Data analyst positions are in high demand as companies increasingly rely on data to drive business decisions. As a result, the hiring process for data analysts often includes some form of skills assessment such as a data analyst test. This helps employers evaluate a candidate’s technical abilities in areas like statistics, SQL, and data visualization. Knowing what to expect on a data analyst test can help you prepare and put your best foot forward. Here is an overview of common components on a data analyst test and tips for acing the assessment.
Technical Screening
Many companies will conduct an initial technical screening via phone or video call. This consists of questions about your education, skills, and experience working with data. Be prepared to discuss the analysis tools and programming languages you are familiar with like Excel, SQL, Python, R, and Tableau. Brush up on terminology you may encounter like descriptive vs. inferential statistics, ETL (extract, transform, load), big data, machine learning, and business intelligence. The recruiter wants to verify you have the baseline technical competency for the role before moving forward.
SQL Assessment
The ability to retrieve and manipulate data using SQL is critical for data analysis jobs. A SQL skills test is often administered online and timed. You will be presented with a database schema and asked to write SQL queries to extract certain information. Queries may involve SELECT statements, JOIN clauses, aggregations like COUNT and SUM, subqueries, and data manipulation with UPDATE, INSERT, and DELETE. Study up on SQL fundamentals and practice writing queries based on business cases. Strive for clear, efficient code that returns the requested data.
Statistics Assessment
Since statistic skills are core to data analysis, expect some statistics questions. Be prepared for both conceptual questions testing your understanding of techniques like hypothesis testing, regression, clustering, and classification as well as calculating statistics manually for a provided data set. Brush up on your knowledge of statistical significance, p-values, confidence intervals, and common distributions. Review how to calculate averages, standard deviations, correlations, and other metrics.
Data Visualization Test
Data visualization and presentation is often a major part of the role. You may be provided sample data sets and asked to create visualizations conveying key insights. The tests may assess your ability to build charts, graphs, and dashboards in tools like Tableau, Power BI, or Excel. Consider aesthetics like color and layout to create clear, impactful visuals tailored to different audiences. Comment on insights the data reveals about trends, relationships, comparisons, and performance.
Case Study Analysis
Many tests present a business scenario and data set for you to analyze. Case studies assess your ability to clean, analyze, and interpret real-world data to drive insights. Carefully review the background and objectives highlighted in the case study prompts. Ask clarifying questions if any instructions are unclear. Check the data for anomalies, duplicate records, and missing values. Document your analysis processes and assumptions in a clear manner. Build visualizations, segment the data, highlight patterns and variations. Summarize 3-5 key takeaways and provide data-backed recommendations on how to achieve the desired business outcomes.
Preparing for a Data Analyst Test
Here are some tips to prepare for and successfully pass a data analyst skills assessment.
Review Key Concepts
– Refresh your knowledge of statistics like distributions, hypothesis testing, regression, classification models
– Brush up on SQL queries including JOINs, nested queries, aggregations
– Study analytics techniques like exploratory data analysis, predictive modeling, A/B testing
– Understand types of data like time series, text, spatial, numerical, categorical
Practice Using Tools
– Work through SQL tutorials and practice questions
– Recreate visualizations and dashboards in Tableau from samples
– Use Excel for techniques like pivot tables, VLOOKUP, What-If analysis
– Write Python or R scripts to clean, analyze, and visualize sample data sets
Work Through Example Tests
– Many companies publish sample tests online. Try taking these under timed conditions.
– Ask colleagues to share examples of tests they have taken.
– Work through practice cases and questions from prep books.
Review Your Resume
– Study your own resume and be prepared to provide examples and details for every skill and experience claim.
– Quantify your impact at past jobs with data and metrics.
– Refresh yourself on analytics projects from college or coursework.
Organize Work Samples
– Gather examples that showcase your data analysis skills: code, visualizations, reports.
– Build a portfolio site if allowed or prepare a document.
– Only share samples that highlight your skills – don’t just turn in raw data.
– Be prepared to discuss your analysis process and insights.
Treat It as a Hands-On Test
– Where allowed, approach the tests hands-on as if in a real job setting.
– Create actual visualizations vs just describing what you would build.
– Write working code, not just pseudo-code. Maximize what you can demonstrate.
Talk Through Your Thought Process
– Verbalize your analytical thinking and decision making out loud to the interviewers.
– Discuss options you considered and why you took the approach you did.
– Ask clarifying questions if any aspects of the problem are unclear.
Manage Your Time
– Take note of any time limits and allocate time wisely.
– Start by quickly scanning all the test materials and requirements.
– Budget time for documenting your work, not just doing the calculations.
– Save a few minutes to review your work for typos, errors, and next steps.
What to Avoid on a Data Analyst Test
When taking a data analyst skills test, keep the following pitfalls in mind:
Don’t Rush In Without Reviewing the Materials
– Take time to carefully read and digest any background info and the test prompts.
– Clarify anything that seems ambiguous before diving in. Don’t make assumptions.
– Skipping initial steps can lead to wasted effort if you work in the wrong direction.
Avoid Sloppy Syntax and Typos
– With code and SQL queries, small errors will cause things not to run properly.
– Take care to avoid typos, missing semicolons, misspelled keywords that can break your analysis.
– Use consistent naming conventions and formatting in your work.
Don’t Overcomplicate the Analysis
– Look for the simplest way to cleanly extract the needed data and deliver insights.
– Start basic and add complexity once the core analysis is complete.
– Remember that accuracy and clarity trumps using complex methods.
Don’t Cut Corners on Documentation
– Annotate your steps, assumptions, and thought process thoroughly.
– Explain why you chose certain techniques and how they work.
– Document your work as if it will be handed off to future team members.
Don’t Forget the Business Objectives
– Always tie your analysis back to the desired business goals and outcomes.
– Make your insights and recommendations specific and actionable.
– Focus on what decisions your analysis will drive.
Following Up After a Data Analyst Test
Your work doesn’t end when you complete the assessment. Here are some things you can do following up:
Ask About Next Steps
– Inquire about when you can expect to receive feedback.
– Get clarity on the overall hiring timeline and remaining stages.
– Send a thank you note to reiterate your interest.
Review Your Performance
– Identify areas where you did well to build on for future tests.
– Note any knowledge and skill gaps you should work on.
– Obtain the interviewers’ feedback if possible.
Add Value
– Offer follow up thoughts on the case with ideas not covered given the time constraints.
– Forward relevant examples of your work not already shared.
– Ask thoughtful questions that show your understanding of their business.
Be Patient
– Remember that the hiring process often takes time, especially for technical roles.
– Use any waiting period to continue sharpening your skills.
– Follow up periodically if you don’t hear back after 1-2 weeks.
Conclusion
Data analyst assessment tests allow employers to thoroughly evaluate your technical skills in statistics, SQL, data visualization, and analytics. Come prepared for practical questions and hands-on case analysis. Refresh key concepts and practice using the actual tools. Read prompts carefully, document your work, and think aloud. Stick to simple, accurate, reproducible analysis focused on business impacts. Ask clarifying questions, manage your time, and don’t rush. Then follow up promptly and continue building your skills while you await next steps. With the right amount of prep and polish, you can ace the data analyst test and make a great impression.