PGP in Data Science

About this program

A flagship postgraduate program engineered for professionals targeting senior data science roles. Across 12 months and 520+ live mentor-led hours you progress from mathematical foundations to production machine learning, deep learning, big-data engineering and MLOps. The curriculum is delivered through 14 rigorous modules, 6 portfolio-grade capstone projects, weekly 1:1 mentorship, mock interviews and dedicated career and placement support, plus a premium complimentary software stack worth thousands of dollars.

What you'll be able to do

• Master Python, SQL and advanced statistics for rigorous data analysis
• Build, tune and evaluate production machine learning and deep learning models
• Engineer scalable data pipelines and feature stores with Spark and Airflow
• Deploy, monitor and govern models in production with full MLOps tooling
• Communicate insight to executives through storytelling and dashboards
• Lead end-to-end data science projects from problem framing to business impact

Tools you'll use

1. 01
   Data Science Foundations & Python3 topics

   • Python for data science
   • NumPy and Pandas
   • Environments, Git and reproducibility
2. 02
   Mathematics & Statistics for ML4 topics

   • Linear algebra
   • Calculus for optimization
   • Probability and distributions
   • Hypothesis testing
3. 03
   Data Wrangling & SQL4 topics

   • Advanced SQL
   • Data cleaning
   • ETL fundamentals
   • Feature engineering
4. 04
   Exploratory Data Analysis & Visualization3 topics

   • EDA techniques
   • Matplotlib and Seaborn
   • Dashboarding and storytelling
5. 05
   Supervised Machine Learning4 topics

   • Regression
   • Classification
   • Trees and ensembles
   • Model evaluation
6. 06
   Unsupervised Learning3 topics

   • Clustering
   • Dimensionality reduction
   • Anomaly detection
7. 07
   Deep Learning4 topics

   • Neural networks and backprop
   • CNNs
   • RNNs
   • PyTorch and TensorFlow
8. 08
   Natural Language Processing4 topics

   • Text processing
   • Embeddings
   • Transformers
   • LLM basics
9. 09
   Big Data Engineering3 topics

   • Apache Spark
   • Distributed computing
   • Data lakes and warehouses
10. 10
    Data Pipelines & Orchestration4 topics

    • Airflow
    • dbt
    • Streaming
    • Data quality
11. 11
    MLOps & Model Deployment4 topics

    • Docker
    • CI/CD for ML
    • Model serving
    • Monitoring and drift
12. 12
    Cloud for Data Science3 topics

    • AWS and Azure ML services
    • Scalable training
    • Cost optimization
13. 13
    Business Analytics & Experimentation3 topics

    • A/B testing
    • Causal inference
    • Metrics design
14. 14
    Capstone & Career Readiness4 topics

    • Capstone projects
    • Portfolio review
    • Interview prep
    • Placement support
15. Certificate

    Industry-recognized PGP in Data Science certificate, capstone portfolio review and a verifiable digital credential.

Projects you'll build

Hands-on work reviewed by your mentor. Together they become your portfolio.

1. P1Retail demand forecasting engine (time series)
2. P2Customer churn prediction and retention model
3. P3Real-time fraud detection pipeline
4. P4NLP document intelligence system
5. P5End-to-end MLOps deployment on cloud
6. P6Executive analytics dashboard with A/B test analysis

Where it can take you

Roles this program prepares you for:

Your mentor

From learners

Questions