Data Analytics Bootcamp · Cohort 1

Excel, SQL, Power BI and Python — the full analyst toolkit

Four tools. Four portfolio projects. One capstone.

Most analytics courses teach one tool and leave you to work out the rest. Job descriptions ask for four. This covers all four, in the order you would actually learn them, and you finish each one with something in a portfolio rather than a certificate.

Starts: Saturday 24 October 2026
Sessions: Saturdays 10:00–14:00 · Sundays 14:00–17:00 WAT
Length: 14 weekends · 98 hours
Ends: Sunday 7 February 2027

Live online · 40 seats · Two-week break 21 December – 8 January

Apply for a seat

Four tools, four projects, one capstone — and a two-week break over Christmas, because a session nobody attends helps nobody.

No prior experience required. You need a laptop, a stable internet connection, and the time to turn up for both weekend sessions.

Fourteen weekends

Saturdays are four hours, Sundays three. Every block ends with something you keep.

Excel & Power Query

Weeks 1–3

Week 1

Power Query for cleaning and transformation

Power Query editor and why it replaces manual cleaning
Connecting to workbooks, CSVs and whole folders
Removing duplicates, replacing values, filling down
Data type conversion and basic transformations

Week 2

Advanced transformation and pivot tables

Text transformations, splitting columns by delimiter
Merging tables and the four join types
Appending, pivoting and unpivoting
Pivot tables, grouping, filtering and calculated fields

Week 3

Power Pivot, DAX and dashboard design

The data model: relationships and cardinality
DAX basics — calculated columns and measures
Dashboard layout, visual hierarchy and colour
Slicers, sparklines and interactive filtering

Project. An interactive sales dashboard, built from raw files

SQL

Weeks 4–7

Week 4

Foundations

Databases and RDBMS concepts
SELECT, WHERE and comparison operators
ORDER BY, LIMIT and combining clauses
Aggregates, GROUP BY and HAVING

Week 5

Intermediate SQL

String functions, SUBSTRING, POSITION, CONCAT
Type casting and handling NULLs with COALESCE
DDL — CREATE, ALTER, DROP and constraints
Subqueries: single-row, multi-row and correlated

Week 6

Views, CTEs, DML and advanced joins

Views: creating, querying and updating
Common table expressions, single and multiple
INSERT, UPDATE, DELETE and TRUNCATE
Self joins, cross joins, full outer joins, set operators

Week 7

Window functions and stored procedures

OVER(), PARTITION BY and ordering within windows
ROW_NUMBER, RANK, DENSE_RANK and NTILE
LEAD, LAG, FIRST_VALUE and LAST_VALUE
Stored procedures, parameters and functions

Project. An end-to-end SQL analysis and reporting exercise

Power BI

Weeks 8–10

Week 8

Foundations and data preparation

The Power BI ecosystem: Desktop, Service, Mobile
Connecting to files, folders and databases
Power Query Editor inside Power BI
Cleaning, profiling and handling anomalies

Week 9

Data modelling and DAX

Star schema versus snowflake, and why it matters
Relationships, cardinality and filter direction
Date tables, hierarchies and modelling best practice
Row context versus filter context; measures and time intelligence

Week 10

Advanced DAX, visualisation and publishing

CALCULATE, FILTER, ALL and the iterator functions
Drill-through, bookmarks, custom tooltips and themes
Publishing, workspaces, apps and row-level security
Gateways, scheduled refresh and version control

Project. An executive Power BI dashboard, published and secured

Python

Weeks 11–14

Week 11

Statistics foundations and Python basics

Population and sample, statistics and parameters
Variable types and levels of measurement
Central tendency versus variability
Environment setup, Jupyter, variables and operations

Week 12

Data structures and control flow

Lists, tuples, dictionaries, indexing and slicing
Conditionals, loops and string methods
Functions, lambda expressions, map and filter
Reading and writing files, including CSV

Week 13

NumPy and pandas

Arrays, randomisation, indexing and slicing
Series and DataFrames; loc and iloc
Filtering, sorting and handling missing data
Merging, groupby, pivot tables and time series

Week 14

Visualisation, statistical analysis and capstone

Matplotlib, Seaborn and Plotly
Descriptive statistics and exploratory data analysis
Hypothesis testing, t-tests, z-scores and ANOVA
Data storytelling and reporting to stakeholders

Project. Final capstone: end-to-end analysis, presented to stakeholders

What you leave with

Four tools

Excel and Power Query, SQL, Power BI and Python — the stack most analyst roles actually ask for.

Four portfolio projects

An Excel dashboard, a SQL reporting exercise, a published Power BI dashboard and a Python analysis.

A capstone

One end-to-end analysis, presented to stakeholders the way you would at work.

Recordings and community

Every session recorded, plus the cohort group for the whole programme.

Certificate of completion

Issued on finishing the capstone.

Who this is for

Career switchers: Moving into data from another field entirely
Graduates: Building the portfolio a first analyst role asks for
Business & operations staff: Already work with data, want to do it properly
Finance & accounting: Beyond spreadsheets into modelling and BI
Product & marketing: Answering your own questions without waiting on a queue
Anyone stuck in manual reporting: Automate the report you rebuild every month

Want to go further afterwards?

This programme takes you to competent analyst. If you then want to build AI systems on top of those skills, the AI Engineering Bootcamp is the next step — and the Python you learn here is the prerequisite for it.

See all programmes

Apply

Choose a tier and tell us where you are starting from. You will get a reply within two working days to arrange a short conversation. Nothing is owed until then.