シャドーイング練習: Top 5 Insider Interview Questions Data Analysts Must Master Before Any Interview! - 動画で英語スピーキングを学ぶ

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I analyzed thousands of data analysts interview questions,

and today we're going to go over the top five different types of questions

that I saw that you'll be expected to face in your next interview.

Number one, behavioral interview questions.

Behavioral interview questions are the most common type of question

that you're expected to see in almost any data analyst interview going forward.

Unlike data scientists that spend probably 70% of their time coding and doing IC work and maybe 30% talking to stakeholders,

data analyst work probably falls more in that 50-50 range,

where they're spending a lot more time actually interacting with people,

gathering requirements, creating charts, and collaborating with stakeholders.

Data analyst positions are generally pretty junior.

Another reason is that most people that apply for data analyst positions are generally more junior in their career.

So for a lot of new grads,

you're expected to see a lot of behavioral questions because they're trying to see how well you work with other people.

For example, some really common questions are,

how would you approach

x issue did you ever face y problem tell me about a situation where your time management went wrong

and how you fix it in answering these kinds of questions the easiest way to do

so is always to pick two

or three different stories where in your past experience you were

kind of depicted as a rock stone the best thing about

picking just a few different stories where you can rehearse them really well

and know them down to a t is that you can reapply these for all different kinds of question.

That same story that you use where you're showcasing your star project,

you can probably also talk about how you apply time management in there,

how you maybe you solve the conflict with another group member,

and it also describes how you work with other people.

Another way to approach this is to use a star framework,

which is situation, task, action, and result.

And I don't want to go too into it here.

You can always check out my other video on data science behavioral interview questions down below.

Now, I want to stop right here before going to the next question

because I want to add a caveat about how often the interview questions are going to show up for your interview.

One thing I noticed in my analysis of different company interviews is

that behavioral interview questions for data analyst interviews do come up more often depending on a variety of factors.

Namely, if the company that you're interviewing for is more non-tech,

so for example, something in finance like JP Morgan or airlines like American Airlines or even consulting like Deloitte,

A lot of the times they're going to emphasize behavioral interview questions more than the actual technical ones.

The more tech-focused companies like Facebook,

Amazon, or the fan companies are going to emphasize more technical questions compared to the behavioral ones.

And sometimes for a lot of these non-tech companies,

I've seen them ask only behavioral interview questions and they won't even ask you a single technical question at all.

The second thing to note is that the number of behavioral interview questions

that are asked is also correlated with generally less pay.

That's apparent because our next data analyst interview question type is on SQL.

Now SQL questions also range in difficulty and they also depend on what kind of data analyst interview you're going for.

So for example, pulling directly from Glassdoor and other sources,

you'll see companies like American Airlines will literally just ask you

on a scale of one to 10 how good your skill level is on SQL,

Python, and other technical skills.

They might also ask you an example of simple questions on SQL,

such as explaining what a CTE is,

or describing the difference between different types of joints.

These questions are meant to test your knowledge of SQL,

but they're not really expecting you to really test your effectiveness in writing SQL query.

Again, these questions show up for more non-tech companies,

whereas actual tech companies like Facebook and Amazon will give you case studies like these.

So for example, they'll give you two tables,

one with managers and one with employees,

and they'll ask you to write a query to find the manager with the biggest team side.

Now, these kinds of SQL questions are more like SQL case studies,

and companies like Amazon for their data analyst interview will even go a step further with their technical screen on SQL questions.

They'll ask you first a question like the manager team size is one,

and then they'll give you harder and harder questions.

You're given the same two tables.

Now write a query to find all the employees that join before the manager.

For the Amazon data analyst interview,

they'll give you five concurrent questions,

each one a little bit harder than the next.

Each candidate is then benchmarked on how well they do.

So if you can solve all five questions within the amount of time for the technical interview,

then they'll generally pass that interview.

But over time, if they see that more candidates are passing the technical screen pretty easily,

then they'll make those questions slightly harder.

Because at the end of the day,

Amazon is trying to filter out around 50% of the candidates at every step of the interview.

If you want to check out more SQL interview questions,

check out my video below where I've conducted mock interviews with

multiple different kinds of data scientists at top tech companies on SQL.

The next common question that you're going to see is the analytics case study question.

Now analytics case study questions can comprise of various levels of difficulty.

The easiest ones are generally the most straightforward where they're asking you a theoretical case.

So for example, what metrics would you use to measure the success of a product?

Another one would be something like,

we've seen a drop in conversion rate over the last week,

how would you investigate what would happen?

Now while these are more theoretical in nature,

sometimes you'll actually have to go into the actual data set that the interviewer provide and analyze the actual data.

And I really like these questions myself as an interviewer

because these give me a two-for-one kind of case study where I can figure out,

one, how good their analytical reasoning skills are,

and two, exactly how good their actual SQL or technical skills are as well.

So let's take an example question.

So you're given a table that represents search results from all searches on Facebook.

The query column is a search term and the position column represents each position their search result came in.

And lastly, the rating column represents human of a search result from one to five,

but five is high relevance and one is low.

So in this example, we have to solve two things.

One is we actually have to create the metric on our own,

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given the problem requirements.

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And the second is that we also have to write the query.

