Shadowing Practice: Data Analyst Interview Questions and Answers | Data Analytics Interview Questions | Edureka - Learn English Speaking with Video

Bir pratik modu seçin

Her mod tam ekran bir pratik odası açar.

281 cümleler

Cümleler çok kısa veya çok uzun ise, ayarlamak için Düzenle'ye tıklayın.

the job market for data analysts is highly competitive therefore the

interview process can be challenging during an interview an interviewer will assess your technical skills problem solving abilities

and your experience with data analysis the interviewer may ask questions about specific projects that you have worked on,

how you handle missing or incomplete data, your experience with statistical methods, and how you have used data to inform decision -making in the past.

To excel in the interview, it is essential to be prepared with answers to common data analyst interview questions.

To answer those questions effectively, you should prepare examples and additional details to demonstrate your understanding of the subject matter.

Additionally, it is crucial to be well versed in the latest developments and advancements in the field of data analysis.

Hi everyone, I am Elton from Edureka

and in this video I will take you through some of the most commonly asked data analyst interview questions.

But before we go ahead, make sure to hit the like button and subscribe to our channel.

You can also hit the bell icon to receive regular updates from here.

We also have a lot of training programs and certification courses on our website.

So if you are interested in it, do check out the links given in the description.

So let's not waste any more time and see our agenda for today.

We will start with the most generally asked interview questions for a data analyst interview.

After that we will see interview questions on statistics, interview questions on python and interview questions on SQL.

We will cover around 30 questions in total.

So let's head over to the first part which is the general interview questions.

So the first question for today is to mention the difference between data mining and data profiling.

You can think of data mining as the process of sorting through large datasets to identify patterns

and relationships that can help you solve business problems through data analysis.

The thing over here is that data mining will help you discover relevant information that has not been identified before,

whereas data profiling is done to evaluate a dataset for its uniqueness, logic and consistency.

This basically means that you're checking your dataset to see if it's valid for your use case.

The second difference is that in data mining, raw data is converted into valuable information.

This can either be done through scraping data from the internet or filtering the existing dataset.

But in data profiling, you cannot identify inaccurate or incorrect data values.

Now let's head over to the second question.

Define the term data wrangling in data analytics By definition,

data wrangling is the process of transforming and mapping data from one data form to another format.

Basically, data wrangling is the process wherein raw data is cleaned, structured, and enriched with the intent of making it more valuable for data analytics.

This process can turn and map out large amounts of data extracted from various sources into a more useful format.

Techniques such as merging, grouping, concatenating, joining, and sorting are used to analyze data.

Thereafter, it gets ready to be used with another data set.

Our third question for today is, what are the various steps involved in a data analytics project?

This is one of the most basic data analyst interview questions.

The various steps involved in any common analytics projects are as follows.

You start with understanding the problem.

Understand the business problem, define the organizational goals, and plan for a lucrative solution.

After that, you will start collecting data.

You will gather the right data from various sources and other information based on your priorities.

The third step is cleaning data.

Over here, you will clean the data to remove unwanted, redundant, and missing values and make it ready for analytics.

After the cleaning process is done, you will start exploring and analyzing the data.

You can do this by using data visualization and business intelligence tools, data mining techniques, and predictive modeling to analyze the data.

The last step is interpreting the results.

You will interpret the results to find out hidden patterns, future trends, and then gain insights.

Our fourth question for today is what are the common problems that data analysts encounter during analysis?

One of the biggest problems that data analysts face is handling the data, right?

It starts with handling redundant data, collecting the right data at the right time, handling data purging and storage problems,

keeping that data secure, and then handling compliance issues.

These are some of the most common problems that data analysts face, and you should have a solution for this when you attend a data analyst interview.

With that, let's head over to the fifth question, that is, list out some of the common tools that you have worked with.

Now, this question can be considered as a pattern of skillset most data analysts should have.

Let's start with database systems.

In database systems, you will need to know MySQL, MongoDB, Apache Cassandra, and CouchDB.

