Luyện nói tiếng Anh bằng Shadowing qua video: Top 5 Insider Interview Questions Data Analysts Must Master Before Any Interview!

I analyzed thousands of data analysts interview questions,

⏸ Tạm dừng

Tốc độ:

205 câu

Nếu các câu quá ngắn hoặc quá dài, hãy bấm Edit để chỉnh sửa.

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,

100

given the problem requirements.

101

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

102

So let's try to tackle the first part.

103

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

104

Well, let's look at the problem.

105

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

106

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

107

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

108

So looking at this, I guess the actual answer

109

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

110

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

111

Now, no matter what the overall rating is,

112

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

113

Let's take another example.

114

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

115

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

116

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

117

Given a data set of events,

118

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

119

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

120

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

121

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

122

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

123

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

124

So Python questions comprised of two different types.

125

One is data manipulation that involves pandas.

126

So that means basically taking a SQL interview question

127

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

128

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

129

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

130

The reason why Python is so popular now is

131

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

132

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

133

dashboards like Streamlit, and also being able to implement anything

134

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

135

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

136

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

137

At the end of the day,

138

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

139

Example question that was asked recently at Deloitte would be,

140

given a list of integers,

141

identify all of the duplicate values in the list.

142

So pretty standard.

143

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

144

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

145

And those would be the values that we would return.

146

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

147

So given two strings, A and B,

148

write a function can shift to return whether

149

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

150

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

151

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

152

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

153

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

154

which is more experimentation.

155

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

156

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

157

The company might ask you,

158

what are Z tests and T tests?

159

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

160

A t-test is a statistical test

161

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

162

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

163

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

164

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

165

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

166

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

167

that you might have to know

168

so for example the capital approval rates have gone down for

169

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

170

and the approval rate went down 82 this week

171

which is a statistically significant reduction the first analysis shows

172

that all the approval rates stayed flat or increased over time

173

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

174

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

175

if you know you know

176

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

177

when basically a trend shows in several groups either disappears

178

or is reversed when combining the data and that's

179

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

180

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

181

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

182

I would then highly recommend InterviewPro,

183

which is a company that I founded five years ago.

184

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

185

and we have hundreds of data analysts,

186

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

187

Specifically, we have a learning path that you can take

188

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

189

inference, data analytics case studies,

190

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

191

But don't just take my word for it.

192

I know it sounds great and stuff.

193

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

194

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

195

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

196

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

197

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

198

Thank you for your work on the platform.

199

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

200

so I recommended you get your platform.

201

We're so happy about our success stories.

202

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

203

obviously check out Interview Query.

204

You can use the link below.

205

Thanks for watching everyone.

TRENDING

Phổ biến

Phương Pháp Shadowing Là Gì?

Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.

ShadowingEnglish.com – Luyện Nói Tiếng Anh Bằng Shadowing

Nói tiếng Anh lưu loát với phương pháp Shadowing — nghe người bản xứ, đọc theo từng câu, rèn phản xạ ngôn ngữ thật sự. Được hàng ngàn người học IELTS tin dùng.

Luyện nói tiếng Anh bằng Shadowing qua video: Top 5 Insider Interview Questions Data Analysts Must Master Before Any Interview!

Phổ biến

Bối cảnh & Nền tảng

5 Câu Hỏi Hàng Đầu Để Thực Hiện Trong Giao Tiếp Hàng Ngày

Hướng Dẫn Luyện Nói Từng Bước

Phương Pháp Shadowing Là Gì?

Luyện nói tiếng Anh bằng Shadowing qua video: Top 5 Insider Interview Questions Data Analysts Must Master Before Any Interview!

Tải Ứng Dụng

Bối cảnh & Nền tảng

5 Câu Hỏi Hàng Đầu Để Thực Hiện Trong Giao Tiếp Hàng Ngày

Hướng Dẫn Luyện Nói Từng Bước

Phương Pháp Shadowing Là Gì?