Best Data Analytics Course in Bangalore (2026)
Experience Building Course with Internship
Master Data Analytics Course in Bangalore with Internship & 100% Placement Support (2026). Learn Python, SQL, Power BI, Tableau, Excel, Microsoft Fabric, Snowflake & GenAI. Work on live projects & get hired by top MNCs. Book a free demo today! 10+ Years of Excellence in Career Transitions from different backgrounds like freshers, working professionals & career gaps from India, USA and UK.
Cohort Start Date
27th Feb, 2026
Time Commitment
1.5-2Hours/Day
Program Duration
5 Months
Course Fee
44999/- (Including GST)
Avail NO COST EMI
We Help You Make Career Switch into Data Analytics
Data Analytics Course Real-Projects with Internship
The career outlook is exceptionally strong, with the global market valuation surpassing $350 billion in 2026 a massive jump from previous years.
Data analytics is the science of analyzing raw data to find patterns and answer questions. In 2026, it has moved far beyond simple spreadsheets; it is now the “brain” behind business strategy, powered by AI and real-time processing
What is Data Analytics:
At its core, data analytics is the process of examining, cleaning, transforming, and modeling data to discover useful information. It is generally categorized into four types that build upon each other:
Descriptive (What happened?): Summarizing historical data to identify trends (e.g., a monthly sales report).
Diagnostic (Why did it happen?): Drilling down into data to find causes (e.g., why did sales drop in a specific region?).
Predictive (What might happen next?): Using statistical models and ML to forecast outcomes (e.g., predicting customer churn).
Prescriptive (What should we do about it?): Recommending specific actions to achieve a goal (e.g., optimizing supply chain routes).
Analyze historical transaction data to segment customers using RFM (Recency, Frequency, Monetary) analysis.
Use anonymized hospital data to identify patterns in patient readmissions. Analyze the correlation between demographic factors, previous treatments, and recovery times to optimize bed allocation.
Analyze GPS and telematics data from delivery fleets to identify bottlenecks and inefficient routing.
Scrape or use APIs to gather brand mentions across Twitter (X), Reddit, or LinkedIn. Perform Natural Language Processing (NLP) to categorize public sentiment and compare it against three main competitors.
Analyze internal survey data and exit interviews to understand why employees leave. Identify “flight risk” departments based on overtime hours, tenure, and performance ratings.
A retail brand spends $5M monthly across TV, YouTube, Meta, and Search, but they don’t know which channel is “wasting” money and which is driving the most incremental sales.
Instructors Real-World AI & Data Analytics Projects Experience
Guided Hands-On Job-Switch program designed by AI Solutions Architect & Generative AI & Agentic AI Consultant Rajeev Kanth.
This program bridges the gap between experimental AI and production-grade Agentic Systems.
Master the modern 2026 stack, LangGraph, CrewAI, Groq, and Vector DBs, while integrating with enterprise pipelines like Databricks, Kafka, and Kubernetes. Build scalable, governed, and self-improving AI agents.
Real-World Projects Developed by Rajeev Kanth
Autonomous Enterprise Agents: Designed multi-agent systems using LangGraph for automated supply chain reconciliation and real-time inventory optimization.
Scalable RAG Architectures: Implemented high-throughput Retrieval-Augmented Generation for Pharmaceutical Products with advanced chunking and evaluation frameworks (RAGAS, TruLens).
Agentic DevOps & SRE: Built self-healing infrastructure agents that monitor, triage, and remediate cloud infrastructure optimization & security incidents autonomously
Multi-Modal Generative Workflows: Developing systems capable of generating synthetic customer data, high-fidelity video, and audio for hyper-personalized marketing at scale.
MLOps 2.0: CI/CD for LLMs, LLMOps, Model Monitoring, and Versioning with MLflow and Ray.
Responsible AI: Implementing bias detection, hallucination mitigation, and compliance guardrails (GDPR, ISO 42001, SOC2).
Data Engineering for AI: Building feature stores and real-time streaming pipelines to feed autonomous decision engines.
- Experience in Building End-to-End analytics solutions through requirements gathering, establishing key metrics, building data models, designing dashboards and visualizations, and analyzing data to drive actionable insights for our customers and internal teams.
150+ Corporate Bootcamps: Trusted by like Walmart, Suzlon, DU Dubai, XFab Malaysia, Barclays, Bank of America, Merck, Dell, Yes Bank, Kotak, Nokia, EY, Cognizant, and BMW.
