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DATA SCIENCE & AI PROGRAMME

Go Beyond Notebooks — Ship a Deployed, Monitored Model

A nineteen-week path from statistics and Python through classical machine learning, deep learning, generative AI and MLOps — ending with a live, monitored model, not a trained one sitting in a notebook.

19 WEEKS
95 SESSIONS
MON-FRI · 1.5 HRS/DAY
OUTCOMES

What you'll be able to do

PythonMachine LearningDeep LearningRAGMLOpsPower BI
THE PATH

What you'll cover, week by week

01

Python & StatisticsWeeks 1-3

Advanced pandas/NumPy, probability and hypothesis testing.

02

SQL for Data ScienceWeek 4

Analytical SQL for sourcing and joining features.

03

Machine Learning EssentialsWeeks 5-7

Regression, classification, ensembles and evaluation.

04

Feature EngineeringWeek 8

Feature engineering, tuning, SHAP explainability.

05

Deep LearningWeeks 9-11

Neural networks, CNNs, RNNs, TensorFlow and PyTorch.

06

NLP & Generative AIWeeks 12-14

Embeddings, vector databases, LangChain, RAG.

07

MLOps & DeploymentWeeks 15-16

FastAPI, Docker, MLflow, production monitoring.

08

AI-Assisted CommunicationWeek 17

AI copilots, Power BI dashboards, stakeholder storytelling.

09

Capstone ProjectWeeks 18-19

One dataset, start to finish — a deployed, monitored model.

Aravind Reddy Dudipala
Taught personally by Aravind Reddy Dudipala

Senior Data Scientist & AI/ML Programme Leader, 11 years of experience. Instructor at Berkeley Haas and Imperial College Business School. Every batch — up to 50 learners — taught live, session by session.

CAPSTONE

Choose a project in your own domain

Every learner finishes with a deployed, portfolio-ready project — not a certificate for finishing videos.

TRACK 01E-commerce dynamic pricing
TRACK 02Short-term rental price optimisation
TRACK 03Content engagement prediction
TRACK 04IT service ticket classification
TRACK 05Customer feedback & sentiment mining
TRACK 06RAG-based domain chatbot
FREE DOWNLOAD

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