AdvancedCoreDataScience
A rigorous 3-month, 144-hour journey from statistical reasoning to deep learning, MLOps and industry-grade capstones. Built for learners who already speak Python & Pandas.
From statistical thinking to production ML
Designed for learners with a working grip on Python, NumPy, Pandas and basic ML. Over 12 weeks — 2 hours a day, 6 days a week — you advance through statistics, feature engineering, advanced algorithms, time series, unsupervised learning, SQL/Spark, deep learning, MLOps and finally end-to-end industry capstones with interview prep.
Each week is anchored to one core module. Days 1–5 cover conceptual and applied topics; Day 6 is a hands-on lab or project that consolidates the week.
Python for Data Science & Clean Code
- Python refresher · OOP basics
- File handling (CSV, JSON, text)
- Jupyter / Colab + virtual environments
- PEP8, docstrings & clean-code practices
Your journey, phase by phase
12 weeks · 10 modules · a structured path from statistical thinking to production ML.
Python for Data Science & Clean Code
- Python refresher · OOP basics
- File handling (CSV, JSON, text)
- Jupyter / Colab + virtual environments
- PEP8, docstrings & clean-code practices
Data Manipulation — NumPy, Pandas & Polars
- NumPy arrays & vectorized ops
- Pandas: merge, group, reshape
- Memory optimisation for large data
- Polars for out-of-core, lazy processing
Statistics for Data Science (Core)
- Descriptive stats & distributions
- Hypothesis testing · p-values · CIs
- Correlation & regression fundamentals
- A/B testing statistical foundations
SQL for Data Analytics
- Joins, aggregations, subqueries
- SQL ↔ Python / Pandas workflows
- Business query patterns
- SQLite & PostgreSQL locally
Data Cleaning, Wrangling & Feature Engineering
- Missing values & outliers
- Encoding categorical features
- Scaling & engineering pipelines
- Feature Stores with Feast
EDA & Interactive Visualization Dashboards
- Exploratory analysis workflow
- Matplotlib · Seaborn · Plotly
- Streamlit dashboards (local)
- Tableau for business stakeholders
Data Engineering Foundations & Pipeline Orchestration
- ETL: CSV → SQLite / Parquet
- PySpark (local mode) for larger-than-memory data
- Spark DataFrames · transformations · actions
- Apache Airflow DAGs & scheduling
Time Series Forecasting & Enterprise A/B Testing
- Decomposition & stationarity
- ARIMA & Prophet forecasting
- A/B experiment design
- Statistical impact analysis for business
Big Data Concepts & MLOps Foundations
- Big data concepts overview
- Model deployment with FastAPI
- Containerisation with Docker
- Experiment tracking · MLflow · DVC
Capstone Projects & Live Portfolio Packaging
- End-to-end production-style capstone
- Healthcare / Finance / Retail / HR / Marketing
- Local packaging & demo
- Hiring-partner presentation · portfolio & resume
Master the industry-standard toolkit
From notebooks to production — the same stack used at Netflix, Uber, Airbnb & Zoho.
Ship a production-grade project
Pick one, design it, build it, present it on Demo Day.
Where this programme takes you
Every role below maps to specific modules — the programme is designed so you can specialise as you graduate.
Career Outcomes
Build production-style ML apps run locally with FastAPI, Docker, MLflow & DVC
Engineer features, pipelines & feature stores for tabular ML
Forecast time series and design enterprise A/B tests
Ship interactive dashboards with Streamlit & Tableau
Orchestrate ETL pipelines with PySpark & Apache Airflow
Package a portfolio-ready capstone across Healthcare, Finance, Retail, HR or Marketing
Reserve your seat
Complete the application below — our admissions team will reach out within 24 hours.