These are some of the most commonly used database tools.

After that, a data analyst also needs to know how to report and create dashboards, right?

For that, you will need Excel, Tableau, and Power BI.

A data analyst also needs to know how to code for

that most data analysts use python r

and spss these are the most commonly used programming languages

and you will need to have a thorough understanding of them finally we have presentations

and reports for this you can use powerpoint

and keynote during the interview you can also give some examples

and use cases of how you've used these tools before this

can help the interviewer understand your knowledge about these tools the next question is what is the significance of exploratory data analysis,

abbreviated as EDA.

The first thing over here is that exploratory data analysis can help you get a better understanding of your data.

You can use different tools and techniques to understand how your data works internally.

The second point is that you can have more confidence in your decisions.

Exploratory data analysis is based on statistics, and the decisions you make will be based on that.

So if your research is correct, then you have more probability of your decision being right.

EDA can also help you refine feature selection during modeling.

You can avoid overfitting or underfitting your model, and this analysis can help you choose the right model for your use case.

EDA can also help you discover hidden trends in your data.

This can help you find unique information that can help you make better decisions

and with that we have the next question which is explained descriptive predictive

and prescriptive analytics the main point about descriptive analytics is

that it provides insights into the past to understand what has happened you should understand

that descriptive analytics is used to understand data

and draw conclusions from it you use techniques such as hypothesis testing to find out whether certain assumptions are true.

Now, predictive analytics is used to understand the future to answer the questions on what could happen.

It uses available data to predict the future based on patterns.

Prescriptive analytics on the other hand suggests various courses of action that answers what you should do.

You can think of this as a doctor's prescription.

If you have a problem, then prescriptive analytics can give you a solution to it.

Now, all three of these are used in different use cases.

So the second difference can be about the techniques that you use in these use cases.

Descriptive analytics uses data aggregation and data mining techniques, whereas predictive analytics uses statistical models and forecasting techniques,

whereas prescriptive analytics uses simulation algorithms and optimization techniques to advise possible outcomes.

Now, the next question is, what are the different types of sampling techniques used by data analysts?

To understand this, let's first understand what sampling is.

Sampling is a statistical method to understand a subset of data

from an entire dataset to estimate the characteristics of the whole population.

What this essentially means is that, in sampling, you take a part of the entire dataset and and you try to analyze that, and based on the results of that particular sample,

you will derive conclusions for the entire dataset.

Now, for the different types of sampling techniques, we have simple random sampling,

systematic sampling, cluster sampling, stratified sampling, and judgmental sampling.

100

Now we have the question, describe univariate, bivariate, and multivariate analysis.

101

Univariate analysis is the simplest and easiest form of data analysis with the data being analyzed contains only one variable.

102

That is why it's called uni, right?

103

So with that we can see what bivariate is.

104

Bivariate analysis basically means you are comparing two variables.

105

This can be explained using correlation coefficients, linear regression, logistic regression, scatter plots, and box plots.

106

Now we have multivariate analysis.

107

This kind of analysis involves the analysis of three or more variables to understand the relationship of each variable with other variables.

108

Our tenth question and the last question for this section is what are the best methods for data cleaning?

109

So the first thing is to create a data cleaning plan by understanding where the common errors are taking place

110

and after that you will keep all the communications open.

111

You should also remember that before you start working with data, You should identify and remove all the duplicates.

112

This will lead to an easy and effective data analysis process.

113

You should also focus on the accuracy of data.

114

You can set cross -field validation, maintain value types of data, and implement mandatory constraints.

115

You can also normalize the data at the entry point so that it is less chaotic.

116

You need to ensure that all information is standardized.

117

This will lead to fewer errors on entry.

118

With that, we are done with the general interview questions that we face in data analyst interviews, and next we can see the interview questions on statistics.

119

The first question over here is how you handle missing values in a dataset.

120

Now if you've handled data before, then you know that missing values is one of the most commonly found problems in a dataset.

