シャドーイング練習: 10 Data Analyst Interview Questions and Answers - Senior Data Analyst Explains - YouTubeで英語スピーキングを学ぶ
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If you're prepping for a data analyst interview in 2025,
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If you're prepping for a data analyst interview in 2025,
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pause the Python, close that 200-page SQL PDF, and watch this first.
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I've been through these interviews.
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I've sat on both sides.
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I've been on the applicant employee side of things,
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and I've been on the side actually helping to make the hiring decisions.
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So I've been on both sides of the table,
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and I can tell you the questions that actually matter.
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In this video, we'll go over questions that you'll likely be asked.
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These are the ones that show up the most.
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And I'll quickly go through how I'd answer them if I had an interview coming up tomorrow.
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Without any further ado, let's get straight into it.
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Question number one.
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What excites you about working in data analytics?
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Or why do you want to be a data analyst?
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Now, if I was answering this question,
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I'd go back and pick out an experience or project that I did at university,
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perhaps, or during my internship or during a boot camp
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and pick out that experience or project and explain the value
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and importance of data and how you figured out why data was so important for helping a business.
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You could even use a statistic like the exponential increase of data that businesses are having to deal with.
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I was actually recently looking at some stats from Statista that said in 2003,
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the total amount of data ever created was 5 exabytes.
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And projections for 2026 estimate that we're expecting to produce 181 zettabytes of data annually.
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And just for information, one zettabyte is one billion terabytes.
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So use a statistic like that and then link it back to why you think that's a valuable industry to go into.
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Also, in recent years, business data is either doubling or tripling every single year.
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So the growth is absolutely exponential.
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And winding that within your answer for why you want to become a data analyst
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or why you want to be a data analyst and what excites you about working in data analytics,
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that's a really good step forward.
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Question number two.
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What do you think is the most important aspect of being a data analyst?
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There are a few different approaches you can take to answer this question.
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You could go completely on the technical side of things,
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explaining how Excel, SQL, Power BI,
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data visualization, data cleaning, ETL processing are some of the most important skills, which is true.
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One of the best answers I've ever heard for that question was nothing to do with technical skills.
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I'm just going to be honest with you.
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It was nothing to do with SQL,
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Python, R, nothing to do with that.
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It was to do with communication.
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How important communication is becoming for data analysts.
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Data analysts are being seen less and less as just part of a technical team or specialist technicians.
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There's a shift happening within the data analytics industry.
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There's a marked shift towards self-serve analytics.
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So a data analyst is required to come in,
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help set up, let's say,
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for example, a dashboard, and then teach the team they created the dashboard for how to update that dashboard,
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how to work with their data.
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So the pendulum is swinging from just being technical analysts to almost leaning towards being consultants.
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As part and parcel of that means that just learning the technical side of things isn't sufficient.
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More and more applicants have ticked off all of the technical skills,
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Excel, SQL, Power BI, Python,
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R, Tableau, they'll have those ticked off.
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Now, the differentiation comes when you're dealing with someone who knows how to communicate really well,
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knows and understands business strategy,
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knows and understands business against someone who perhaps is incredibly technically proficient,
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but doesn't have those soft skills that are becoming increasingly important.
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And so that's how I would frame that question.
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The most important aspect of Being a data analyst,
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I personally would focus on a soft skill.
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I would go with communication.
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Being able to communicate to stakeholders of different technical levels,
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non-technical stakeholders, technical stakeholders, how your approaches would differ when you're explaining something to them.
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That's the route I would take for this question.
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Leading nicely into question number three,
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how do you approach explaining technical data to a non-technical audience?
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So this question sort of solidifies the foundation and importance of soft skills
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and communication because it's asking how you would differ your approach
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when you're explaining complex technical data to someone who doesn't have the technical knowledge that you have.
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Again, in most cases, I'm going to use an example to back up my answer.
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In this case, I'd explain how I'll have done enough background research in the discovery stage with the client,
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understanding exactly what they need the data to show,
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what data they need, and exactly what business problem they're trying to solve.
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That way you'll understand not only what you're expected to deliver to the client,
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but also anything additional or helpful analysis that you can add on to the delivery.
