Programme Parameters
Graduate / Intermediate
5 Days (7.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Participants will learn the fundamentals of business intelligence and explore popular BI tools such as Tableau, Power BI, and Google Data Studio. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Participants will learn the fundamentals of business intelligence and explore popular BI tools such as Tableau, Power BI, and Google Data Studio.
📋 Course Objectives
1. Develop hands-on skill in Power BI.
2. Translate AI Enablement theory into practical, reproducible analysis.
3. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
• Data and computational scientists moving into Power BI
🚀 Key Learning Outcomes
• Confidence to reason about Power BI in real projects.
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
What BI Adds to Research Work
• Dashboards for monitoring against ad hoc analysis for answering a question
• Tableau, Power BI and Looker Studio compared on cost and licensing
• When a BI tool is the wrong choice and a script is better
Preparation and Modelling
• Connecting sources, extracts against live connections and refresh scheduling
• Tidy data, star schemas and why one wide spreadsheet breaks down
• Power Query and joins, with the fan and chasm traps that inflate totals
Measures and Logic
• Calculated fields, DAX measures and row against filter context
• Aggregation errors: averaging an average, and totals that do not reconcile
• Time intelligence for period comparison
Dashboards People Can Read
• Chart selection matched to the question, and charts to avoid
• Layout, hierarchy and the small number of numbers a dashboard can carry
• Colour used for meaning, with accessible palettes
Sharing and Trust
• Publishing, row-level security and controlling who sees what
• Documenting definitions so two dashboards do not disagree
• Version control, validation against source and retiring stale reports
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