121

To handle this, the first method is list -wise deletion, where an entire record is excluded from analysis if any single value is missing.

122

The second method is average imputation also called as mean imputation

123

where you fill in the missing values with the average of that particular value.

124

The third method is regression substitution followed by the fourth method which is multiple imputations.

125

Now the second question

126

that we have in this section is explain the term normal

127

Normal distribution is one of the most basic concepts in graph theory

128

and it refers to a continuous probability distribution that is symmetric about the mean.

129

Some of the important points over here is that you should remember that the mean,

130

median and mode of a normal distribution is equal and all three of them are located in the center of the distribution.

131

Now let's head over to the next question which is, what is time series analysis? By definition,

132

time series analysis is a statistical procedure that deals with ordered sequence of values of a variable at equally spaced time intervals.

133

Basically, time series data is this data that is collected at adjacent time periods.

134

That is why you can see a correlation between the observations.

135

This feature distinguishes time series data from cross -sectional data.

136

I have added two graphs below for you to understand this concept better.

137

The next question is explain the difference between overfitting and underfitting.

138

Now this question is one of the most basic questions that you can find on building models.

139

You say that your model is overfitting

140

when it trains well on the training dataset but the performance drops drastically in the testing dataset.

141

This happens when your model learns random fluctuations and noise from your training dataset.

142

Underfitting occurs when your model does not really train well on the training dataset

143

and performs poorly on both training and testing dataset.

144

This occurs when your training dataset is so small

145

that it can't really derive any conclusions or if you're developing a linear model with a non -linear dataset.

146

After this we have the question, how do you treat outliers in dataset?

147

Now to answer that question, the first thing that you need to understand is what is an outlier?

148

Basically, an outlier is just a data point that is distant from other similar points.

149

This may occur due to variability in the measurement or may indicate experimental errors.

150

Now for you to treat outliers, the first thing that you can do is drop outliers where you can just delete all the records

151

that contain outliers and the second method is capping outliers data.

152

The third method is assigning a new value.

153

You can assign the mean, median or some other appropriate value over here.

154

The fourth thing that you can do is just try a new transformation.

155

And with that, we come to the next question which is What are the different types of hypothesis testing?

156

A hypothesis is basically just an educated guess about a specific parameter or population.

157

And hypothesis testing can be described as the procedure used by statisticians and scientists to accept or reject a statistical hypothesis.

158

Now, in hypothesis testing, you have two different kinds of hypothesis.

159

The first thing is a null hypothesis, and the second one is an alternative hypothesis.

160

Now you can also get another question based on this concept.

161

What is the difference between type 1 and type 2 errors?

162

The type 1 error occurs when the null hypothesis is rejected even if it is true,

163

whereas the type 2 error occurs when the null hypothesis is not rejected even if it is false.

164

Now let's end this section over here and head over to the interview questions on python.

165

The first question in this section is what is the correct syntax for reshape function in numpy.

166

For you to use the reshape function all you need to do is use two parameters.

167

The first parameter is the array name and the second parameter is the shape of that array.

168

Now, I have attached a snippet that uses the reshape function over here.

169

Go through it and type the output in the comment section.

170

What are the different ways to create a data frame in pandas?

171

For this question, you can list down the two ways to create data frames in pandas.

172

The first method is by initializing list and the second one is initializing a dictionary.

173

For you to create a pandas data frame using lists, the first thing that you need to do is type pandas .dataframe and then give it two parameters,

174

data and columns.

175

Now, the data parameter consists of a list containing different lists.

176

This will later be converted into your records.

177

And the second parameter, columns, will consist of your column names.

178

Initializing a pandas data frame using dictionary is even simpler.

179

All you need is a dictionary with values which you can convert into a data frame.

180

The syntax is just pandas .dataframe, and in the parameters, you will mention the dictionary's name.

181

For the next question we have, there are two arrays, A and B.

182

Stack the arrays A and B horizontally using the NumPy library in Python.

