Data Science Interview Questions | Data Science Tutorial | Data Science Interviews | Edureka


( Data Science Training – )
This Data Science Interview Questions and Answers video will help you to prepare yourself for Data Science and Big Data Analytics interviews. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science, Big Data Analytics and Machine Learning. Below are the topics covered in this tutorial:

1. Data Science Job Trends
2. Data Science Interview Questions
A. Statistics Questions
B. Data Analytics Questions
C. Machine Learning Questions
D. Probability Questions
3. Conclusion

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How it Works?

1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!

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About the Course

Edureka’s Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on ‘R’ capabilities.

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Why Learn Data Science?

Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

After the completion of the Data Science course, you should be able to:
1. Gain insight into the ‘Roles’ played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R

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Who should go for this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. ‘R’ professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies

For more information, Please write back to us at or call us at IND: 9606058406 / US: 18338555775 (toll free).


Customer Reviews:

Gnana Sekhar Vangara, Technology Lead at, says, “Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now…Thanks EDUREKA and all the best.”



Comment List

  • edureka!
    December 8, 2020

    Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Data Science Training Certification Curriculum, Visit our Website:

  • edureka!
    December 8, 2020

    Thank u very much Sir..

  • edureka!
    December 8, 2020

    Thank You very much Karthik and Edureka for providing this informative heads-up about the Interview ! Thanks a lot for the advises at the end as well!

  • edureka!
    December 8, 2020

    Thanks alot for clear inputs.

  • edureka!
    December 8, 2020


  • edureka!
    December 8, 2020


  • edureka!
    December 8, 2020

    00:02:57 : ( 1) What is Data Science

    00:04:40 : ( 2) What are the important skills to have in Python with regard to data analysis (pandas, numpy)

    00:06:23 : ( 3) What is Selection Bias (selection effect, distortion, randomized selection, non-stratified sample)

    00:09:18 : ( 4) Difference b/w long and wide format data

    00:11:00 : ( 5) What is a Normal Distribution (sym. Bell curve, (Std normal distribution: mean 0, Std deviation 1); Central Limit Th, law of large numbers…)

    00:14:30 : ( 6) What is the goal of A/B Testing

    00:17:08 : ( 7) What do u understand by statistica power of Sensitivity and how do you calc it (Confusion matrix, Precision, Specificity, Recall, F-1 Score,

    00:22:31 : ( 8) Differences b/w overfitting and underfitting (generalization as the baseline, below baseline, above baseline)

    00:26:01 : ( 9) Python or R, which would u prefer for text analytics (R packages: tm, Python packages: pandas, numpy, nltk)

    00:27:18 : (10) How does data cleansing plays a vital role in analysis

    00:29:22 : (11) Differentiate b/w univariate, bovariate and multi variate analysis

    00:30:44 : (12) What is cluster sampling (Systematic sampling) (used when it is diffult to study the whole pipulation spread across wide areas)

    00:32:21 : (13) What is systemtic sampling

    00:32:44 : (14) What is an eigen value and eigen vectors (linear combination of variables, for reducing the variables / dimensionality reduction .. PCA basis)

    00:35:38 : (15) Can u site some examples where a false positive is important than a false negative

    00:38:30 : (16) Can u site some examples where a false negative is important than a false positive

    00:40:11 : (17) Cite cases when both FP / FN are important

    00:41:13 : (18) Difference between Test set and validation Set (Valiation step, k-fold Cross validation, tuning params)

    00:44:22 : (19) What is Cross validation (Training on varios subsets of data)

    00:46:10 : (20) What is ML

    00:47:08 : (21) What is Supervised learning (Egs: Support vectormachines , regression, )

    00:48:20 : (22) What is unSupervised learning

    00:49:10 : (23) What are various classification algos (Various classifiers are Linear ; Deccision Trees ; SVM ; Neural networks ; Kernel Estimation ; Quadratic)

    00:50:29 : (24) What is logistic regression. State an example when used this (Used for binary classification)

    00:51:47 : (25) What are recommendater systems

    00:54:40 : (26) What is a linear regression (continuous regression, root mean square error)

    00:58:26 : (27) What is collaborative filtering (user based / item based recommendation engines)

    00:59:37 : (28) How can outlier values be treated (removing mean +/- 3Std deviation;)

    01:02:36 : (29) Steps involved in Analytics project( Problem statement ; transformation and visualzation, correlations ; )

    01:04:52 : (30) How to u treat missing data during analysis (finding average and replacing, mean and )

    01:06:47 : (31) How will u define the number of clusters in a clustering algo (K-mean clustering; elbow curve creation)

    01:09:39 : (32) In any 15m interval there is 20% prob that u ll see atleast 1 shootin star. What is the probability to see that in an hour ???

    01:12:52 : (33) How do u generate a random number between 1-7 with only a die

    01:16:40 : (34) A couple has two kids and atleast one of them is a girl, what is the probability that they have two girls

    01:18:06 : (35) a jar has 1000 coins, of which 999 are fair and 1 is double headed. Pick a coin at random and toss it 10 times. Given all are heads, what is pro next is also head

  • edureka!
    December 8, 2020

    Good for quick refresher.

  • edureka!
    December 8, 2020

    Thanks! Very helpful for my upcoming interview.

  • edureka!
    December 8, 2020

    Great resource, while preparing for my internship 🙂

  • edureka!
    December 8, 2020

    Thank you sir very clear concepts

  • edureka!
    December 8, 2020

    Data Analytics Question 24:46

  • edureka!
    December 8, 2020


  • edureka!
    December 8, 2020

    Thanks a lot for your lectures. They are very clear

  • edureka!
    December 8, 2020

    1:17:30…Question number 34. The answer 1/3 is wrong, the actual answer is 1/2 because the you have considered BG and GB as two different cases but this problem requires Combination not Permutation. We are only interested in knowing if they have one boy and a girl, we are not interested in knowing weather the first child is Boy and the second is a Girl and vice versa.

  • edureka!
    December 8, 2020

    Few pointers, for first problem it is actually a poisson distribution, so with a mean (0.20 x 4 = 0.8), so probability of not seeing any star is e^(-0.8), and the probability to the contrary is 1-e^(-0.8) ~ 0.55

  • edureka!
    December 8, 2020

    I follow your videos a lot so plz share a video of BASE SAS interview question and answers….

  • edureka!
    December 8, 2020

    Great teacher, kept me engaged!

  • edureka!
    December 8, 2020

    Thank You very much! I found this video to be like a one stop shop for DS interview questions. Very well explained as well.

  • edureka!
    December 8, 2020

    very well video, thanks to dedicate your time teaching us.

  • edureka!
    December 8, 2020

    Great collection of questions and great answers.

  • edureka!
    December 8, 2020

    thank you! this was of great help!

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