Programme Parameters
Advanced Postgrad
3 Days (4.5 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
This three-day course covers essential data science techniques using Python, from data cleaning and EDA to advanced data visualization and predictive analytics. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This three-day course covers essential data science techniques using Python, from data cleaning and EDA to advanced data visualization and predictive analytics.
📋 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
Python for Analysis
• pandas for loading, cleaning and reshaping tabular data
• NumPy vectorisation and avoiding slow row-wise loops
• Notebook discipline: execution order, hidden state and reproducibility
Understanding a Dataset
• Profiling, missingness patterns and outlier investigation
• Exploratory visualisation with matplotlib and seaborn
• Forming hypotheses from exploration without confirming them on the same data
Inference for Analysts
• Sampling, confidence intervals and bootstrap methods
• Hypothesis testing, multiple comparisons and practical significance
• Correlation, confounding and the limits of observational analysis
Prediction With scikit-learn
• Pipelines, encoding and preventing leakage across the split
• Regression and classification with honest validation
• Feature importance and its frequent misinterpretation
Communicating Analysis
• Dashboards and reports that answer a stated question
• Presenting uncertainty to decision-makers
• Packaging an analysis for handover and rerun
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python 3 |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | VS Code |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Flask/Django |
| Covered Tool / Platform | Git |
Frequently Asked Questions
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