183

Now, for this question, there are two ways you can approach it.

184

The first way is the concatenate method, where you will type numpy .concatenate A and B, and then mention the axis.

185

The second method is the HedgeStack method.

186

This is another NumPy method where in the parameters, you will provide a list with the values A, B.

187

Next question is how can you add a column to a panda's dataframe?

188

Doing this is extremely simple.

189

All you need to do is type the dataframe's variable name followed by the column name.

190

This is then assigned values from a list.

191

So let's move on to the next question, which is, how will you print 4 random integers between 1 and 15 using numpy?

192

This is again a very simple question.

193

If you refer to the snippet that I have given you over here, you can see that in the second line I have used numpy .random .randent and I have given a few parameters.

194

This randent function can help you generate your numbers.

195

Now, the randent function itself has 3 parameters, right?

196

In this method, the first value represents the start point.

197

You won't find any numbers being generated before this point.

198

The second value is the endpoint.

199

You won't find any values being generated after this number.

200

The third value that I mentioned over here is the number of integers that we want from this method.

201

Now let's move on to the next question.

202

Suppose there is an array that has values 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 how will you display the following values from the array?

203

1, 3, 5, 7, 9.

204

Now if we observe the values that need to be extracted, we can see that all of those numbers are just odd numbers, right?

205

So this question again refers to some of the basics of programming.

206

All you need to do is create a list with the values given at the start of the question

207

and then check for the odd numbers.

208

You can do this by using the modulus operator.

209

Now the next question is, suppose there is an array with the following values.

210

Extract the value 8 using 2d indexing.

211

To answer this question, all you need to do is start by creating your numby array.

212

And over here you will pass the same values as given in the question.

213

You can do this as follows.

214

Now if you check your array, then you can see that the value 8 lies in the third row and the second column, right?

215

But the indexing in Python starts from zero.

216

So the third row becomes the second row and the second column becomes the first column.

217

So if you type those indexes, you will definitely get your answer.

218

And then we have the next question.

219

How do you select specific columns from a data frame?

220

Now this is also a basic question.

221

You can do this with a single line of code.

222

You will start by typing the data frame name and you will open two sets of square brackets inside one another.

223

Once you do this, you can type the names of the columns that you want inside the inner square brackets.

224

With that, we can come to the fourth section of this video which is data analyst interview questions on SQL.

225

The first question in this section is what is the difference between where clause and having clause in SQL.

226

Let's do this with a table the first point is

227

that where clause operates on row data which means that

228

when you use a where clause you are only operating on

229

individual rows of data the having clause on the other hand

230

operates on aggregated data in the where clause the filtering occurs before the groupings are made in the data.

231

But the HAVING clause is used to filter values from a group of data.

232

In the WHERE clause, you can't use aggregate functions.

233

But HAVING clause allows you to do that.

234

The second question is a very common sort of question.

235

Is the following SQL query correct?

236

Now in the interview, the interviewer can ask any kind of SQL query.

237

You need to make sure that you have a thorough understanding of SQL before attempting to answer these questions.

238

So the query is as follows.

239

Select customer ID

240

and order date from year as order year from order where order year is greater than or equal to 2023.

241

Now is the SQL query correct?

242

Pause the video over here and analyze the question.

243

You can type your answers in the comment section down below. So, what's your answer?

244

If you answered that the query is correct, then you are wrong.

245

The query stated above is incorrect, as we cannot use the alias name while filtering data using the WHERE clause.

246

It will throw an error.

247

The correct form of the SQL query is as follows.

248

All you need to do is pass the order date through here, not directly.

249

Now with that, let's see the next question which is, what is subquery in SQL?

250

A subquery in SQL is just a query within another query.

251

It is also known as a nested query or an inner query.

252

Subqueries are just used to enhance the data which is queried by the main query.

253

These queries come in two types, correlated and non -correlated.

254

You can find more information about correlated and non -correlated subqueries in the blog that has been given in the description.

