Business Intelligence & Analytics Internship

Turn data into a decision somebody actually makes.

SQL, dashboards and the harder skill underneath both: working out what question is worth answering. A dashboard nobody opens is a failure regardless of how good it looks.

Beginner-friendly4 weeks · remote4 briefs · complete any 2Free to join

What you'll be able to do

Write SQL that answers a business question, not just a query
Build a dashboard someone would open twice
Define a metric precisely enough that two people compute it the same way
Present a finding to someone who does not care about your method

Who it suits

Students and professionals moving into data roles who want portfolio work with commercial framing.

What you'll work with

SQL
Power BI or Tableau
Excel
Python (pandas)
PostgreSQL

The project briefs

Complete any 2 of these 4to finish the programme. Assessment criteria are published here so you know what “good” means before you start.

SQL Analysis of a Real Dataset

~10h estimated

Answer five business questions with SQL, and defend the answers.

Load a real dataset into a real database. Write the questions before the queries. Joins, window functions and aggregations — and a sanity check on every number you produce.

Assessed against

Five business questions stated before the analysis
Window functions used where they are genuinely the right tool
Every result sanity-checked against a second method
Query performance considered on the largest table

Suggested datasets

Northwind
Olist Brazilian e-commerce
NYC Taxi trips

You'll finish with

GitHub repo with .sql files
Findings document

Dashboard Built for a Specific Person

~12h estimated

Build a dashboard for a named role, not for everyone.

Decide whose dashboard this is and what decision it supports. Every chart that does not serve that decision gets deleted. Restraint is the skill being assessed.

Assessed against

A stated audience and the decision it supports
Five or fewer views — justify anything you kept
Filters that a non-technical user can operate
A written note on what you deliberately left out

You'll finish with

Dashboard file or public link
Screenshots
Design rationale

Metric Definition and Data Quality

~8h estimated

Define metrics precisely and find where the data lies to you.

Take three metrics everyone thinks they agree on. Write the exact definition, including edge cases. Then audit the data for nulls, duplicates and impossible values.

Assessed against

Three metrics defined precisely enough to be unambiguous
Edge cases documented — refunds, cancellations, timezones
A data quality audit with counts
A note on how each quality issue would skew the metric

You'll finish with

Metric definitions document
Data quality report

Analysis Presented to a Non-Technical Audience

~8h estimated

Communicate a finding to someone who will not read your notebook.

One recommendation, the evidence behind it, and what you would do next. Lead with the answer, not the method — the method belongs in the appendix.

Assessed against

A single clear recommendation, stated first
Evidence that supports it, including anything that contradicts it
Limitations stated openly, not buried
Under 10 slides or a 5-minute video

You'll finish with

Presentation or video
Supporting analysis

Free to apply and free to complete. The completion certificate is optional.

Other domains

AI Agents & Automation

Build autonomous agents that use tools, not chatbots that answer questions.

Generative AI & LLMs

Go past prompt tinkering into retrieval, evaluation and cost control.

Machine Learning

Classical ML done properly — including the evaluation everyone skips.

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Business Intelligence & Analytics Internship

Turn data into a decision somebody actually makes.

SQL, dashboards and the harder skill underneath both: working out what question is worth answering. A dashboard nobody opens is a failure regardless of how good it looks.

Beginner-friendly4 weeks · remote4 briefs · complete any 2Free to join

What you'll be able to do

Write SQL that answers a business question, not just a query

Build a dashboard someone would open twice

Define a metric precisely enough that two people compute it the same way

Present a finding to someone who does not care about your method

Who it suits

Students and professionals moving into data roles who want portfolio work with commercial framing.

The project briefs

Complete any 2 of these 4to finish the programme. Assessment criteria are published here so you know what “good” means before you start.

SQL Analysis of a Real Dataset

~10h estimated

Answer five business questions with SQL, and defend the answers.

Load a real dataset into a real database. Write the questions before the queries. Joins, window functions and aggregations — and a sanity check on every number you produce.

Assessed against

Five business questions stated before the analysis
Window functions used where they are genuinely the right tool
Every result sanity-checked against a second method
Query performance considered on the largest table

Suggested datasets

Northwind
Olist Brazilian e-commerce
NYC Taxi trips

You'll finish with

GitHub repo with .sql files
Findings document

Dashboard Built for a Specific Person

~12h estimated

Build a dashboard for a named role, not for everyone.

Decide whose dashboard this is and what decision it supports. Every chart that does not serve that decision gets deleted. Restraint is the skill being assessed.

Assessed against

A stated audience and the decision it supports
Five or fewer views — justify anything you kept
Filters that a non-technical user can operate
A written note on what you deliberately left out

You'll finish with

Dashboard file or public link
Screenshots
Design rationale

Metric Definition and Data Quality

~8h estimated

Define metrics precisely and find where the data lies to you.

Take three metrics everyone thinks they agree on. Write the exact definition, including edge cases. Then audit the data for nulls, duplicates and impossible values.

Assessed against

Three metrics defined precisely enough to be unambiguous
Edge cases documented — refunds, cancellations, timezones
A data quality audit with counts
A note on how each quality issue would skew the metric

You'll finish with

Metric definitions document
Data quality report

Analysis Presented to a Non-Technical Audience

~8h estimated

Communicate a finding to someone who will not read your notebook.

One recommendation, the evidence behind it, and what you would do next. Lead with the answer, not the method — the method belongs in the appendix.

Assessed against

A single clear recommendation, stated first
Evidence that supports it, including anything that contradicts it
Limitations stated openly, not buried
Under 10 slides or a 5-minute video

You'll finish with

Presentation or video
Supporting analysis

Free to apply and free to complete. The completion certificate is optional.