Founder of BEPEC: Pioneered the “Experience Building” model, helping thousands of learners transition into AI roles through 60+ live AI solutions and real-time POCs.
About Data Analytics Course in Bangalore
Best Data Analytics course in Bangalore. At BEPEC, we don’t just teach tools; we teach decision-making. The curriculum is updated for the 2026 job market, ensuring you learn exactly what top MNCs and startups in Bangalore are hiring for right now.
In the Data Analytics course in Bangalore we transform learners from Zero To Job-Switch in 120 Days as Data Analyst by Teaching Python, SQL, Data Science Insights, Power BI, Tableau, Excel, Snowflake, Generative AI, Microsoft Fabric, R, Data Visualization, Statistics & Probability, Inferential Statistics, Descriptive Statisitcs, Regression Analysis, Forecasting, KPIs and Dashboard Development.
Experience Building Course: Most courses give you a certificate; we give you a portfolio. Our unique training model immerses you in real-world simulations. You won’t just watch videos—you will work on live business cases involving:
Retail & E-commerce: Analyzing sales trends and customer behavior.
BFSI (Banking): detecting credit risk and loan default patterns.
Healthcare: Optimizing patient flow and resource allocation.
Supply Chain: Streamlining inventory using predictive analysis.
What you will learn in 2026
Our comprehensive syllabus covers the end-to-end data pipeline, from raw data collection to impactful storytelling.
Advanced Excel for Analytics: Master the tool that runs the world. Learn VLOOKUP, HLOOKUP, Pivot Tables, and advanced dashboarding techniques to handle quick data crunches.
SQL (Structured Query Language) & Snowflake : Data lives in databases. You will learn to write complex queries, perform joins, and manage databases using MySQL/PostgreSQL. This is the #1 skill recruiters look for.
Data Visualization (Power BI & Tableau): Transform boring numbers into stunning, interactive dashboards. Learn DAX functions, data modeling, and how to present insights to stakeholders confidently.
Python for Data Analysis: Move beyond drag-and-drop. Master Python libraries like Pandas and NumPy to clean, manipulate, and analyze massive datasets that Excel can’t handle.
Statistics & Probability: Build a strong foundation in the math behind the analysis. Understand hypothesis testing, distributions, and correlation to ensure your insights are statistically significant.
Generative AI for Analysts: New for 2026! Learn how to use AI assistants (like ChatGPT & Copilot) to write SQL queries faster, debug Python code, and automate routine reporting tasks.
Why Choose BEPEC for Data Analytics Training in Bangalore?
Finding the right Data Analytics training institute in Bangalore can be challenging. BEPEC stands out by offering a practical, project-based learning approach that mirrors real-world industry demands.
Experience Building: Don’t just learn; build a portfolio on Data Analytics. We focus on creating live Proof of Concepts (POCs) that you can showcase on LinkedIn and your resume.
Live Instructor-Led Training: Learn directly from industry experts with 12+ years of experience.
Career Switch Focus: tailored for professionals seeking a 60-100% salary hike and a smooth transition into Data Science and AI roles.
Course Curriculum - Data Analytics Course in Bangalore
Definition, scope, and importance of Data Analytics
Distinction between Data Analytics, Data Science, and Business Intelligence
The four types of Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
Overview of the Data Analytics Lifecycle (Discovery, Preparation, Planning, Building, Communicating, Operationalizing)
Data types: Structured vs. Unstructured vs. Semi-structured
Levels of measurement: Nominal, Ordinal, Interval, and Ratio
Data Collection methods: APIs, Web Scraping, Databases, Surveys, and IoT
Introduction to the Analytics Tech Stack: Excel, SQL, Python/R, and BI Tools (Tableau/Power BI)
- Introduction to Python
- Why Python, Value, Variable, Function, Library [Roadmap on Python]
- IDE in Python, Different Data Types
- List, Tuple, Set & Dictionary Overview
- Different List Methods
- Different Tuple Methods
- Set & Frozenset
- Dictionary & String Manipulations
- Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda
- Types of Operators, Conditional Statements
- While Loop, List Comprehension, Break, Continue, Arguments
- Functions, Escape Sequences, Lambda Functions
- Hackathon-1
- Iterators, Decorators & Generators
- Modules in Python
- Creating Custom Python Libraries
- Lambda, Map, Filter, Reduce
- Exception Handling
- File Handling
- Regular Expressions (Regex)
- Web Scraping Basics
- Introduction to OOPS
- Instance Variable, Class Variable, Class Method
- Association vs Composition & Aggregation
- Oops Concept
- Encapsulation, Inheritance
- Polymorphism, Method Overloading, Method Overriding
- Introduction to Pandas
- Data Analysis using Pandas
- Introduction to Numpy
- Different Numpy Commands
- Introduction to Data Visualisation
- Data Visualisation using Matplotlib
- Data Visualisation using Seaborn
- Data Visualisation using Plotly
- Why Data Cleaning?
