Programme Parameters
Advanced Postgrad
3 Months (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 Data Scientist Certification Program is a structured, career-focused path through the full data-science skill set. You build the foundation — Python, statistics and probability, and wrangling messy real data — then move through exploratory analysis, visualisation and the core of machine learning: building, evaluating and tuning models. Crucially, the program also develops the skills that distinguish strong data scientists: framing business problems and communicating results. It culminates in a capstone project that demonstrates the whole workflow. You finish credentialed and able to work as a data scientist. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This certification program builds end-to-end data-science competency — from Python, statistics and data wrangling through machine learning to communication and a capstone project.
📋 Course Objectives
1. Build a foundation in Python, statistics and data wrangling.
2. Perform exploratory analysis and visualisation.
3. Build, evaluate and tune machine-learning models.
4. Frame problems and communicate results.
5. Deliver an end-to-end capstone project.
👥 Who Should Enroll?
• Aspiring data scientists and career-switchers
• Analysts moving into data science
• Graduates entering the field
• Anyone building a data-science career
🚀 Key Learning Outcomes
• End-to-end data-science competency.
• A capstone project and portfolio.
• A credential for data-science roles.
• 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
From Business Question to Analysis Plan
• Turning a vague request into a testable question with a defined decision
• Choosing a success metric that matches the decision, not the model
• Recognising problems that do not need machine learning
Acquisition, Quality and Preparation
• SQL for analytical workloads: joins, window functions, aggregation at scale
• Data quality assessment and honest handling of missingness
• Feature engineering and the leakage traps that inflate offline metrics
Supervised and Unsupervised Practice
• Regression and classification workflows with proper validation splits
• Regularisation, hyperparameter search and overfitting diagnosis
• Clustering and dimensionality reduction, and their instability
• Interpretability with SHAP and partial dependence for stakeholder trust
Experimentation and Causal Questions
• A/B test design, power analysis and stopping rules
• Quasi-experimental methods when randomisation is impossible
• Distinguishing correlation from a claim that will drive a decision
Communication and Handover
• Visualisation that supports a decision rather than decorating a deck
• Communicating uncertainty to non-technical stakeholders
• Packaging analysis for reproducibility and handover to engineering
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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