Programme Parameters
Graduate / Intermediate
4 Days (6 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
The Advanced Data Science Techniques for Academicians Program provides a deep dive into data science methodologies tailored for academic use cases. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Advanced Data Science Techniques for Academicians Program provides a deep dive into data science methodologies tailored for academic use cases.
📋 Course Objectives
1. Put Artificial Intelligence techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in Artificial Intelligence
• R&D engineers and working professionals applying Artificial Intelligence in industry
• Academics and educators building research or teaching capacity in Artificial Intelligence
🚀 Key Learning Outcomes
• Tangible, reproducible Artificial Intelligence work to show supervisors or employers.
• 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
Statistics for Publishable Claims
• Aligning analysis plan with research question before data collection
• Effect size, confidence intervals and moving beyond p-value reporting
• Preregistration and analytic flexibility as a source of false findings
Methods Common in Academic Work
• Multilevel and mixed-effects models for nested data
• Structural equation modelling and its assumptions
• Bayesian estimation and reporting posterior uncertainty
Non-Tabular Research Data
• Text mining and topic modelling for corpus-scale research
• Bibliometric and citation network analysis
• Content analysis with inter-coder reliability
Open Science Practice
• Reproducible pipelines and computational environment capture
• Data deposition, FAIR principles and licensing
• Responding to replication requests and sharing code responsibly
Writing and Reviewing
• Reporting statistical methods to journal standards
• Reading and reviewing quantitative papers critically
• Grant applications: justifying method and sample size
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | Seaborn |
| Covered Tool / Platform | Tableau |
| Covered Tool / Platform | SQL |
Frequently Asked Questions
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