Genai Applied
Sign in Join the waitlist

Career track · Data Scientist

Become a data scientist for the AI era: modelling and experiments, with AI as your research assistant.

Foundation models changed the toolkit but not the core of the job: framing problems, running honest experiments and proving a model works. Learn both the classics and the new stack.

Or get your personal AI playbook now

What changes

Your work, re-mapped.

How AI is changing this work today, task by task. Your assessment maps it onto your own week.

Tasks AI can already do for you.

  • Boilerplate preprocessing and feature code
  • Routine hyperparameter searches
  • First drafts of model documentation
  • Literature and library lookups

The curriculum

Ten modules.
One path through them.

Your assessment orders these for your level and goals and skips what you already know. Open any module to see what it covers.

01Data science today5 topics

How foundation models changed data science, and what stayed the same.

  1. The modern DS workflow
  2. Classical ML vs LLMs: when to use which
  3. Roles: DS, ML engineer, AI engineer
  4. Using AI coding assistants well
  5. Building a learning plan
02Statistics and probability5 topics

The foundation that separates scientists from tool users.

  1. Distributions and estimation
  2. Hypothesis testing
  3. Bayesian thinking
  4. Bootstrapping and resampling
  5. Common statistical traps
03Classical ML for tabular data5 topics

Models that still win on structured business data.

  1. Linear and logistic regression
  2. Tree ensembles: XGBoost and LightGBM
  3. Feature engineering
  4. Tabular foundation models such as TabPFN
  5. Interpretability
04Evaluation done right5 topics

Prove your model works on data it has never seen.

  1. Train, validation and test splits
  2. Leakage and how to catch it
  3. Metrics for imbalanced problems
  4. Calibration
  5. Drift and monitoring
05Experimentation and causal inference5 topics

Measure what actually caused the change.

  1. A/B test design
  2. Power and sample size
  3. Difference-in-differences
  4. Propensity and uplift modelling
  5. Communicating causal results
06Deep learning foundations5 topics

Understand neural networks enough to use and adapt them.

  1. Neural network basics
  2. Training loops in PyTorch
  3. Embeddings
  4. Transformers at a high level
  5. Transfer learning
07LLMs for data scientists5 topics

Use language models for text, extraction and analysis.

  1. Prompting for structured output
  2. Classification and extraction with LLMs
  3. Embeddings and semantic search
  4. Retrieval-augmented generation
  5. Cost and latency trade-offs
08Fine-tuning and LLM evaluation5 topics

Adapt open models and measure them honestly.

  1. Prompting vs RAG vs fine-tuning
  2. LoRA and parameter-efficient fine-tuning
  3. Building an eval set
  4. Faithfulness and groundedness metrics
  5. LLM-as-judge and its biases
09From notebook to production5 topics

Hand over models that work outside your laptop.

  1. Reproducible pipelines
  2. Experiment tracking with MLflow
  3. Packaging and serving basics
  4. Monitoring after launch
  5. Working with ML engineers
10Portfolio and career5 topics

Show the judgment employers hire for.

  1. End-to-end portfolio projects
  2. Writing up experiments
  3. DS interview preparation
  4. Case and take-home rounds
  5. A 60-day plan

Outcomes

By the end, you can.

  • 01Build and honestly evaluate models on tabular data
  • 02Design experiments and causal analyses that hold up
  • 03Apply LLMs and fine-tuning where they beat classical methods
  • 04Ship a reproducible model with tracking and monitoring

Tools and techniques you'll use

  • Python
  • scikit-learn
  • XGBoost and LightGBM
  • Polars and DuckDB
  • PyTorch
  • Hugging Face
  • TabPFN
  • MLflow

Who it's forAnalysts moving into modelling, early-career data scientists and engineers moving into ML.

The course is opening soon

Be first in.

Join the waitlist for the Data Scientist course. One email when it opens, nothing else.

Questions

Good to know.

Do data scientists still need classical machine learning?

Yes. Gradient-boosted trees and solid statistics still win on most structured business data, and evaluation skills matter more as LLMs spread. The course teaches both the classics and the LLM stack.

How much maths do I need?

Comfort with basic algebra is enough to start. We teach the statistics and probability you actually use, with intuition first and formulas second.

When does the course open?

The full course is being built now and opens to the waitlist first. Join with your email and we'll tell you as soon as it's ready.

How is the course personalised?

Before the course, a five-minute assessment, by text or by voice with Iris, our AI guide, learns your role, daily work, skills and goals. Your personal playbook then orders these modules for your level and goals and skips what you already know.

What can I do while I wait?

Take the assessment now. You get your personal AI playbook straight away, with the tasks in your work AI can take over, the ones it speeds up, and your learning path.