Five tracks. One runway.

Bootcamps

Showing 4 bootcamps

• Data Science — Foundation

  Foundation

  4 months, 5–8 hrs/week

  20 USDper month

  No coding or statistics background needed. You'll learn Python from the very first line, the tools data scientists work in every day, and how to load a real dataset and start asking it questions.

  • Python
  • Jupyter
  • Git
  • Pandas

  1. Phase 1

     Python from zero

     Variables, loops, functions, lists and dictionaries.

  2. Phase 2

     A data scientist's tools

     Notebooks, the terminal, Git and GitHub, and fixing your own errors.

  3. Phase 3

     First look at data

     Loading a CSV with Pandas, then counting, averaging and charting it.

  4. Phase 4

     Capstone

     Answer real questions about a dataset in a notebook you can share.

  Apply now for Data Science (Foundation)

• Data Science — Core

  Core

  5 months, 5–8 hrs/week

  25 USDper month

  You can write basic Python. Now do the daily work of a data scientist: clean messy data with Pandas, pull it from databases with SQL, and use probability, statistics and charts to explain what's really going on.

  • NumPy
  • Pandas
  • SQL
  • Statistics
  • Matplotlib

  1. Phase 1

     NumPy & Pandas

     Fast maths on arrays, then cleaning, joining and reshaping messy data.

  2. Phase 2

     SQL & databases

     Queries, joins, grouping and window functions on a real database.

  3. Phase 3

     Statistics & visualization

     Probability, distributions and sampling, with charts in Matplotlib and Seaborn.

  4. Phase 4

     Capstone

     A full analysis of a real dataset, with written findings you can defend.

  Apply now for Data Science (Core)

• Data Science — Intermediate

  Intermediate

  6 months, 5–8 hrs/week

  32 USDper month

  You can clean and analyse data. Now learn what separates a data scientist from an analyst: running experiments, building predictive models, and proving they'll hold up on data they haven't seen.

  • A/B testing
  • Scikit-learn
  • Feature engineering
  • Model evaluation

  1. Phase 1

     Statistics & experiments

     Hypothesis tests, confidence intervals and A/B tests, without the proofs.

  2. Phase 2

     Machine learning

     Regression, classification and trees, plus clustering and PCA, in Scikit-learn.

  3. Phase 3

     Models you can trust

     Feature engineering, cross-validation, the right metrics, and avoiding leakage.

  4. Phase 4

     Capstone

     A predictive model for a business problem, presented to a non-technical audience.

  Apply now for Data Science (Intermediate)

• Data Science — Advanced

  Advanced

  8 months, 5–8 hrs/week

  40 USDper month

  You can build and evaluate a model. Now take on the harder problems of the job: forecasting, text data and LLMs, data too big for one laptop, models running in production, and the SQL rounds and case studies interviews ask for.

  • Forecasting
  • Gradient boosting
  • BigQuery
  • Model deployment

  1. Phase 1

     Advanced modelling

     Gradient boosting, time-series forecasting, and text with embeddings and LLMs.

  2. Phase 2

     Data at scale

     A cloud data warehouse, scheduled pipelines, and a model served behind an API.

  3. Phase 3

     Case studies & interviews

     Business cases, SQL and statistics rounds, and take-home projects.

  4. Phase 4

     Capstone

     An end-to-end project on real data: pipeline, model, and a readout for leaders.

  Apply now for Data Science (Advanced)