255

Next, we have what is the difference between delete and truncate statements.

256

The delete command can delete rows in your table.

257

You can delete individual rows or a group of rows.

258

Whereas the truncate statement is used to delete all the rows from the table.

259

You can roll back to your data after using a delete statement.

260

You can think of rollback as a save point in SQL.

261

With rollback, you can navigate to a previous version of your data.

262

This feature is not available with the truncate statements.

263

The third difference is the category of commands that these two commands belong to.

264

Delete is a DML command whereas truncate is a DDL command.

265

The fourth difference between these commands lie within speed.

266

The delete command is slower than the truncate command.

267

Let's now head over to the next question which is what do you understand by query optimization?

268

Query optimization is basically a phase that deals with optimizing the efficiency of your query.

269

With query optimization, your outputs are generated faster, a large number of queries can be executed in less time,

270

and query optimization reduces the time and space complexity of your queries.

271

And with that, we have reached the end of this session.

272

This video is an introductory video that gives you a basic overview of the interview questions.

273

I highly recommend that you also watch our in -depth videos that talk about each of these sections separately.

274

The links to those videos are provided below.

275

I really do hope to see you there.

276

And until then, happy learning.

277

I hope you have enjoyed listening to this video.

278

Please be kind enough to like it

279

and you can comment any of your doubts and queries and we will reply them at the earliest.

280

Do look out for more videos in our playlist and subscribe to Edureka channel to learn more.

281

Happy learning!

Uygulamayı İndir

Akıcı konuşmak için ihtiyacınız olan her şey

AI TelaffuzHer cümleyi puanla

IPA AlıştırmasıHer sesi ustalaştır

Kelime BilgisiKelime bankanızı oluşturun

Kelime OyunuOynarken öğrenin

QR Tara

Download on theApp Store Get it onGoogle Play

📺 Aynı Kanal

ChatGPT Tutorial | Chat GPT Explained | What is Chat GPT ? | Edureka

Daily ConversationWorking Adults

✨ Video Öner

Top 20 Data Analyst Interview Questions And Answers for 2026

124 cümleler

Content Moderation Most Important Interview Question and Answers | @CorporateWala | Ankit Yadav

Interview EnglishWorking Adults

125 cümleler

Bu dersin kelimeleri ve konuşma notları

Bu konuşma dersi “Data Analyst Interview Questions and Answers” videosuna dayanıyor. En çok tekrarlanan kelimeler: value, interview, analysis, dataset, query. Bu videoda gölgeleme çalışması için 280 cümle ve 3805 kelime var. Konuşma bölümü 23:30 sürüyor. Konuşmacı dakikada yaklaşık 162 kelimeyle, günlük konuşmaya yakın doğal bir hızda konuşuyor. Kelimelerin %82’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.

Bu videodaki önemli kelimeler

Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:

Kelime	Telaffuz	Anlam
query isim	/ˈkwɪɹ.i/	sorgulama
hypothesis isim	/haɪˈpɒθɪsɪs/	hipotez, faraziye
parameter isim	/pəˈɹæm.ə.tɚ/	parametre
clause isim	/klɔːz/	cümlecik
delete fiil	/dɪˈliːt/	silmek
array isim	/əˈɹeɪ/	elbise, giyecek
variable sıfat	[ˈvæɹ.i.ə.bl̩]	değişken
occur fiil	/əˈkɜː/	olmak
column isim	/ˈkɒləm/	kolon, sütun
optimization isim	/ˌɑptəmaɪˈzeɪʃən/	optimizasyon
commonly zarf	/ˈkɑmənli/	sık sık, sıkça
filter isim	/ˈfɪltɚ/	filtre
interviewer isim	/ˈɪntɚvjuɚ/	röportajcı
refer fiil	/ɹɪˈfɜː/	zikretmek
conclusion isim	/kənˈkluːʒən/	son

Duyacağınız deyimsel fiiller

Kelime	Anlam
find out fiil	anlamak, ortaya çıkarmak
check out fiil	gözden geçirmek

Tekrar etmeye değer cümleler

Videodan, günlük konuşmada yeniden kullanabileceğiniz kısa ve tam cümleler:

Let's start with database systems.
You can think of this as a doctor's prescription.
To understand this, let's first understand what sampling is.
How do you select specific columns from a data frame?