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So let's try to tackle the first part.

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How do we construct a metric that actually applies a good rating for every search result?

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Well, let's look at the problem.

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For example, if the first result is rated at a five and the last result is rated at a one, that's good.

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But it'd be better if the first result is rated five and the last result is also rated five.

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The worst example scenario would be if the first result is rated as a one and the last result is a file.

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So looking at this, I guess the actual answer

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that we should do is we should inverse the position as a weighted factor.

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In this case, we would apply one over the position as a weighted score.

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Now, no matter what the overall rating is,

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we have a way to weigh the position into the formula.

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Let's take another example.

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So let's say that you're given event data from a social networking site like Facebook.

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A product manager is interested in understanding the average number of sessions that occur every day.

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However, the company has not technically defined what a session is yet.

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Given a data set of events,

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how would you define what a raw session looks like for a company like Facebook?

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Then write a query to return the average number of sessions per day.

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So again, from our example here, we're doing two things.

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One is we're actually defining the metric at large,

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and then two is that we're actually going through and writing a query for this example schema.

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The next common interview question that we see on the interview is Python.

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So Python questions comprised of two different types.

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One is data manipulation that involves pandas.

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So that means basically taking a SQL interview question

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and transposing it into more of a pandas interview question because they both deal with data manipulation.

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The second one is more around general kind of string manipulation,

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data manipulation that you can do with just using other data structures in Python.

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The reason why Python is so popular now is

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that data analysts have slowly transitioned away from R and S-A-S as kind of the main tools for data analysis,

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given that Python now has much more modularity when it comes to working with different kinds of data analytics tools,

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dashboards like Streamlit, and also being able to implement anything

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that you need more in a script in production easier than scaling out languages like R.

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The other thing to note is that most Python interview questions are going to be fairly easy.

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You won't expect anything from leak code in your data analyst interview.

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At the end of the day,

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the most that data analysts will work on in pipeline is probably writing scripts and not really scaling anything in production.

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Example question that was asked recently at Deloitte would be,

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given a list of integers,

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identify all of the duplicate values in the list.

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So pretty standard.

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A solution here would be to create a dictionary and add all of the integers as the keys,

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and then the counts of the actual duplicates would be anything greater than one.

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And those would be the values that we would return.

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Another really common Python data analyst interview question we've been seeing recently is one around string shift.

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So given two strings, A and B,

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write a function can shift to return whether

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or not A can be shifted to the number of places to get to B.

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The last question that we see on data analyst interviews are around statistics and A-B testing type interview questions.

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Baseline of a lot of data analytics work is obviously fundamentally based in stats.

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Specifically, there's a couple of concepts that we wanna talk about here, right?

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Cause there's the causal inference part of statistics and then there's also the A-B testing part,

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which is more experimentation.

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At the base of both of those is just general statistics questions

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that you might perceive if you're interviewing for an entry-level data analyst.

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The company might ask you,

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what are Z tests and T tests?

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What's the difference between them and when should we use either of them?

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A t-test is a statistical test

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that uses the t family of distributions to compare two means to see if they're significantly different from each other.

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And they're typically conducted when the population standard deviation is unknown.

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So generally these kinds of questions are gonna be more definition-based.

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You're just gonna have to remember these in general,

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especially if you're expected to have a more broader and fundamental statistical analysis background.

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More practical case study is one where the solution the case study is actually a type of formula

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that you might have to know

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so for example the capital approval rates have gone down for

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our overall approval rate let's say last week is 85

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and the approval rate went down 82 this week

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which is a statistically significant reduction the first analysis shows

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that all the approval rates stayed flat or increased over time

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when we have products product one 84 85 percent week over week product 277 to 77 product 381 82

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and product 488 to 88 what could be the cause of the decrease now

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if you know you know

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and for example this solution the actual answer is the simpson's paradox the simpson's paradox is a phenomenon

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when basically a trend shows in several groups either disappears

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or is reversed when combining the data and that's

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because the size of some of these groups is maybe larger than the size of other groups

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which causes conflation when we merge all the data together wow this is a lot of information

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and it's a lot to How do I actually prepare for my next interview?

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I would then highly recommend InterviewPro,

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which is a company that I founded five years ago.

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We are the number one interview prep platform for data analytics,

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and we have hundreds of data analysts,

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interview questions, and course material on data analytics as well.

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Specifically, we have a learning path that you can take

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that has over 10 plus hours of course content that guides you through causal inference,

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inference, data analytics case studies,

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and different types of interview questions like statistics, SQL, Python, and more.

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But don't just take my word for it.

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I know it sounds great and stuff.

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Let's look at Siobhan, who actually landed a job using the interview query.

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He sent me an email that said, Hi, Jay.

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I actually use your videos and platform to help me prepare for my interview.

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And I got a job as a data analyst and product analytics at Autodesk.

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Before my interviews, I would watch your videos and go over any questions they may ask.

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Thank you for your work on the platform.

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I had a friend interviewing for data jobs and he's an engineer,

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so I recommended you get your platform.

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We're so happy about our success stories.

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And if you're looking for facing your next interview,

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obviously check out Interview Query.

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You can use the link below.

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Thanks for watching everyone.

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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。

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