Pratique du Shadowing: 10 Data Analyst Interview Questions and Answers - Senior Data Analyst Explains - Apprendre l'anglais à l'oral avec la vidéo

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217 phrases

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If you're prepping for a data analyst interview in 2025,

pause the Python, close that 200-page SQL PDF, and watch this first.

I've been through these interviews.

I've sat on both sides.

I've been on the applicant employee side of things,

and I've been on the side actually helping to make the hiring decisions.

So I've been on both sides of the table,

and I can tell you the questions that actually matter.

In this video, we'll go over questions that you'll likely be asked.

These are the ones that show up the most.

And I'll quickly go through how I'd answer them if I had an interview coming up tomorrow.

Without any further ado, let's get straight into it.

Question number one.

What excites you about working in data analytics?

Or why do you want to be a data analyst?

Now, if I was answering this question,

I'd go back and pick out an experience or project that I did at university,

perhaps, or during my internship or during a boot camp

and pick out that experience or project and explain the value

and importance of data and how you figured out why data was so important for helping a business.

You could even use a statistic like the exponential increase of data that businesses are having to deal with.

I was actually recently looking at some stats from Statista that said in 2003,

the total amount of data ever created was 5 exabytes.

And projections for 2026 estimate that we're expecting to produce 181 zettabytes of data annually.

And just for information, one zettabyte is one billion terabytes.

So use a statistic like that and then link it back to why you think that's a valuable industry to go into.

Also, in recent years, business data is either doubling or tripling every single year.

So the growth is absolutely exponential.

And winding that within your answer for why you want to become a data analyst

or why you want to be a data analyst and what excites you about working in data analytics,

that's a really good step forward.

Question number two.

What do you think is the most important aspect of being a data analyst?

There are a few different approaches you can take to answer this question.

You could go completely on the technical side of things,

explaining how Excel, SQL, Power BI,

data visualization, data cleaning, ETL processing are some of the most important skills, which is true.

One of the best answers I've ever heard for that question was nothing to do with technical skills.

I'm just going to be honest with you.

It was nothing to do with SQL,

Python, R, nothing to do with that.

It was to do with communication.

How important communication is becoming for data analysts.

Data analysts are being seen less and less as just part of a technical team or specialist technicians.

There's a shift happening within the data analytics industry.

There's a marked shift towards self-serve analytics.

So a data analyst is required to come in,

help set up, let's say,

for example, a dashboard, and then teach the team they created the dashboard for how to update that dashboard,

how to work with their data.

So the pendulum is swinging from just being technical analysts to almost leaning towards being consultants.

As part and parcel of that means that just learning the technical side of things isn't sufficient.

More and more applicants have ticked off all of the technical skills,

Excel, SQL, Power BI, Python,

R, Tableau, they'll have those ticked off.

Now, the differentiation comes when you're dealing with someone who knows how to communicate really well,

knows and understands business strategy,

knows and understands business against someone who perhaps is incredibly technically proficient,

but doesn't have those soft skills that are becoming increasingly important.

And so that's how I would frame that question.

The most important aspect of Being a data analyst,

I personally would focus on a soft skill.

I would go with communication.

Being able to communicate to stakeholders of different technical levels,

non-technical stakeholders, technical stakeholders, how your approaches would differ when you're explaining something to them.

That's the route I would take for this question.

Leading nicely into question number three,

how do you approach explaining technical data to a non-technical audience?

So this question sort of solidifies the foundation and importance of soft skills

and communication because it's asking how you would differ your approach

when you're explaining complex technical data to someone who doesn't have the technical knowledge that you have.

Again, in most cases, I'm going to use an example to back up my answer.

In this case, I'd explain how I'll have done enough background research in the discovery stage with the client,

understanding exactly what they need the data to show,

what data they need, and exactly what business problem they're trying to solve.

That way you'll understand not only what you're expected to deliver to the client,

but also anything additional or helpful analysis that you can add on to the delivery.

And through that consultative process,

through a constant feedback loop,

I'm constantly assessing the client's technical status and also moulding my delivery to fit their needs and their level of understanding.

So if, for example, I'm doing work for the CEO of an insurance company,

as a CEO, I know they're not going to have time for in-depth analysis.

They're just going to want the top-level overview.

They're going to want an oversight on what the data is showing.

In that same example, if I'm presenting to the actuarial team,

if I'm presenting to the underwriters,

then I can go into a bit more detail about the analysis I conducted,

where the data came from,

the sources I used, the analysis I conducted,

the decisions I made along the way.

so knowing and understanding the level of your audience is very important

and that should be the framework for answering

that question question number four tell me about a data analysis project you worked on now this is generally

if you're applying for your for your second or third role

but even

if you're applying for a junior role your first role out

of university perhaps during the summer you should do a project

that you can add to your project portfolio

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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Vocabulaire et conseils d’expression pour cette leçon

Cette leçon d’expression orale de niveau C1 s’appuie sur la vidéo « 10 Data Analyst Interview Questions and Answers - Senior Data Analyst Explains ». Les mots qui reviennent le plus souvent : project, analyst, technical, Python, experience. Cette vidéo contient 217 phrases et 2417 mots à répéter en shadowing. La partie parlée dure 12:42. Le locuteur parle vite, environ 190 mots par minute : attendez-vous à des liaisons et à des sons réduits. 83 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.