Bu videodaki dil bilgisi

Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:

Yapı	Videoda
Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem	have worked · has not been identified · has happened
Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli	has not been identified · is done · is converted
İlgi cümlecikleri who / which + cümle — bir kişi ya da şey hakkında ek bilgi	part which is · question which is · method which is

Dikkat edilecek telaffuzlar

Konuşmacı can't, won't, you're gibi kısaltılmış biçimleri 8 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.

“th” sesleri: hypothesis /haɪˈpɒθɪsɪs/, thorough /ˈθʌɹoʊ/, gather /ˈɡæðɚ/, thereafter /ˌðɛɹˈæf.tɚ/
“sh” ve “zh” sesleri: optimization /ˌɑptəmaɪˈzeɪʃən/, conclusion /kənˈkluːʒən/, dictionary /ˈdɪk.ʃəˌnɛ.ɹi/, correlation /ˌkɔɹ.əˈleɪ.ʃən/, essentially /ɪˈsɛnʃəli/
Uzun kelimeler — vurguyu doğru yere koyun: analytics /ˌæn.əˈlɪt.ɪks/, hypothesis /haɪˈpɒθɪsɪs/, parameter /pəˈɹæm.ə.tɚ/, statistical /stəˈtɪs.tɪ.kəl/, optimization /ˌɑptəmaɪˈzeɪʃən/

Türkçe konuşanların zorlandığı sesler:

/w/ — dudaklar yuvarlak, /v/ değil: query /ˈkwɪɹ.i/, whereas /(h)wɛɹˈæz/, equally /ˈiːkwəli/, sequence /ˈsiː.kwəns/
Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: query /ˈkwɪɹ.i/, clause /klɔːz/, whereas /(h)wɛɹˈæz/, statistical /stəˈtɪs.tɪ.kəl/, procedure /pɹəˈsiː.d͡ʒə/
/æ/ — “e”den daha açık: analyst /ˈænəlɪst/, analytics /ˌæn.əˈlɪt.ɪks/, parameter /pəˈɹæm.ə.tɚ/, variable [ˈvæɹ.i.ə.bl̩], whereas /(h)wɛɹˈæz/

Bu videoyla nasıl çalışılır

Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
Kendinizi kaydedin ve orijinaliyle karşılaştırın; query, hypothesis, parameter gibi kelimelere özellikle dikkat edin.

Gölgeleme Tekniği Nedir?

Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.

Shadowing tekniği: adım adım eksiksiz rehberi okuyun →

Shadowing Practice: Data Analyst Interview Questions and Answers | Data Analytics Interview Questions | Edureka - Learn English Speaking with Video

Bir pratik modu seçin

Uygulamayı İndir

ChatGPT Tutorial | Chat GPT Explained | What is Chat GPT ? | Edureka

Top 20 Data Analyst Interview Questions And Answers for 2026

Content Moderation Most Important Interview Question and Answers | @CorporateWala | Ankit Yadav

How To Answer Data Analyst Interview Questions to Get a Job

QA Automation Interview Questions and Answers

FMCG Interview Questions and Answer for Beginners and Experts!

How AI Is Ruining Job Interviews

Data Science Interview Prep: 3 Key Resources You Need to Know

Uber Data Engineering Mock Interview - Ride-Sharing Data Warehouse Schema

Data Analyst Interview 2024 | SQL | POWER BI | Data Science | Mock Interview

The Essential UX Interview Questions (And How To Answer Them!)

Phlebotomy Job Interview Questions and Answers! Get hired!

Google Interview Tips That Actually Get You Hired