- Data Cleaning with Sklearn & pandas
- Regular Expression Basics
- Statistics using Scipy Library
- Introduction to Advanced DSA
- Non-Primitive Data Structures
- Non-Linear Data Structures
- What is an Algorithm
- Theory & Code Implementation of Linked List
- Stacks & Queues Assignment
- Coding Stack Data Structure
- Coding Queue Data Structure
- Tree Data Structures
- Types of Tree Data Structure
- Tree Traversal
- BFS Traversal
- Bubble Sort Theory
- Bubble Sort Code Implementation
- Selection Sort Theory
- Selection Sort Code Implementation
- Insertion Sort Theory
- Insertion Sort Code Implementation
- Merge Sort Theory
- Merge Sort Code Implementation
- Quick, Merge Sort Performance
- Quick Sort Theory
- Linear Search & Bisection Search
Pandas DataFrames for Coding Interviews
Data Cleaning and Imputation Logic
GroupBy and Aggregation Coding
Merging, Joining, and Concatenating DataFrames
Reshaping Data (Melt, Pivot)
NumPy Array Broadcasting
Vectorized Operations vs Loops
Handling DateTime Objects in Python
String Manipulation with Regex
- What is SQL, RDBMS, and Table Structure
- Understanding Sprint, Scrum and Agile Project Breakdown in SQL
- OLTP vs OLAP
- Data Warehousing Concepts
- Data types (INT, VARCHAR, DATE, BOOLEAN, etc.)
- ER Diagrams
- Data Models like Star Schema and Snowflake Schema
- DDL vs DML vs DCL vs TCL Commands
- Basic CRUD Operations — 41:21
- Different DDL Commands — 41:22
- Different DML Commands
- Upsert Operations
- Different DQL Commands
- Database Constraints
- Aggregate Functions, Date Functions and String Functions
- SQL Joins: Inner, Self, Cross, Left, Right and Outer Join
- SQL Grouping & Aggregations
- SubQueries and Types of SubQueries
- Window Functions in SQL
- Data Integrity & Referential Integrity
- Data Normalisation
- First & Second Normal Form
- Functional Dependency & Transitive Dependency
- Boyce Codd Normal Form
- Denormalization
- Temporary Tables, CTE, Recursive CTE
- When to Use Temporary Table, CTE, Recursive CTE
- SubQuery in MySQL
- Views in MYSQL
- Stored Functions
- Stored Procedures
- Triggers in MySQL
- Create Events
- In-depth DDL Commands
- Different Functions in MySQL
- What is Data? Various Types of Data
- Levels of Measurement
- Categorical Variables & Visualisation Techniques
- Continuous Variables and Visualisations
- Measures of Central Tendency
- Measuring Skewness
- Measures of Dispersion
- Covariance & Correlation Coefficient
- Box & Whisker Plot
- Outlier Identification
- Treating Outliers
- Sampling Techniques
- Statistical Analysis Consideration
- Types of Analytics
- Sample vs Population
- Introduction to Inferential Statistics
- Law of Large Numbers
- Central Limit Theorem
- Probability Distributions
- Conditional Probability
- Continuous Probability Distribution & Discrete Probability Distribution
- PMF, CDF, PDF, PPF in Probability
- Binomial Distribution
- Poisson Distribution
- Normal Distribution
- Exponential Distribution
- Laplace Distribution
- Log-Normal Distribution
- Standard Error
- Confidence Interval Calculations
- Z-Table for Confidence Interval
- T-Table for Confidence Interval
- Hypothesis Testing
- Null & Alternate Hypothesis
- Type-1 Error & Type-2 Error
- 2-Sample T Test
- ANOVA Test
- p-Value Calculations
- Chi-Square Test
- Project Demonstration using all the Statistical Techniques
- Basics of Statistics
- Central Limit Theorem, Normal Distribution, Skewness
- Real-Time Project Demonstration on EDA with Real-World Project
- End-to-End Statistics using Excel
- Probability Distribution, Random Variable, Binomial, Poisson, Exponential
- Normal Distribution
- Real-Time Project Demonstration on Probability
- Hypothesis Testing Part-1
- Hypothesis Testing Part-2
- How to Calculate P-Value? Coding Hypothesis Testing
- How to solve PDF, CDF, PMF?