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And through that consultative process,
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through a constant feedback loop,
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I'm constantly assessing the client's technical status and also moulding my delivery to fit their needs and their level of understanding.
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So if, for example, I'm doing work for the CEO of an insurance company,
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as a CEO, I know they're not going to have time for in-depth analysis.
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They're just going to want the top-level overview.
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They're going to want an oversight on what the data is showing.
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In that same example, if I'm presenting to the actuarial team,
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if I'm presenting to the underwriters,
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then I can go into a bit more detail about the analysis I conducted,
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where the data came from,
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the sources I used, the analysis I conducted,
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the decisions I made along the way.
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so knowing and understanding the level of your audience is very important
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and that should be the framework for answering
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that question question number four tell me about a data analysis project you worked on now this is generally
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if you're applying for your for your second or third role
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but even
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if you're applying for a junior role your first role out
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of university perhaps during the summer you should do a project
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that you can add to your project portfolio
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and in this question what would aim to do is aim to hit the steps of the data life cycle.
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How you went about processing each step within the project and also how you ended up with the final delivery.
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Whether that be a project you have in your portfolio or whether that be a project you did in your previous experience.
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In this instance for this question I would also ensure I've read the job description for the job I'm applying
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and if they've mentioned data cleaning,
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data warehousing, a data processing technique,
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I'd choose a project that includes that skill and explain how I worked through that during that project.
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Question number five, tell me about a time you had to learn something new
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or learn a new skill in order to analyze data effectively.
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Now for me, I go straight to Python.
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I'd had a bit of Python experience at university.
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I did a module for programming with Python and I also attended a couple of lectures,
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a couple of online courses regarding Python during university.
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But I never had any practical experience with Python.
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And when I got into my role,
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we produced a weekly market monitoring report that took data from the FCA and Companies House and a multitude of other sources.
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We amalgamated that data to create a report to present to senior stakeholders and a set of clients.
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Now, two sections of that report,
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sections one and two, had the potential to be automated.
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And through about
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a year
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and a half I took up the process of learning Python to be able to automate those sections of that weekly report.
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Now that included not only building on the knowledge I had beforehand from university
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and a couple of courses I'd taken online
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but then also learning from other people within the team using our learning
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and development budget to set up actual sessions for automation with Python delivered by experts,
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which I created a business case for and presented to the CEO and my senior managers.
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And then also learning in my own time.
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That's how I would approach that question.
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For me, I understand that question because of my experience.
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That question ends up having a pretty good answer.
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I can build a solid foundation off of it,
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learning in my own time,
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creating a business case to assign some of the L&D budget to having a Python expert come in
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and teach us how to automate using cutting-edge AI tools and large language models to help me understand the syntax.
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Also building upon the knowledge I already had,
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the knowledge base that I already had.
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Question number six, leading nicely into question number six,
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how would you go about learning a new language?
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Again, in this case, I'm lucky in the sense I can use that Python example and mold it to this question.
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But let's take SQL, for example.
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SQL I had to learn on the job because I had no experience,
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prior experience, before I got my job whatsoever.
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And so I would go through what functions I learned about SQL first.
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In all honesty, if it's a junior job,
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I would go straight with, I learned select functions.
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Then I learned join functions.
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Then I learned aggregate functions.
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Walk through some queries.
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Walk through some of the data tables within SQL you used.
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And then I would finally build upon that
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and go into how I helped create the SQL warehouse for the data tables and data feeds within my previous team.
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And question number seven.
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How do you ensure your project is accurate?
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Now, what they're asking you in this question is what's your process for mitigating data errors,
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ensuring data accuracy and integrity,
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and essentially what's your quality assurance process?
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So again, you can either take a live project that you've done in previous experience,
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or you can take a project that you did as part of your project portfolio.
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Work through the example of how you collected the data,
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how you ensured there were no missing values,
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how you ensured that the data you received was accurate,
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either by cross-checking from multiple sources.
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That's something I do my daily job.
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If I pull a set of data from one database,
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I'll ensure there's a parameter within that data that I can use to match to another database.
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And so I can cross-check those values and make sure the data is correct.
