Prove you can turn data into decisions.
Data analyst roles grew 40% in the UK between 2022 and 2024 — yet 82% of companies say they can't find candidates with the right skills. Our free assessment measures your data literacy, statistical reasoning, and insight communication, and produces a report employers can act on.
What the Data Analytics domain tests.
10 scenario-based questions covering every skill a junior data analyst uses in their first week — from reading charts to writing SQL logic to communicating a finding to a non-technical stakeholder.
Chart & Graph Reading
Interpret bar charts, line graphs, scatter plots, and heatmaps — extract the key insight quickly and accurately.
Descriptive Statistics
Work with mean, median, mode, range, and outliers — understand what each tells you and when each misleads.
Data Quality & Errors
Spot the incorrect row in a dataset, identify missing values, and evaluate the impact of data quality issues.
Spreadsheet Reasoning
Trace the logic of IF functions, VLOOKUP, and SUMIF formulas — predict what output they produce.
SQL Fundamentals
Read and interpret SELECT queries with WHERE, GROUP BY, and JOIN — determine what data a query returns.
Data Ethics & Privacy
Apply GDPR principles, identify consent and anonymisation issues, and spot datasets that create unfair bias.
Visualisation Design
Choose the right chart type for a given dataset and communication goal — and explain why others would mislead.
Statistical Inference
Distinguish correlation from causation, identify confounding variables, and evaluate conclusions from a study.
Business Insight from Data
Given a dataset summary, identify the key business finding and translate it into a recommendation.
Data Storytelling
Structure a data narrative — choose what to lead with, what to cut, and what visualisation best tells the story.
Plus 24 questions across General Aptitude (verbal + numerical reasoning), Workplace Skills (situational judgement), and an Interest Profile to confirm your track fit. See the full assessment breakdown →
Where a Data Analytics internship leads.
Data literacy is the most transferable skill in the modern economy. These are the roles our top Data Analytics track candidates pursue — across sectors, not just tech.
Junior Data Analyst
SQL, Excel, Python basics, data cleaning, dashboard creation, stakeholder reporting
Business Intelligence Analyst
Power BI, Tableau, data modelling, KPI tracking, executive dashboards
Market Research Analyst
Survey design, data collection, segmentation, competitive benchmarking
Reporting Analyst
Automated reporting, Excel/Google Sheets, data reconciliation, SLA tracking
Data Quality Analyst
Data validation, anomaly detection, master data management, documentation
Graduate Data Scientist (pathway)
Statistical modelling, Python/R, ML pipelines, experimentation design
Who the Data Analytics track is for.
You don't need to be a mathematician — you need to be someone who asks "what does this data actually tell us?" If that's your natural instinct, this track is your competitive advantage.
The numbers-curious student
You naturally look for patterns in information. You've used Excel beyond the basics, you notice when a statistic in an article doesn't add up, and you find yourself asking "how do they know that?" This assessment validates that instinct with a verified score.
The Maths or Science student
You're strong in Maths, Statistics, Physics, or Biology and you're looking for a career path that applies those skills in a business context. Data analytics is that bridge — and employers are actively looking for analytical students with real-world exposure.
The spreadsheet power user
You've used Google Sheets or Excel seriously — building models, tracking data, creating charts for a project or enterprise. You have practical data instincts that the assessment is specifically designed to reward.
All 34 questions, broken down.
The adaptive engine learns your level as you progress, so every question is calibrated to push you accurately — not just recycle easy items.
| Phase | Questions | What we measure |
|---|---|---|
| General Aptitude | 10 | Verbal reasoning, numerical reasoning, logical inference |
| Data Analytics Domain | 10 | Charts, statistics, SQL, visualisation, data ethics |
| Workplace Skills | 8 | Situational judgement, stakeholder communication, accuracy under pressure |
| Interest Profile | 6 | Track alignment, preferred data tools, working style |
Frequently asked questions.
Do I need to know Python or SQL to take the Data Analytics track?+
What companies hire student data interns in the UK?+
How is data analytics different from just using Excel?+
Will the readiness report help me get into a Data Science or Statistics degree?+
What is the difference between the Data Analytics and Technology tracks?+
Further reading
Internships at Early Age: Development & Career Benefits
The developmental and career case for professional experience at 14–16, not 17–18. Neuroscience, university admissions data, and labour market research show early internship experience produces measurably better outcomes — and the gap widens over time.
High School Internship Benefits
The evidence-based case for high school internships — how structured work experience at 14–18 builds self-efficacy, resilience, and professional identity, and measurably improves university application outcomes.
Your data internship starts here.
Free 34-question assessment. AI readiness report. Real placement opportunities. No CV required to start.
Apply free — Data Analytics track →Free for all students aged 14–18 · Takes 35 minutes · Instant results