Data Science Certification — Live Python, Machine Learning, Deep Learning and MLOps Cohort

What your organisation gets from this program

• Practitioners who ship: the program ends in MLOps — containerisation, cloud deployment and CI/CD — so teams stop stalling at the notebook stage.
• One modelling method across the team: feature engineering, evaluation and tuning taught as a single repeatable workflow.
• Governance-aware delivery: experiment tracking, model versioning and monitoring so deployed models stay auditable and can be handed over.
• Less dependence on external vendors: routine modelling, retraining and pipeline maintenance move in-house.

Who this cohort is for

Senior professionals

Analysts, engineers and reporting professionals who need the full modelling stack — Python, statistics, machine learning, deep learning and MLOps — applied to real organisational problems.

Team leads and group enrolment

Leaders standardising data science practice across an analytics or engineering team, with one shared toolchain and a predictable weekend schedule. Group enrolment is available for teams of 15+ learners.

Enterprise capability build

Executive sponsors accountable for AI and analytics outcomes who need enough internal people to take a model from notebook to a monitored production service.

Program mechanics

• Format: live instructor-led online cohort
• Duration: 16 weeks across 5 modules
• Next cohort: August 8, 2026, every weekend
• Prerequisites: basic mathematics; programming exposure helpful
• You finish with: deployed project work plus certification
• Audience: global — India, EMEA and the Americas
• Tech stack: Python, SQL, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Docker, AWS / GCP, MLflow, Git and GitHub

16-week curriculum

Module 1 — Python Programming Fundamentals (3 weeks)

Python basics, data types and structures; object-oriented programming; file handling, APIs and web scraping; error handling and debugging; version control with Git and GitHub.

Module 2 — Data Analysis and Visualization (3 weeks)

NumPy for numerical computing; Pandas for data manipulation; Matplotlib and Seaborn visualization; exploratory data analysis; statistical analysis and hypothesis testing.

Module 3 — Machine Learning Foundations (4 weeks)

Supervised learning algorithms; unsupervised learning techniques; model evaluation and selection; feature engineering strategies; hyperparameter tuning and optimization.

Module 4 — Deep Learning and Neural Networks (3 weeks)

Neural network fundamentals; TensorFlow and Keras; computer vision with CNNs; natural language processing; transfer learning and fine-tuning.

Module 5 — MLOps and Production Deployment (3 weeks)

Model versioning and experiment tracking; Docker containerization; cloud deployment on AWS and GCP; CI/CD pipelines for ML; monitoring and model maintenance.

Faculty

Taught by Rishav Das, Senior Business Intelligence Consultant, a seasoned BI professional with extensive experience in Fortune 500 companies across healthcare, finance and retail.

Live cohort vs self-paced courses

Both formats teach the same algorithms. The live cohort adds instructor-led weekend sessions, questions answered in session, production deployment with monitoring and CI/CD, a schedule that keeps the group moving over 16 weeks, and career assistance covering resume review, LinkedIn optimisation, mentoring and interview preparation. Self-paced courses typically stop at a notebook and an accuracy score.

Investment

₹55,000 INR for India and $700 USD internationally, as a one-time payment. EMI options are available and organisations can be invoiced for group enrolment of 15+ learners.

Related programs

Data Analytics 360° cohort — Excel, SQL, Power BI and Python | AI Engineering Program — LLM systems, RAG and agents | Microsoft Power BI Professional Program | Enterprise AI execution console