शैडोइंग अभ्यास: 10 Data Analyst Interview Questions and Answers - Senior Data Analyst Explains - YouTube के साथ अंग्रेजी बोलना सीखें

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

⏸ रुका हुआ

गति:

217 वाक्य

यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।

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.

100

How you went about processing each step within the project and also how you ended up with the final delivery.

101

Whether that be a project you have in your portfolio or whether that be a project you did in your previous experience.

102

In this instance for this question I would also ensure I've read the job description for the job I'm applying

103

and if they've mentioned data cleaning,

104

data warehousing, a data processing technique,

105

I'd choose a project that includes that skill and explain how I worked through that during that project.

106

Question number five, tell me about a time you had to learn something new

107

or learn a new skill in order to analyze data effectively.

108

Now for me, I go straight to Python.

109

I'd had a bit of Python experience at university.

110

I did a module for programming with Python and I also attended a couple of lectures,

111

a couple of online courses regarding Python during university.

112

But I never had any practical experience with Python.

113

And when I got into my role,

114

we produced a weekly market monitoring report that took data from the FCA and Companies House and a multitude of other sources.

115

We amalgamated that data to create a report to present to senior stakeholders and a set of clients.

116

Now, two sections of that report,

117

sections one and two, had the potential to be automated.

118

And through about

119

a year

120

and a half I took up the process of learning Python to be able to automate those sections of that weekly report.

121

Now that included not only building on the knowledge I had beforehand from university

122

and a couple of courses I'd taken online

123

but then also learning from other people within the team using our learning

124

and development budget to set up actual sessions for automation with Python delivered by experts,

125

which I created a business case for and presented to the CEO and my senior managers.

126

And then also learning in my own time.

127

That's how I would approach that question.

128

For me, I understand that question because of my experience.

129

That question ends up having a pretty good answer.

130

I can build a solid foundation off of it,

131

learning in my own time,

132

creating a business case to assign some of the L&D budget to having a Python expert come in

133

and teach us how to automate using cutting-edge AI tools and large language models to help me understand the syntax.

134

Also building upon the knowledge I already had,

135

the knowledge base that I already had.

136

Question number six, leading nicely into question number six,

137

how would you go about learning a new language?

138

Again, in this case, I'm lucky in the sense I can use that Python example and mold it to this question.

139

But let's take SQL, for example.

140

SQL I had to learn on the job because I had no experience,

141

prior experience, before I got my job whatsoever.

142

And so I would go through what functions I learned about SQL first.

143

In all honesty, if it's a junior job,

144

I would go straight with, I learned select functions.

145

Then I learned join functions.

146

Then I learned aggregate functions.

147

Walk through some queries.

148

Walk through some of the data tables within SQL you used.

149

And then I would finally build upon that

150

and go into how I helped create the SQL warehouse for the data tables and data feeds within my previous team.

151

And question number seven.

152

How do you ensure your project is accurate?

153

Now, what they're asking you in this question is what's your process for mitigating data errors,

154

ensuring data accuracy and integrity,

155

and essentially what's your quality assurance process?

156

So again, you can either take a live project that you've done in previous experience,

157

or you can take a project that you did as part of your project portfolio.

158

Work through the example of how you collected the data,

159

how you ensured there were no missing values,

160

how you ensured that the data you received was accurate,

161

either by cross-checking from multiple sources.

162

That's something I do my daily job.

163

If I pull a set of data from one database,

164

I'll ensure there's a parameter within that data that I can use to match to another database.

165

And so I can cross-check those values and make sure the data is correct.

166

And that's especially important in finance when you're working with

167

so many financial metrics and parameters and dates

168

and I'll usually pull a set of data let's say a company reg number

169

and let's say any let's say profit for the last 10 years from database A

170

and then I'll do the same from database B I'll pull company reg number

171

and profit for the last 10 years quickly do a either a pivot table

172

or an xlookup in Excel to check

173

if there's any missing values in database A

174

or B to see whether there is any differing values between database A

175

or B because that set of data should be the same.

176

And then what I'll do is if the data is really going to be used for a large or important project,

177

I'll take a sample size and I'll actually go to each company financial report and pull out that data manually,

178

which is largely inefficient, but that's only in the cases where the data is going to be used to inform,

179

let's say, a 10, 20 million dollar deal.

180

That's when data accuracy is of utmost importance.

181

You can't get one decimal point wrong.

182

You can't get one zero wrong.

183

And that's where I would actually take a sample size and manually check.

184

Again, I'm lucky in the sense in my current team,

185

there's a quality assurance process in place where one analyst completes the work

186

and then they send it to another analyst who conducts the quality assurance on that piece of work.

187

They'll work through it from start to finish,

188

that project from start to finish,

189

just ensuring all the data is correct.

190

They'll have a checklist, they'll work through your documentation,

191

they'll ensure all the data is correct,

192

they'll ensure your output is correct they'll replicate the project

193

and make sure it's easy to understand and so

194

that process again I'm lucky I have that previous experience

195

that I can I can latch onto and build off of

196

but if you've done a project for your portfolio in the past use

197

that use that explain how you made sure there were no missing values

198

or explain how you filled in missing values explain how you made sure

199

that the data was correct

200

or explain how you made sure the data was was accurate

201

there you have it all right i think i was seven questions seven data analyst interview questions

202

and answers that's all for this video

203

if you want me to make a follow-up video where i

204

go through where i go through additional questions leave the questions down in the comments below

205

and i'll make another video answering those exact questions how i would answer those exact questions

206

if you want to watch the video i made where i

207

walk you through my real data analyst senior data analyst interview experience i'll link

208

that up here and down in the description below.

209

I've also made a video on the four page plan I use to prep for any interview that I might have.

210

I'll leave that link up here and down in the description as well.

211

So let me know down in the comments.

212

I want to hear what questions you think are tough to answer in interviews you've been in or ones you don't understand.

213

And I'll do my best to include them in a follow-up video and I'll go through how I would answer them.

214

That's all for this video.

215

If this video was useful,

216

be sure to leave a like and subscribe.

217

Thanks for watching and I will see you guys in the next one.

TRENDING

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

☕ हमें एक कॉफी पिलाएं

आपके समर्थन के कारण ShadowingEnglish 100% मुफ्त है। सर्वर और AI की लागत अधिक है — आपकी कॉफी हमें आगे बढ़ने में मदद करती है! 🙏

PayPal के माध्यम से दान करें

ShadowingEnglish.com – अंग्रेज़ी शैडोइंग अभ्यास

शैडोइंग अभ्यास: 10 Data Analyst Interview Questions and Answers - Senior Data Analyst Explains - YouTube के साथ अंग्रेजी बोलना सीखें

लोकप्रिय

इस पाठ के बारे में

प्रमुख शब्दावली और वाक्यांश

अभ्यास के सुझाव

शैडोइंग तकनीक क्या है?

शैडोइंग अभ्यास: 10 Data Analyst Interview Questions and Answers - Senior Data Analyst Explains - YouTube के साथ अंग्रेजी बोलना सीखें

ऐप डाउनलोड करें

इस पाठ के बारे में

प्रमुख शब्दावली और वाक्यांश

अभ्यास के सुझाव

शैडोइंग तकनीक क्या है?