Test and Authenticate Your Data Analytics Skills

The exact format depends on the challenge, but many problems require you to work through several stages of the analytical process rather than complete one isolated task.

You may begin by understanding the business objective and reviewing the available data. From there, you might need to clean and organise the dataset, define the metrics that matter, analyse different segments or time periods and determine which patterns deserve attention.

The final stage may involve presenting your conclusion through a short analytical summary, visualisation, dashboard or another defined output.
The current page already covers challenge formats across SQL, Python, exploratory analysis, business metrics, visualisation, statistics and real-world business scenarios.

• Customer Behaviour Challenges

  Analyse customer activity, purchases or engagement to understand repeat behaviour, retention, churn or differences between customer groups.

• Sales Analytics Challenges

  Investigate revenue, product performance, locations or customer segments to identify trends and performance drivers.

• Marketing Analytics Challenges

  Compare campaigns, channels and audiences using metrics such as conversion, acquisition, engagement or revenue contribution.

• Product Analytics Challenges

  Work with behavioural or event data to understand feature adoption, customer journeys, funnels and drop-off points.

• Data Cleaning Challenges

  Prepare imperfect datasets by resolving missing information, duplicates, inconsistent values and formatting problems before analysis begins.

• Business Metrics Challenges

  Determine which metrics should be calculated and how they should be interpreted within a specific business situation.

• Visualisation and Dashboard Challenges

  Turn analytical findings into charts or dashboards that make the most important patterns easy to understand.

• Operations Analytics Challenges

  Analyse process, delivery, productivity or performance data to identify delays, bottlenecks and areas of inconsistent performance.

You do not need to be an SQL expert before getting started. The appropriate starting point depends on your current knowledge and what you want to demonstrate.

• Apply Different Skills Together

  A practical analytics problem may require data cleaning, querying, metric selection, visualisation and interpretation within the same workflow. Instead of practising these skills separately, you learn how they work together.

• Strengthen Analytical Thinking

  Challenges require you to decide what needs to be investigated rather than simply applying a technique because an exercise tells you to use it.

• Work With Unfamiliar Problems

  You may encounter datasets, business questions or situations you have not previously worked with. This helps build confidence beyond familiar examples.

• Identify Skill Gaps

  A challenge can reveal whether you need more practice with SQL, Python, statistics, data cleaning, visualisation, business metrics or communication.

• Benchmark Your Ability

  Where scoring, evaluation or rankings are available, you can see how effectively you approached the problem and where you can improve.

• Build Practical Evidence

  Completing structured analytical problems helps demonstrate that you can use data skills to reach an outcome rather than simply list analytics tools as capabilities.

Data analytics challenges can support different levels of experience. What matters is choosing a challenge that matches the capabilities you currently have.

• Students and Beginners

  Start with problems involving smaller datasets, straightforward cleaning, introductory SQL, spreadsheet analysis or basic business metrics.

• Aspiring Data Analysts

  Use challenges to bring together skills learned separately and gain experience solving complete analytical problems.

• Working Data Analysts

  Work with unfamiliar datasets and business situations to strengthen areas outside your everyday responsibilities.

• BI and Reporting Professionals

  Practise metric selection, data preparation and interpretation before information reaches a dashboard or report.

• Career Switchers

  Apply skills developed through courses or independent learning to structured problems that require independent decisions.

• Data Scientists and Other Data Professionals

  Strengthen exploratory analysis, data understanding and business interpretation alongside more technical work.

The value of participating is not limited to completing another exercise. A well-designed challenge can help you understand how effectively you use your skills when the analytical path is not already provided.

• Practical Analytics Experience

  Work through complete problems that can require several analytical skills rather than practising one function at a time.

• Stronger Analytical Judgement

  Learn to decide what should be measured, compared or investigated before choosing a technique.

• Better Problem Framing

  Practise turning a broad business question into smaller analytical steps that can actually be answered with data.

• Skill-Gap Discovery

  Identify whether you need additional practice in areas such as SQL, Python, data cleaning, statistics, metric selection, visualisation or communication.

• Performance Benchmarking

  Where a challenge includes defined scoring or rankings, use the results to better understand your current performance.

• Evidence of Applied Skills

  Show that you can apply data analytics knowledge to a defined problem rather than only listing individual tools as skills.

• Certificates, Rankings and Rewards

  Eligible CompeteX challenges may include certificates, leaderboard rankings, cash rewards or other forms of recognition depending on the individual challenge.