- Chebyshev’s, Log, Power Law, Q-Q, CLT
- Supervised vs Unsupervised Learning
- Fundamentals of Machine Learning Part-1
- Fundamentals of Machine Learning Part-2
- Insights from Train & Test Accuracy
- Associate Rules Part-1
- Associate Rules Part-2
- Problem Identification & Approach Designing
- Associate Rules Scripting
- Why to do Dimensionality Reduction? Project Implementation
- How to Prepare Machine Learning Math for Interviews
- Why to use Linear Regression?
- What is Linear Regression
- Case Study on Linear Regression
- Math Behind Linear Regression Part-1
- Math Behind Linear Regression Part-2
- Math behind OLS Technique
- Assumptions of Linear Regression
- Evaluation Metrics for Regression Model
- Accuracy Improving Techniques
- Coding Linear Regression Model
- Regularisation Techniques
- Why Logistic Regression
- Math Behind Logistic Regression
- Evaluation Metrics Behind Classification Algorithms
- ROC & AUC Curve
- Coding Logistic Regression Model
- Introduction to Decision Tree
- Intuition Behind Decision Tree
- Math Behind Decision Tree
- Math Behind Decision Tree using GINI
- Drawbacks of Decision Tree
- Random Forest and Gradient Boosting
- Coding Decision Tree, Random Forest, GB
- SMOTE Technique to Handle Imbalanced Dataset
- Filter Method, Wrapper Method, Embedded Method Feature Selection Techniques
- Coding Feature Selection Techniques
- Math Behind KNN
- Math Behind SVM
- Plotting SVM
- Additional Topics
- Introduction to PCA
- Math behind PCA
- Coding PCA
- AutoML Using Pycaret
Introduction to Business Intelligence and Power BI
Power BI Desktop, Service, and Mobile overview
Installation and setup of Power BI Desktop
Connecting to data sources (Excel, SQL Server, Web, CSV, etc.)
Import vs. DirectQuery vs. Live Connection
Introduction to Power Query Editor
Data profiling and quality check (Column distribution, quality, and profile)
Data cleaning and transformation (Remove rows/columns, split columns, change data types)
Handling missing values and errors
Unpivoting and pivoting columns
Merging and appending queries
Creating conditional and custom columns
Introduction to M language basics
Data Modeling concepts (Star Schema vs. Snowflake Schema)
Managing relationships (One-to-One, One-to-Many, Many-to-Many)
Cross-filter direction and active/inactive relationships
Creating calculated columns and measures
Introduction to DAX (Data Analysis Expressions)
DAX syntax and operators
Common DAX functions: Aggregation (SUM, AVERAGE, COUNT)
Logical functions (IF, SWITCH, AND, OR)
Text functions (LEFT, RIGHT, CONCATENATE)
Date and Time functions (YEAR, MONTH, DATEDIFF, CALENDAR)
Time Intelligence functions (TOTALYTD, SAMEPERIODLASTYEAR, DATEADD)
Filter functions (CALCULATE, FILTER, ALL, ALLEXCEPT)
Iterator functions (SUMX, AVERAGEX)
Data Visualization best practices
Working with standard visuals: Bar charts, Line charts, Pie charts, Donut charts
Using Maps (bubble maps, filled maps) and Matrix visuals
Cards, Gauges, and KPI visuals
Slicers and Filters (Visual-level, Page-level, Report-level)
Drill-down and Drill-through functionality
Custom tooltips and report page tooltips
Formatting visuals (Colors, titles, backgrounds, borders)
Using Themes and custom visuals from the Marketplace
Bookmarks, Selection Pane, and Buttons for navigation
Creating and managing Hierarchies
Introduction to Power BI Service
Publishing reports from Desktop to Service
Creating Dashboards vs. Reports
Pinning visuals and live pages to dashboards
Workspaces (My Workspace vs. App Workspaces)
Sharing reports and dashboards
Configuring On-premises Data Gateway
Scheduled Refresh and data dataset settings
Row-Level Security (RLS) implementation
Power BI Mobile app features and layout
Analyzing in Excel
Publishing to Web (Public vs. Secure Embed)
Creating Power BI Apps
Performance Analyzer and optimization techniques
Introduction to Microsoft Fabric architecture and OneLake
Understanding Fabric tenant settings, capacities, and compute engines
Managing workspaces, roles, and permissions
OneLake concepts: shortcuts, file explorer, and data organization
Overview of Data Factory in Fabric (Pipelines vs. Dataflows Gen2)
Building data pipelines for orchestration and data movement
