Data Analytics & AI Foundations. SQL, Python and AI for a first data role.

How data analyst, data scientist, data engineer and ML engineer roles differ, and where each fits.

Topics covered

• Data analyst, data scientist, data engineer and ML engineer: what each does
• How data roles work together in a team
• Reading data analyst job posts: the skills and tools they ask for
• Planning your learning path through the course

You produce: a skills map for the data role you want.

Write the SQL analysts use every day, from SELECT and JOIN to window functions and CTEs.

Topics covered

• SELECT, WHERE and ORDER BY: pulling the rows you need
• JOINs across tables, and avoiding duplicate rows
• GROUP BY and aggregates for summary reports
• Window functions and CTEs for rankings, running totals and cleaner queries

You produce: a set of SQL queries that answer real analysis questions.

Advanced Pandas for cleaning messy data and building features for analysis and models.

Topics covered

• Advanced Pandas: reshaping, pivoting and method chaining
• Handling missing values, duplicates and wrong data types
• Working with dates, text and categorical columns
• Feature engineering: creating useful variables for analysis and models

You produce: a cleaning notebook that turns a raw dataset into an analysis-ready one.

The statistics analysts use at work: distributions, hypothesis tests, A/B tests and confidence intervals.

Topics covered

• Distributions and summary statistics
• Hypothesis testing and p-values in practice
• Designing and reading an A/B test
• Confidence intervals, and how to report uncertainty

You produce: an A/B test analysis with a clear conclusion.

Model data, write DAX measures and build dashboards, then secure and publish them.

Topics covered

• Loading and shaping data, and building a data model
• DAX measures and calculated columns
• Designing dashboards people can read at a glance
• Row-level security (RLS) and publishing reports

You produce: a published Power BI dashboard.

Build visualisations in Tableau Public and learn when to choose Tableau over Power BI.

Topics covered

• Getting started with Tableau Public
• Building charts and an interactive dashboard
• Tableau or Power BI: choosing the right tool for the job

You produce: a Tableau Public dashboard.

Regression, classification and clustering with scikit-learn: when to use each and how to evaluate it.

Topics covered

• Regression for predicting numbers
• Classification for predicting categories
• Clustering for finding groups in data
• Evaluating models, and when machine learning is the right tool

You produce: a trained and evaluated model for a business question.

Use ChatGPT Advanced Data Analysis to speed up routine analysis, with prompt patterns built for analytics.

Topics covered

• Uploading and exploring data in ChatGPT Advanced Data Analysis
• Prompt patterns for cleaning, exploring and charting data
• Checking AI-generated code, numbers and charts
• Knowing when to switch back to SQL or Python

You produce: an AI-assisted analysis with your prompts and checks documented.

Turn analysis into clear recommendations that people can act on.

Topics covered

• Starting from the business question, not the data
• Telling a story with data: structure, charts and headlines
• Writing insights and recommendations people can act on
• Presenting findings and handling questions

You produce: an insights deck with recommendations.

Take a real dataset and produce a complete analytics deliverable, from cleaning to presentation.

Topics covered

• Choosing a real dataset from the options Rooman provides
• A cleaning notebook and SQL queries
• A Power BI dashboard and an insights deck
• Recording a presentation video of your findings

You produce: your capstone analytics deliverable.