Tool

• Select Tool

• Sort Tool

• Sample Tool

• Unique Tool

• Generate Rows Tool

Join Tools

• Join Tool

• Union Tool

• Find Replace Tool

• Append Fields Tool

• Make Group Tool

• Fuzzy Match Tool

• Spatial Match Tool

Transform Tools

• Cross Tab Tool

• Transpose Tool

• Formula Tool

• Multi-Field Formula Tool

• Running Total Tool

• Tile Tool

• Imputation Tool

Reporting Tools

• Table Tool

• Charting Tool

• Layout Tool

• Render Tool

• Report Map Tool

• Text Box Tool

• Email Tool

Database Tools

• Connect In-DB Tool

• Data Stream In Tool

• Filter In-DB Tool

• Formula In-DB Tool

• Join In-DB Tool

• Summarize In-DB Tool

• Write Data In-DB Tool

Documentation Tools

• Comment Tool

• Tool Container

• Block Until Done Tool

• Message Tool

• Summarize Tool

• Tool Annotation

• Metadata Output Tool

Full Workflow

• Designing a Workflow

• Best Practices for Workflow Management

• Debugging and Error Handling

• Workflow Automation

• Performance Optimization

• Sharing & Collaboration

• Real-World Workflow Examples