Transforming data using Dataflows Gen2 (Power Query Online)
Connecting to on-premises and cloud data sources
Introduction to Synapse Data Engineering
Creating and managing Lakehouses
Working with Apache Spark pools and environments
Using Fabric Notebooks for data engineering (PySpark, SQL, Scala)
Implementing Medallion Architecture (Bronze, Silver, Gold layers)
Working with Delta Tables and V-Order optimization
Introduction to Fabric Data Warehouse
Differences between Warehouse, Lakehouse, and SQL Analytics Endpoint
Writing T-SQL for data loading and transformation
Cross-database querying within OneLake
Performance tuning and distribution strategies in Warehouse
Introduction to Real-Time Intelligence
Setting up Eventstreams for streaming data ingestion
Creating and querying KQL Databases (Kusto Query Language)
Building Real-Time Dashboards and setting alerts with Data Activator
Introduction to Data Science in Fabric
Exploratory Data Analysis (EDA) using Data Wrangler
Training and tracking machine learning models with MLflow
Using Semantic Link to bridge Spark and Power BI
Power BI integration: Direct Lake mode vs. Import vs. DirectQuery
Creating and managing Semantic Models in Fabric
Implementing Row-Level Security (RLS) and Object-Level Security (OLS)
Application Lifecycle Management (ALM) with Git integration
Deployment Pipelines for dev, test, and prod environments
Monitoring, logging, and auditing Fabric usage
Governance and lineage with Microsoft Purview
Introduction to Excel interface, ribbons, and the Quick Access Toolbar
Essential keyboard shortcuts for navigation and selection
Understanding cell referencing: Relative, Absolute, and Mixed
Formatting cells, rows, and columns for readability
Custom number formatting (Dates, Currencies, Percentages)
Conditional Formatting using rules, data bars, and color scales
Data Validation and creating drop-down lists
Text functions for cleaning: TRIM, PROPER, UPPER, LOWER, CLEAN
Text manipulation functions: LEFT, RIGHT, MID, FIND, LEN, SUBSTITUTE
Combining text: CONCAT, TEXTJOIN, Flash Fill
Date and Time functions: TODAY, NOW, DATEDIF, NETWORKDAYS, EOMONTH
Logical functions: IF, IFS, AND, OR, NOT, IFERROR, SWITCH
Statistical aggregation: SUM, AVERAGE, COUNT, MAX, MIN
Conditional aggregation: SUMIF, SUMIFS, COUNTIF, COUNTIFS, AVERAGEIF
Lookup and Reference: VLOOKUP, HLOOKUP
Modern Lookup functions: XLOOKUP, XMATCH
Advanced Reference: INDEX and MATCH combination
Dynamic Array functions: UNIQUE, SORT, FILTER, SEQUENCE
Handling errors (#N/A, #VALUE!, #DIV/0!) effectively
Finding and removing duplicates
Using “Text to Columns” for data parsing
Working with Excel Tables and structured references
Sorting data (Multi-level sort) and Filtering
Advanced Filter and extracting unique records
Subtotals and Grouping/Ungrouping data
Creating and customizing PivotTables
Summarizing data: Sum, Count, Average, Min, Max
Value Field Settings: % of Grand Total, % of Row, Running Total
Grouping data in PivotTables (Dates, Numbers)
PivotCharts and formatting for insights
Adding Slicers and Timelines for interactive filtering
Creating Calculated Fields and Items in PivotTables
Introduction to Data Visualization principles
Standard Charts: Column, Bar, Line, Pie, Area
Advanced Statistical Charts: Histogram, Pareto, Box & Whisker
Hierarchical Charts: Treemap, Sunburst
Combo Charts and using a Secondary Axis
Sparklines and Trendlines for trend analysis
Introduction to Power Query (Get & Transform)
Importing data from CSV, Web, and Folder sources
Power Query transformations: Unpivot, Split Column, Merge Queries, Append Queries
Introduction to Power Pivot and the Data Model
Creating relationships between tables in Data Model
Basic DAX measures for Power Pivot
What-If Analysis tools: Goal Seek and Data Tables
Scenario Manager and Solver Add-in for optimization
Introduction to Macro recording and basic automation
Protecting worksheets, workbooks, and ranges
Designing interactive Dashboards
Introduction to Snowflake Cloud Data Platform architecture
Separation of Storage, Compute, and Services layers
Understanding Snowflake Editions and Credit usage
Navigating the Snowsight Web Interface and Worksheets
