The Data Analytics training at IPEC Solutions, Banashankari is designed to help learners understand how raw data is converted into useful information for business decisions. This Data Analytics training in Banashankari provides hands-on learning, practical skills, and real-world applications to prepare learners for successful careers.
Data Analytics has become one of the most important skills in today’s digital economy. Data Analytics training in Banashankari helps learners gain the expertise needed to make data-driven business decisions. Nearly 85% of decisions are now driven by data, and companies that use analytics effectively perform 2 to 3 times better than their competitors. Top organizations use data analytics to understand customer behavior, optimize marketing campaigns, reduce costs, and improve operational efficiency.
The demand for data analytics professionals is growing at around 35–40% every year, making it a strong and stable career option. Compared to many technical courses, data analytics is easier to learn and faster to enter, especially for career changers. Its wide application across industries such as IT, finance, healthcare, retail, and education makes data analytics more demanding and valuable than many other professional courses today.
| Course Name | Batch Start Date | Weekday Timing | Weekend Timing |
|---|---|---|---|
| Data Science & AI Accelerator (Data Scientist) | 13/04/2026 | 10 am - 11 am (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| AI-ML with Python (Intelligent Automation Engineer) | 15/04/2026 | 11 am - 12 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| Excel Xpert (Microsoft Excel Mastery Program) | 11/05/2026 | 2 pm - 3 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| Ai-Powered Data Analytics (Excel, SQL, POWER-BI & AI) | 15/04/2026 | 2 pm - 3 pm (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| Data Xcelerator 3.0 (Analytics to Dashboard Mastery) | 10/05/2026 | 3 pm - 4 pm (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| Advanced Certified Professional in AI-Data Science | 15/04/2026 | 3 pm - 4 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| Python Developer Program (PyPro -360) | 14/05/2026 | 11 am - 12 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| Gen AI Mastery (GEN AI Mastery for Non-Coders) | 12/05/2026 | 2 pm - 3 pm (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| Business Data Analytics | 15/05/2026 | 3 pm - 4 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| AI in Medical | 11/05/2026 | 10 am - 11 am (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| DevOps, AWS, Linux, Networking (4 in 1) | 15/04/2026 | 10 am - 11 am (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| Financial Data Analyst | 12/06/2026 | 11 am - 12 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| Retail Data Analyst | 10/06/2026 | 2 pm - 3 pm (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| Stock Market Data Analyst | 14/06/2026 | 3 pm - 4 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| Healthcare Data Analyst | 13/06/2026 | 2 pm - 3 pm (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| Python Mastery: Beginner to Expert | 12/04/2026 | 10 am - 11 am (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| C Developer | 10/07/2026 | 11 am - 12 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| Data Structures Using JAVA | 15/05/2026 | 11 am - 12 pm (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| Advanced JAVA | 13/05/2026 | 2 pm - 3 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| C++ Developer | 11/07/2026 | 10 am - 11 am (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| C++ with Data Structures | 14/07/2026 | 2 pm - 3 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| Core JAVA | 15/05/2026 | 10 am - 11 am (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| Python Advanced | 13/04/2026 | 2 pm - 3 pm (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
| Data Analytics with Python | 12/05/2026 | 11 am - 12 pm (Mon-Fri) | 2 pm - 5 pm (Sat-Sun) |
| JAVA with JDBC | 11/05/2026 | 2 pm - 3 pm (Mon-Fri) | 10 am - 1 pm (Sat-Sun) |
MODULE 1: DATA ANALYSIS FOUNDATION
• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain
MODULE 2: CLASSIFICATION OF ANALYTICS
• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics
MODULE 3: CRIP-DM Model
• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling, Evaluation, Deploying,Monitoring
MODULE 4: UNIVARIATE DATA ANALYSIS
• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.
MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS
• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot
MODULE 6: BI-VARIATE DATA ANALYSIS
• Scatter Plots
• Regression Analysis
• Correlation Coefficients
MODULE 1: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1 : OVERVIEW OF STATISTICS
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 1: DATA ANALYTICS FOUNDATION
• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity
MODULE 2: OPTIMIZATION MODELS
• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity
MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION
• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel
MODULE 4: DECISION MODELING
• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling – Kickathlon Sports Retailer
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: ML ALGO: LINEAR REGRESSSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool
MODULE 3: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool
MODULE 4: ML ALGO: KNN
• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering
MODULE 6: ML ALGO: DECISION TREE
• Decision Tree and How it works
• Hands-on: Decision Tree with ML Tool
MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Hands-on: SVM with ML Tool
MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN, How It Works
• Back propagation, Gradient Descent
• Hands-on: ANN with ML Tool
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner join, Outer Join
• Left join, Right Join
• Self Join, Cross join
• Windows Functions: Over, Partition, Rank
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7: DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management
MODULE 1: BIG DATA INTRODUCTION
• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
MODULE 2: HDFS AND MAP REDUCE
• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
MODULE 3: PYSPARK FOUNDATION
• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
MODULE 4: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3: DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4: CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
Unlock your full potential with our expert-led offline and online courses. Data Analytics training in Banashankari helps you gain practical knowledge, hands-on skills, and industry-ready expertise to advance your career in your chosen field.
Hear from our successful students who have transformed their careers with iPEC’s hands-on training. From mastering AI and automation to securing top industry roles, our graduates share how iPEC’s expert mentorship, real-world projects, and career-focused learning helped them achieve their dreams.
iPEC Solutions is a service-oriented organization in Information Technology (IT) domain, founded with the aim to create significant space into IT Training & Consulting, Managed IT Services & Tech Transformation.
