With the help of our resident career advisor Danielle Sander, we\u2019ve produced a list of frequently asked interview questions and tips on how to answer them. We can\u2019t guarantee you\u2019ll bag the job, but we can certainly give you the confidence to walk out of the interview room knowing you\u2019ve given it your best. Let\u2019s start the interview!<\/p>\n
If you’d like to see if data analytics is for you, try this free data short course<\/strong><\/a>.<\/p>\n These questions are designed to ease you into the interview, and will focus on broad topics so the interviewer can get to know more about you.<\/p>\n Danielle says: When an interviewer asks this, what they\u2019re essentially saying is: \u2018Can you walk me through your career history, giving one takeaway from each of your experiences in work and education?\u2019.<\/p>\n It\u2019s important to bear this in mind when fielding this question, and structure your answer accordingly, so that you share the right kind of information and leave out the bits that aren\u2019t important.<\/p>\n Danielle says: This is your chance to impress them with your passion and drive to work in data analytics.<\/p>\n You need to press home your love of data, and explain the reasons why you\u2019re pursuing analytics as a career. Lead the interviewer through your journey to becoming a data analyst and your approach to data analysis.<\/p>\n Demonstrate your awareness as to how and why having a solid understanding of the industry you\u2019re looking to work in enhances your ability to carry out effective analysis.<\/p>\n Outline your strengths and where they lie. Are you great at collaborating with teams? Are you a natural at programming languages?<\/p>\n Do you love giving presentations on your findings? Explain what tools you\u2019re familiar with, such as Excel, and what programming languages you know.<\/p>\n Danielle says: It\u2019s essential you demonstrate your knowledge of the business and product, because that\u2019s a key part of being a data analyst.<\/p>\n The art of analytics lies in your ability to ask great questions, and you\u2019ll only be able to ask such questions with sufficient background knowledge in the field. So demonstrate to the interviewer that you\u2019ve done your research, and how your own analytical skills relate to the field.<\/p>\n Perhaps you\u2019ve already worked in the area before in a different capacity; show them how your previous experience relates to your new set of skills!<\/p>\n Make sure you spend time considering your past experience, so that you\u2019re able to immediately bring up examples when needed.<\/p>\n Danielle says: It\u2019s best to use the STAR method when asked a question such as this:<\/p>\n Outline the circumstances surrounding a previous data analysis project, describe what you had to do, how you did it, and the outcome of your work.<\/p>\n Don\u2019t worry about being fairly rigid in your approach to this answer\u2014just make sure the interviewer has everything they need to know by the end.<\/p>\n Danielle says: The most important thing when answering questions regarding a mistake or a weakness, is to acknowledge ownership over what\u2019s happened. Mistakes aren\u2019t important to the interviewer, but your transparency and how you found a solution is. Outline the learning process and how that\u2019s enabled you to work more effectively.<\/p>\n Danielle says: Provide some context for what you\u2019re about to say. Explain the project and the goal of it, going into some detail about your own role in the process. Then explain what aspect of it you found the most difficult. Your solution to overcoming this difficulty is what the interviewer\u2019s looking for.<\/p>\n These questions will touch upon more technical aspects of the role of data analyst.<\/p>\n Be prepared to bring up more working examples from your previous roles, and make sure you\u2019ve prepared an answer for what aspects of the role appeal to you. Don\u2019t worry though, these questions aren\u2019t going to dive too deep into your expertise\u2014so don\u2019t worry about being put on the spot!<\/p>\n Danielle says: For this question, It\u2019s important you detail your (hopefully excellent!) Excel skills, which are an integral part of performing data analysis.<\/p>\n Prove your Excel credentials, outlining any courses you\u2019ve been on or examples of analysis you\u2019ve performed with the program. Employers will also want to know what querying languages you\u2019re familiar with, whether it be SAS, R, Python or another language. Querying languages are used for larger sets of data, so you\u2019ll need to prove you have a solid foundation in one of these languages.<\/p>\n Here\u2019s a top tip: try and find out what querying language the company you\u2019re applying to uses, that might come in handy!<\/p>\n Danielle says: In data analytics, staying on top of developments in the field usually involves keeping your knowledge of existing libraries and frameworks up to date.<\/p>\n So make sure you\u2019re able to bring up some names of libraries when asked. The Kaggle Community is an online resource for data scientists and analysts that contains a huge amount of information on the subject, so why not join the community and expand your knowledge. Name dropping such resources in an interview can sometimes help demonstrate your passion for data analytics!<\/p>\n Danielle says: You\u2019re generally going to be referring to data cleansing checks when answering this question about data analytics.<\/p>\n By undertaking such checks, you\u2019re able to ensure results are reliable and accurate. Explaining to your interviewer that an awareness for the kind of results that would be implausible is also a good thing to do. The interviewer might give you a small logic problem and ask you to explain how you\u2019d overcome it. Explaining what you\u2019d do and the necessary investigations you\u2019d undertake if something looks odd will tell the interviewer that you have a good problem solving mindset.<\/p>\n Danielle says: Asking the right questions is essential to being a good data analyst, so every new project must begin with asking the right questions.<\/p>\n You need to ensure you\u2019re measuring what needs to be measured, so walk the interviewer through your processes of determining what data needs to be analysed to answer the question.