Virtual Warehouses: Sizing, Scaling, and Multi-cluster warehouses
Auto-suspend and Auto-resume configurations for cost optimization
Creating Databases, Schemas, and Tables (DDL)
Understanding Micro-partitions and Data Clustering
Loading data using the Web Interface and SnowSQL
Creating and managing Internal and External Stages (S3, Azure, GCS)
Defining File Formats (CSV, JSON, Parquet, Avro, ORC)
Executing the COPY INTO command for bulk data loading
Handling data load errors and using the VALIDATION_MODE
Unloading data from Snowflake to external locations
Querying Semi-structured Data (JSON, XML) using the VARIANT data type
Extracting data using Dot Notation and Bracket Notation
Using FLATTEN, PARSE_JSON, and LATERAL functions
Time Travel: Querying historical data using AT and BEFORE clauses
Undropping tables, schemas, and databases
Understanding Fail-safe and Data Retention periods
Zero-Copy Cloning: Creating instant copies of tables and databases
Caching mechanisms: Result Cache, Metadata Cache, and Warehouse Cache
Creating Standard Views and Secure Views
Introduction to Materialized Views for performance
Using Window Functions and Common Table Expressions (CTEs)
Sampling data using BERNOULLI and ROW methods
Introduction to User-Defined Functions (UDFs) and Stored Procedures
Secure Data Sharing and creating Reader Accounts
Accessing and using the Snowflake Data Marketplace
Introduction to Snowpipe for continuous data ingestion
Implementing Change Data Capture (CDC) using Streams and Tasks
Analyzing Query Profile for performance tuning and optimization
Search Optimization Service and Query Acceleration Service
Role-Based Access Control (RBAC) and managing privileges
Setting up Network Policies and Multi-Factor Authentication (MFA)
Connecting Snowflake to BI tools (Power BI, Tableau) and Python
What is the fee for an Data Analytics course in Bangalore?
Get Group Discount & Corporate Training Discounts
Google Ratings & Trustpilot Reviews!
“I have enrolled for the Data Science Career Transition Program through Bepec Kanth explains how to apply all the concepts in real world. Along with developing domain knowledge he is also helping to enhance our professional skills to face real world challenges as data scientist... What ? Why? Where ?.....3 words most important”
SREE SAI KONDLE
“First of all, I clearly got to know the role of DA/DS ,previously joined to one institute but i didnt get enough knowledge + no real time projects So, I joined here in oct batch everything is so perfect the way Kanth sir teaches and it is more like one should be consistent in learning. When it comes it support team they will always help u at ur queries will update about placement once i got placed.”
Harshini
“Hi I am grateful to Kanth and his team who worked very hard for career transition program into data science and AI field, the program was very smooth starting from the scratch to pro level. As I belong to mechanical field even I grabbed the insights very easily I think one must go for their program without any hesitations. I hope I would become principal data scientist one day. Thank you Kanth and his team”
Mayur Thakare
Data Analytics (AI) Exam & Certification in Bangalore
Our career-focused Data Analytics course in bangalore designed to help you stand out in today’s competitive job market. Whether you’re entering the field or upskilling, this program offers a hands-on learning experience that mirrors the demands of leading companies.
From mastering Python, SQL, Power BI, Tableau, Excel, Statistics, Probability, Prompt Engineering, Generative AI, Microsoft Fabric and Machine Learning to building predictive models and interpreting real-world datasets, you’ll gain in-demand skills that translate directly to the workplace. Our expert-led training ensures you’re equipped with practical tools, not just theory.
You’ll also complete real-time projects based on actual industry case studies—giving you the confidence to tackle business problems across domains such as finance, healthcare, and e-commerce.
To ensure you’re ready, the course concludes with a comprehensive assessment inspired by real interview and certification standards.