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And that's especially important in finance when you're working with
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so many financial metrics and parameters and dates
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and I'll usually pull a set of data let's say a company reg number
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and let's say any let's say profit for the last 10 years from database A
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and then I'll do the same from database B I'll pull company reg number
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and profit for the last 10 years quickly do a either a pivot table
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or an xlookup in Excel to check
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if there's any missing values in database A
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or B to see whether there is any differing values between database A
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or B because that set of data should be the same.
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And then what I'll do is if the data is really going to be used for a large or important project,
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I'll take a sample size and I'll actually go to each company financial report and pull out that data manually,
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which is largely inefficient, but that's only in the cases where the data is going to be used to inform,
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let's say, a 10, 20 million dollar deal.
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That's when data accuracy is of utmost importance.
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You can't get one decimal point wrong.
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You can't get one zero wrong.
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And that's where I would actually take a sample size and manually check.
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Again, I'm lucky in the sense in my current team,
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there's a quality assurance process in place where one analyst completes the work
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and then they send it to another analyst who conducts the quality assurance on that piece of work.
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They'll work through it from start to finish,
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that project from start to finish,
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just ensuring all the data is correct.
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They'll have a checklist, they'll work through your documentation,
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they'll ensure all the data is correct,
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they'll ensure your output is correct they'll replicate the project
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and make sure it's easy to understand and so
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that process again I'm lucky I have that previous experience
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that I can I can latch onto and build off of
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but if you've done a project for your portfolio in the past use
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that use that explain how you made sure there were no missing values
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or explain how you filled in missing values explain how you made sure
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that the data was correct
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or explain how you made sure the data was was accurate
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there you have it all right i think i was seven questions seven data analyst interview questions
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and answers that's all for this video
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if you want me to make a follow-up video where i
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go through where i go through additional questions leave the questions down in the comments below
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and i'll make another video answering those exact questions how i would answer those exact questions
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if you want to watch the video i made where i
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walk you through my real data analyst senior data analyst interview experience i'll link
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that up here and down in the description below.
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I've also made a video on the four page plan I use to prep for any interview that I might have.
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I'll leave that link up here and down in the description as well.
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So let me know down in the comments.
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I want to hear what questions you think are tough to answer in interviews you've been in or ones you don't understand.
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And I'll do my best to include them in a follow-up video and I'll go through how I would answer them.
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That's all for this video.
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If this video was useful,
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be sure to leave a like and subscribe.
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Thanks for watching and I will see you guys in the next one.
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このレッスンについて
このレッスンでは、データアナリストの面接に関する重要な質問とその回答を通じて、英語のスピーキングスキルを向上させることを目的とします。視聴している動画では、面接官が最もよく尋ねる質問に焦点を当て、それらの質問にどう答えるかを示します。これにより、英語スピーキングの流暢さを高めることができ、特にデータアナリストとしてのキャリアに関連する語彙やフレーズを学ぶ絶好の機会となります。
重要な語彙とフレーズ
- データアナリスト (Data Analyst)
- コミュニケーション (Communication)
- データビジュアライゼーション (Data Visualization)
- ETLプロセス (ETL Processing)
- シャドーイング (Shadowing)
- 統計データ (Statistics)
- ビジネスデータ (Business Data)
- 自己サービス分析 (Self-Serve Analytics)
練習のヒント
この動画のスピードとトーンに合わせて英語を練習するためには、英語シャドーイングを活用してみましょう。初めは、動画の音声を繰り返し聴き、各質問の内容を理解することが重要です。その後、音声を止めて、自分の声で繰り返してみてください。特に質問の受け答えの部分では、質問の意図を捉えるために、shadowspeakやshadowspeaksを使用して、言葉のリズムやイントネーションを模倣する練習をしましょう。
また、面接でのコミュニケーションが重要であることを理解し、そのために必要な表現を自分の言葉でまとめることもおすすめします。自分の体験やプロジェクトを語る練習をすることで、より実践的なコミュニケーションスキルが身につきます。英語の発音を良くするためには、音声を真似することで自然な発音やフレーズの使い方が習得できるでしょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。