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AI & Data

Python for Data Analysis

Use AI like a senior engineer, not a tourist.

Duration
12 weeks
Format
2 live sessions + 1 lab per week
Track type
Cohort
Open seats
6 across 1 cohort
Apply for this course →See cohort calendar

About this course

Why students take this

The data-analyst track for Python beginners. Twelve weeks taking you from print("hello") to confidently exploring real datasets, building dashboards, and presenting findings.

We use real, messy datasets — sales data, web logs, survey results — and build end-to-end notebooks in Jupyter. The final project is your own dataset, your own dashboard, and a presentation in front of the cohort.

Twelve weeks of Python + data

  • Python fundamentals + Jupyter workflow
  • Pandas: loading, cleaning, joining, aggregating
  • Visualization: matplotlib, seaborn, plotly
  • Statistics for analysts: distributions, correlation, A/B tests
  • Stretch: scikit-learn classification + regression basics

Outcomes

What you’ll walk away with

Every outcome below is something a graduate can demonstrate on the last day of class — not a vague promise.

  • 01

    Build production-ready AI-powered features

  • 02

    Write prompts, evals, and guardrails like a senior

  • 03

    Ship a portfolio AI project that demonstrates real skill

Syllabus

How the 12 weeks actually unfold

We move in deliberate phases. Each one builds on the last, so by week 12you’re working on real briefs — not still typing your first hello-world.

  1. W1

    Python basics

    Variables, lists, functions, control flow.

    You can…
    • Read + write Python with confidence
    • Use Jupyter notebooks effectively
  2. W2

    Working with data

    Reading CSV, JSON, SQL with pandas.

    You can…
    • Load any dataset into pandas
    • Clean messy data
  3. W3

    Data cleaning

    Missing values, duplicates, type coercion.

    You can…
    • Handle missing data correctly
    • Deduplicate dataframes
  4. W4

    Aggregation + grouping

    GroupBy, pivot tables, multi-index.

    You can…
    • Answer questions with groupby
    • Use pivot tables in pandas
  5. W5

    Joining datasets

    Merge, concat, set operations.

    You can…
    • Join multiple datasets cleanly
    • Debug merge issues
  6. W6

    Visualisation

    matplotlib, seaborn, plotly.

    You can…
    • Build charts that communicate insight
    • Use seaborn for statistical plots
  7. W7

    Statistics for analysts

    Distributions, correlation, hypothesis testing.

    You can…
    • Test hypotheses with the right method
    • Report with confidence intervals
  8. W8

    A/B testing basics

    Sample size, p-values, lift.

    You can…
    • Design + analyse an A/B test
    • Avoid common A/B mistakes
  9. W9

    Time series

    Trends, seasonality, forecasting basics.

    You can…
    • Analyse time series data
    • Produce simple forecasts
  10. W10

    Dashboards

    Streamlit + plotly Dash for interactive UIs.

    You can…
    • Build an interactive dashboard
    • Deploy it to a URL
  11. W11

    ML — supervised basics

    Classification, regression with scikit-learn.

    You can…
    • Train a basic classifier
    • Evaluate model performance
  12. W12

    Capstone project

    Bring-your-own data, ship a notebook + dashboard.

    You can…
    • Deliver a portfolio analytics project
    • Present findings to a non-technical audience

Who this is for

A great fit if you’re…

  • Engineers wanting to add AI to their stack
  • Founders building AI-first products
  • Analysts moving from spreadsheets to Python

Tools you’ll use

The stack you’ll get fluent in

  • OpenAI, Anthropic, Google APIs
  • Python, Jupyter, Pandas, scikit-learn
  • Vector databases, RAG pipelines

Format: 2 live sessions + 1 lab per week. Heavy hands-on coding.

FAQ

Questions students ask before joining

Do I need to know Python?+

Helpful but not required for the intro courses. The Data and LLM courses assume Python basics.

Do I need a GPU?+

No — we use cloud APIs. Your laptop can be modest.

Will I use my own data?+

You can — the LLM and Data courses both encourage bringing your own dataset for the final project.

Are these tools free to use?+

Most have free tiers. We provide API credits during cohort for paid features.

Also in AI & Data

Other courses in this track

AI for Everyone — ChatGPT, Claude, Gemini

2 weeks

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Build with LLMs (OpenAI/Anthropic API)

8 weeks

Course details →

Prompt Engineering for SMEs

2 weeks

Course details →

Next step

4-minute application. Quote in 24 hours.

Tell us about your background and goals. We’ll send the syllabus, fee plan, and a cohort that fits.

Apply for Python for Data Analysis →