Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Basics of Statistical Modeling course is a free, beginner-friendly self-paced program designed to introduce learners to how statistical methods are used to understand data and build simple predictive models. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Basics of Statistical Modeling course is a free, beginner-friendly self-paced program designed to introduce learners to how statistical methods are used to understand data and build simple predictive models.
📋 Course Objectives
1. Translate Artificial Intelligence theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 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
Introduction to Statistical Modeling
What is Statistical Modeling? • Role of Statistics in Data Science • Types of Models and Applications • Examples from Real-World Data
Understanding Data and Variables
Types of Variables: Numerical and Categorical • Independent vs Dependent Variables • Data Distribution Basics • Importance of Data Quality
Basic Statistical Concepts
Mean, Median, and Variance • Probability Basics • Understanding Relationships in Data • Introduction to Correlation
Regression and Modeling Basics
Introduction to Regression • Understanding Simple Linear Relationships • Interpreting Model Outputs • Basic Prediction Concepts
Applications and Next Steps
Statistical Modeling in Business, Healthcare, and Research • Using Models for Decision-Making • Career Pathways in Data Science and Analytics • Mini Learning Activity / Concept-Based Practice
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Statistical Modeling |
| Covered Tool / Platform | Data Analysis |
| Covered Tool / Platform | Regression |
| Covered Tool / Platform | Probability |
| Covered Tool / Platform | Data Interpretation |
Frequently Asked Questions
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