Master Data Analytics & BI! Gain expert skills in SQL, Python, Tableau & Power BI. Build pro dashboards, automate reports & lead business growth. Your premier career launchpad.
Level
Advanced
Duration
8 weeks



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Unlock the hidden value of your data. In today's global job market, data refinement is the ultimate skill set. Our Data Analytics and BI Course offers a deep dive into the technical and strategic world of modern business intelligence.
Real Skills. Real Results. Forget dry lectures. You’ll work with actual industry data to build a portfolio that proves you can optimize operations and increase ROI. Become the analyst that companies are looking for.
Working professional who is carrying more then 10 years of industry experience.
Access to updated presentation decks shared during live training sessions.
E-book provided by TechPratham. All rights reserved.
Module-wise assignments and MCQs provided for practice.
Daily Session would be recorded and shared to the candidate.
Live projects will be provided for hands-on practice.
Expert-guided resume building with industry-focused content support.
Comprehensive interview preparation with real-time scenario practice.
Data Acquisition & SQL Fundamentals
The first step focuses on how to communicate with databases. You will learn to extract, filter, and organize raw information from relational systems.
Statistical Foundations & Excel for Analytics
Before using complex code, you must master the mathematical logic of data. This phase covers essential statistics and advanced spreadsheet techniques for rapid analysis.
Programming with Python or R
Shift into high gear by using code to automate analysis and handle massive datasets. You will learn how to clean messy data and perform complex mathematical operations efficiently.
Business Intelligence & Data Visualization
Here, you turn numbers into narratives using industry-leading BI tools. You’ll focus on creating interactive dashboards that help stakeholders make real-time decisions.
Advanced Analytics & Predictive Modeling
Move beyond "what happened" to "what will happen." This phase introduces basic machine learning concepts to forecast trends and categorize behaviors.
Data Engineering (ETL) & Strategy
Learn the "behind-the-scenes" work of moving data from various sources into a warehouse. This ensures that your data is clean, reliable, and always ready for analysis.
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Comprehensive study materials and resources

Professional resume building session

Master your interview skills

Real-world project demonstrations
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IT Professionals
Non-IT Career Switchers
Fresh Graduates
Working Professionals
Ops/Administrators/HR
Developers
BA/QA Engineers
Cloud / Infra
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Junior Data Analyst
Data Visualization Specialist
Reporting Analyst
Business Intelligence (BI) Analyst
BI Developer
Marketing / Financial Analyst
Analytics Engineer
Database Administrator
Junior Data Analyst
Data Visualization Specialist
Reporting Analyst
Scenario: Analyze Noida-based employee data to optimize "bench" utilization, identifying skill gaps in cloud and AI to reduce hiring costs and improve project deployment speed.
Scenario: Monitor high-volume digital transactions across the Delhi-NCR region to detect suspicious merchant activity and reduce financial losses during peak festive sales.
Scenario: Track the performance of 5,000+ support agents in Tech Mahindra's Noida campus by measuring First Call Resolution (FCR) and CSAT scores against global benchmarks.
Scenario: Streamline global insurance reporting for EXL’s Noida-based audit teams by automating the consolidation of multi-currency financial statements into a single Power BI dashboard.


Additional Support We Provide
24/7 Support
LinkedIn Profile
Resume Writing
Alumni Sessions
Interview Preparation
Live Projects
What are the prerequisites for joining a Data Analytics and BI course?
How does Data Analytics differ from Business Intelligence?
How do I become a Data Analyst in 2026 with no prior experience?
What is the most important part of the data analysis workflow?
What are "Dark Data" and "Data Fabric" in 2026?
What is a Capstone Project and why is it important?
What is the difference between a JOIN and a UNION?
What is the "Logical Order of Execution" in an SQL query?
What is the difference between a "Measure" and a "Calculated Column"?
How do you handle a dashboard that is running too slowly?
How do you handle missing or "dirty" data in a dataset?
Explain the difference between "Descriptive" and "Predictive" analytics.

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