<\/p>\n Danielle says: This is a communications skills question\u2014the interviewer is looking for evidence of your presentation skills. Explain times when you\u2019ve had to present data you\u2019ve worked on. Talk about how you\u2019ve justified the results, and what impact your results had on the project.<\/p>\n These questions tend to be hard to answer, but it\u2019s very important to prepare well for them. You need to leave a good lasting impression with the interviewer!<\/p>\n Danielle says: This is another way of saying \u2018where do you see yourself in five years?\u2019 It\u2019s always hard to answer this question!<\/p>\n Outline the next set of skills and tools you want to learn, or explain what leadership responsibilities interest you. Differentiate whether you want to go down the subject matter track, or the leadership track.<\/p>\n Do you want to have a mentor, or eventually be a mentor yourself? Is there a pivot you want to take in your career? Or do you see yourself growing into the role of data scientist<\/a>, or specializing more in programming? You\u2019ll impress the interviewer if your future career objectives are clear.<\/p>\n Danielle says: It\u2019s a good idea to prepare three to five questions in advance of the interview. If you\u2019re going to be interviewed by several people, then prepare more. You want to avoid having your questions already answered during the interview, so aim to have a surplus.<\/p>\n Avoid generic questions such as \u2018where do you see the company going?\u2019 and personalize your questions to the interviewer. This is the part of the interview where you get the opportunity to open a dialogue and show the value you can bring to the company, if you haven\u2019t already. Questions such as \u2018Who will I be most closely working with?\u2019 and \u2018What are the biggest challenges facing the team this year?\u2019 are likely to leave a good impression on your interviewer.<\/p>\n Although it can be tiring on a candidate, an interview situation where you have multiple meetings with different potential colleagues can be a great opportunity as well.<\/p>\n When she was changing careers from sales manager to development analyst, CareerFoundry Data Analytics graduate Cheryl<\/a> had a string of back-to-back interviews with her eventual employer. She used it as an opportunity to ask them as many questions as possible about the job and the business, but to also get a sense of how they worked and to show how she would fit in.<\/p>\n You\u2019ll now have a greater understanding of the kind of questions you\u2019ll be asked in interviews for data analyst positions.<\/p>\n If you\u2019re curious about becoming a data analyst, why not take CareerFoundry’s one-month Intro to Data Analytics<\/a> course? You\u2019ll come away with a solid grounding in Microsoft Excel, one of the key tools used by data analysts.<\/p>\n Not ready to commit to a full course? Try this free, 5-day data analytics short course<\/a><\/strong>.<\/p>\n If you\u2019d like to read more about working as a data analyst, we suggest you read the following articles:<\/p>\n What questions are likely to be asked in an interview for a data analyst role? And how should you answer them?<\/p>\n","protected":false},"author":106,"featured_media":348,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_lmt_disableupdate":"yes","_lmt_disable":"","footnotes":""},"categories":[3],"tags":[],"class_list":["post-3770","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analytics"],"acf":{"homepage_category_featured":false},"modified_by":"Matthew Deery","_links":{"self":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/3770","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/users\/106"}],"replies":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/comments?post=3770"}],"version-history":[{"count":3,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/3770\/revisions"}],"predecessor-version":[{"id":28710,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/3770\/revisions\/28710"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media\/348"}],"wp:attachment":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media?parent=3770"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/categories?post=3770"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/tags?post=3770"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Data analyst interview questions and answers<\/h2>\n
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1. Introductory Questions<\/h2>\n
\u201cTell me about yourself.\u201d<\/h3>\n
\u201cHow would you describe yourself as a data analyst?\u201d<\/h3>\n
\u201cWhat do you already know about the business\/product\u2014what value does your skillset add to what we\u2019re doing?\u201d<\/h3>\n
2. Data analysis questions<\/h2>\n
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These questions will focus on your own experience working in data analysis. What\u2019s your background? What have you done before?<\/p>\n\u201cPlease share some past data analysis work you\u2019ve done\u2014and tell me about your most recent data analysis project.\u201d<\/h3>\n
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\u201cTell me about a time when you ran an analysis on the wrong set of data and how did you discover your mistake?\u201d<\/h3>\n
\u201cWhat was your most difficult data analyst project? What problem did you solve and what did you contribute to make it a success?\u201d<\/h3>\n
3. Technical Questions<\/h2>\n
\u201cWhat\u2019s your favourite tool for data analysis\u2014your likes, dislikes, and why? What querying languages do you know?\u201d<\/h3>\n
\u201cWhat do you do to stay up to date with new technologies?\u201d<\/h3>\n
\u201cWhat are some of your best practices to ensure that you perform good, accurate, and informative data analysis?\u201d<\/h3>\n
\u201cHow do you know you\u2019re analyzing the right data?\u201d<\/h3>\n
\u201cTell me about a time that you had to explain the results of your analysis to stakeholders.\u201d<\/h3>\n
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<\/p>\n4. Wrap-up questions<\/h2>\n
\u201cTell me about your professional development goals and how you plan to achieve them?\u201d<\/h3>\n
\u201cDo you have any questions?\u201d<\/h3>\n
Final thoughts<\/h2>\n
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