Data Analytics

About the Course

Program Aims To
• Train students in evidence-based decision making
• Democratize access to analytics education through hands-on learning
• Build a workforce that applies data to drive change in society and industry
Target Audience
• Engineering graduates and tech professionals
• Faculty members of academic institutions seeking to enhance their instructional repertoire
• Individuals looking to transition into high-demand technology careers
Program Philosophy
• Hands-on Learning: Real-world datasets and practical applications
• Expert Mentorship: Guidance from 30+ seasoned industry mentors
• Technical Excellence: Meaningful problems that blend statistics, code, and context
• Real-world Projects: 30+ industry-relevant projects applying various technologies
Common Program Features
• Individuals with full 6-month availability
• Basic understanding of programming and mathematics (varied by program)
Delivery Mode
• Online delivery with live sessions
• Compulsory attendance for live sessions
• Time commitment: Minimum 48 hours per week
Selection Process
• Registration and awareness workshops
• Aptitude test
• Final interview
• Onboarding for successful candidates
Program Timings
• Live sessions commence at 8:00 AM
• 2-3 classes daily, each lasting 1-1.5 hours
• Weekly catch-up sessions with mentors
• Expert sessions (compulsory attendance)
What you'll Learn

Syllabus / Curriculum

• Module 1: Module 1: PYTHON PROGRAMMING

  Writing code to solve problems and automate solutions is a crucial skill for anyone in IT. This module teaches Python programming and its applications in system administration, automation, and cloud computing. Learners will gain hands-on experience with Git and GitHub, troubleshooting, debugging, and best practices in software development. Applied Learning Project - Learners will complete a capstone project where they will automate system administration tasks using Python Prerequisites and Eligibility: This course is open to learners from all educational and professional backgrounds. No prior programming experience is required - beginners and experienced learners alike can benefit.
• Module 2: Module 2: SQL (STRUCTURED QUERY LANGUAGE)

  This module introduces learners to SQL and its applications in database management. SQL is a fundamental tool for querying, analyzing, and manipulating data in relational databases. Applied Learning Project - Learners will design and query a relational database to solve real-world data problems. Prerequisites and Eligibility: Basic understanding of data structures and programming concepts is helpful but not required.
• Module 3: Module 3: APPLIED MACHINE LEARNING

  This module provides hands-on experience with machine learning techniques and their applications Applied Learning Project - Learners will develop and fine-tune an ML model using real-world data Prerequisites and Eligibility: Python and statistics knowledge is recommended.
• Module 4: Module 4: POWER BI

  This module covers business intelligence and data visualization using Power BI, a leading analytics tool. Applied Learning Project - Learners will build an interactive business intelligence dashboard using real-world datasets. Prerequisites and Eligibility: Basic knowledge of Excel or databases is recommended.
• Module 5: Module 5: PYSPARK

  This module focuses on big data processing and distributed computing using PySpark. Applied Learning Project - Learners will process and analyze large datasets using PySpark. Prerequisites and Eligibility: Familiarity with Python and SQL is recommended.
• Module 6: Module 6: DATAIKU

  This module introduces Dataiku, a collaborative data science platform, allowing users to build machine learning models and automate workflows. Applied Learning Project - Learners will use Dataiku to build an end-to-end machine learning pipeline. Prerequisites and Eligibility: Basic knowledge of data analysis and Python is recommended.
• Module 7: Module 7: APPLIED LARGE LANGUAGE MODELS

  This module explores the applications of Large Language Models (LLMs) in NLP and AI-driven analytics. Applied Learning Project - Learners will fine-tune an LLM for a specific NLP task. Prerequisites and Eligibility: Familiarity with Python and machine learning concepts is recommended.
• Module 8: Module 8: CAPSTONE PROJECT

  Students will complete a real-world analytics project, using SQL and Dataiku for data collection, Python and PySpark for processing, and Power BI for visualization. They'll build predictive models, apply LLM tools for text analysis, and automate workflows. The project ends with a dashboard and presentation showcasing end-to-end analytical and business insights. Prerequisites and Eligibility: Those completing all core modules - including Python, SQL, Dataiku, PySpark, Applied Machine Learning, Applied LLM and Power BI - will be eligible to undertake the capstone. This ensures a solid foundation for delivering a complete, end-to-end analytics solution

Eligibility Criteria

Selection Process

ⓘ Shortlisted candidates will be notified via Email and SMS. Admit cards available on dashboard.

Fee Structure

* Scholarships may be available via NSP. Fee once paid is non-refundable except in cases of centre cancellation.

Faculty & Industry Partner

Centre Details