Vocabulaire clé de cette vidéo

Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :

Mot	Prononciation	Sens
stakeholder nom	/ˈsteɪkˌhəʊl.də/	agent d'entiercement, plaideur
analytics nom	/ˌæn.əˈlɪt.ɪks/	analytique
automate verbe	/ˈɔ.təˌmeɪt/	automatiser
dashboard nom	/ˈdæʃˌboɹd/	tableau de bord
assurance nom	/əˈʃʊɹəns/	assurance
differ verbe	/ˈdɪfɚ/	différer
excite verbe	/ɪkˈsaɪt/	exciter
exponential adjectif	/ˌɛk.spəˈnɛn.t͡ʃəl/	exponentiel
parameter nom	/pəˈɹæm.ə.tɚ/	paramètre
applicant nom	/ˈæp.lə.kɪnt/	demandeur, candidat
manually adverbe	/ˈmænj(u)əliː/	manuellement, à la main
tick nom	/tɪk/	tique
nicely adverbe	/ˈnaɪs.li/	agréablement
warehouse nom	/ˈwɛə(ɹ)haʊs/	entrepôt, dépôt
actuarial adjectif	/æk.tjuˈɛəɹ.i.əl/	actuariel

Les verbes à particule que vous entendrez

Mot	Prononciation	Sens
go through verbe		traverser
pick out verbe		distinguer, faire la distinction
set up verbe	/ˌsɛt ˈʌp/	mettre en place, installer
back up verbe		reculer
come up verbe		se montrer, sortir du bois
end up verbe		se terminer, se finir
figure out verbe		cerner
go back verbe		rentrer, retourner

Des phrases à répéter

Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :

I've been through these interviews.
I've sat on both sides.
Without any further ado, let's get straight into it.
What excites you about working in data analytics?
How do you ensure your project is accurate?

La grammaire de cette vidéo

Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :

Structure	Dans la vidéo
Present perfect have/has + participe passé — une action passée qui compte encore maintenant	I've been · I've sat · I've ever heard
Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait	be asked · being seen · is required
Propositions relatives who / which + proposition — une précision sur une personne ou une chose	skills, which is · someone who knows · someone who perhaps

Prononciation à surveiller

Le locuteur utilise 72 contractions et formes réduites, comme I'll, I'm, I've. Prononcez-les sous leur forme courte, telles que vous les entendez.

Les sons « sh » et « zh »: dashboard /ˈdæʃˌboɹd/, assurance /əˈʃʊɹəns/, differentiation /dɪf.əˌɹɛn.ʃiˈeɪ.ʃən/, inefficient /ˌɪn.ɪˈfɪʃ.ənt/, preposition /ˌpɹɛpəˈzɪʃən/
Mots longs — placez bien l’accent: analytics /ˌæn.əˈlɪt.ɪks/, exponential /ˌɛk.spəˈnɛn.t͡ʃəl/, parameter /pəˈɹæm.ə.tɚ/, manually /ˈmænj(u)əliː/, actuarial /æk.tjuˈɛəɹ.i.əl/

Les sons difficiles pour les francophones :

/h/ — il se souffle, il n’est pas muet: stakeholder /ˈsteɪkˌhəʊl.də/, warehouse /ˈwɛə(ɹ)haʊs/, beforehand /bɪˈfɔɹhænd/, whatsoever /ˌ(h)wʌtsoʊˈɛvɚ/
/tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: exponential /ˌɛk.spəˈnɛn.t͡ʃəl/, checklist /ˈt͡ʃɛklɪst/, pendulum /ˈpɛnd͡ʒələm/, projection /pɹəˈd͡ʒɛkʃən/, module /ˈmɑd͡ʒul/
/r/ anglais — langue recourbée, sans frotter la gorge: dashboard /ˈdæʃˌboɹd/, assurance /əˈʃʊɹəns/, parameter /pəˈɹæm.ə.tɚ/, warehouse /ˈwɛə(ɹ)haʊs/, actuarial /æk.tjuˈɛəɹ.i.əl/

Comment s’entraîner avec cette vidéo

Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme stakeholder, analytics, automate.

Qu'est-ce que la technique du Shadowing ?

Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.

Technique du shadowing : lire le guide complet étape par étape →

Pratique du Shadowing: 10 Data Analyst Interview Questions and Answers - Senior Data Analyst Explains - Apprendre l'anglais à l'oral avec la vidéo

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Vocabulaire et conseils d’expression pour cette leçon

Vocabulaire clé de cette vidéo

Les verbes à particule que vous entendrez

Des phrases à répéter

La grammaire de cette vidéo

Prononciation à surveiller

Comment s’entraîner avec cette vidéo

Qu'est-ce que la technique du Shadowing ?