This certification is valued by over 30+ global employers, including top firms like The MathCo, Walmart, Honeywell, Bank of America, Tiger Analytics, Merck, Allianz, ToTheNew, Yes Bank, Kotak, HDFC, Suzlon —helping you showcase your capabilities and accelerate your career.
Frequently Asked Questions
We provide comprehensive placement assistance, including resume building, mock interviews with industry experts, and direct referrals to our 500+ hiring partners in Bangalore’s tech hubs like Electronic City and Whitefield. While “guarantees” vary by profile, our 95% placement track record ensures you are job-ready.
In Bangalore, a fresher can expect a starting salary between ₹4.5 LPA to ₹8 LPA. With 2+ years of experience, professionals often scale to ₹12 LPA+. Our curriculum is designed to help you negotiate top-tier packages by mastering in-demand tools like Power BI, SQL, and Python.
Our industry-aligned curriculum covers the full modern data stack:
Data Visualization: Tableau & Power BI
Programming: Python (Pandas, NumPy, Matplotlib)
Databases: Advanced SQL & NoSQL
Spreadsheets: Advanced Excel & VBA
Bonus: Introduction to Generative AI for Data Analytics.
Recruiters focus on your projects with ROI(return on investment) (Most of the learners dont have ROI based Projects) + They ask Why and When?(Most of the learners Fail Here)
In BEPEC, We give Projects with ROI by making you to work on our Client Projects as Interns/Freelancers
Yes, This Course is Designed for Beginners. We start from fundamentals to Solid Level Understanding with End-to-End projects on Python, SQL, Statistics, Snowflake, Machine learning, Deep Learning, Generative AI, Agentic AI, MCP, Langchains, Langgraphs, Azure, AWS Bedrock, LLMOps, RAG, Prompt Engineering, Fine-Tuning, SLMs, LLMs and Pre-Training
Yes, you can place BEPEC Projects in Resume & LinkedIn. Lot of PG Learners, Freshers and Working professionals crack jobs by placing our Projects in Resume.
No, We Teach Python from Basics. We guide you on installation of Python in Your System
No, With BEPEC Course you will get placed with Salary Hike and Better Designation in AI, Gen AI & Data Science.
To Learn How? Book 1:1 Call with Trainer
- BEPEC Projects & BEPEC Strategy helps you get 10X Interview Calls
- BEPEC Shares your Resume with 150+ Hiring Partners
- BEPEC ATS Resume with Job Portal and LinkedIn Optimization gets you more interview calls with PUSH & PULL Strategy
- BEPEC Mock Interviews helps you speak in interviews with confidence and you can crack multiple offers!
Our Instructors are working AI Engineers, AI Researchers, AI Architects, Head of AI, Data Scientist who are working and delivering trainings as freelancers.
Head Trainer Kanth:
- Kanth Worked as a Principal Data Scientist with proficiency in Machine learning, Deep Learning, Generative AI, and LLMs, with around 12+ years of involvement in conveying end-to-end project pipelines using AGILE CRISP, DataOps, Big Data Streaming Pipelines, and MLOps Pipelines
- Kanth is an AI CoE and Six Sigma Certified. He is an AI Consultant for companies like Nokia, EY, Cognizant, BMW, DU Telecom etc. Kanth delivered AI Solutions using AI Software Stack.
- Kanth has experience in delivering customized training on statistics, cloud computing, machine learning, deep learning, Generative AI, Data Engineering, Data Analytics, Data Science, and Reinforcement learning.
- Delivered Training Across Dubai, Malaysia, Singapore, South Africa, Sudan etc.
- Helped various organisations to implement Agentic AI & Gen AI using AI Software Stack & made them achieve tremendous financial gains
- Experience in Delivering 100+ corporate training sessions for working Data Scientists and AI Engineers to Upskill them for Future Projects.
- Experience in delivering 50+ PMP Programs, ITIL Programs, Agile Programs, etc
India – EY, Infosys, TechMahindra, ExlSolutions, Suzlon, Wipro, TCS, CSC, HCL, HP, Deloitte, IIT-Bhubaneswar, KIIT–Bhubaneswar, R&D – Hyundai
Corporate training across different location like Dubai(Etisalat), Malaysia ( X-Fab), Canada(X-Axis), Singapore(DBS Bank)
Colleges – Nearly 400+ Colleges all over India
on “Agentic AI and Future of Autonomous Systems.
