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

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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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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

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Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, 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.”

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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: http://bit.ly/37q65Oc

Thank u very much Sir..

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!

Thanks alot for clear inputs.

Thanks

GREAT VID !

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

Good for quick refresher.

Thanks! Very helpful for my upcoming interview.

Great resource, while preparing for my internship 🙂

Thank you sir very clear concepts

Data Analytics Question 24:46

🗽

Thanks a lot for your lectures. They are very clear

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.

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

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

Great teacher, kept me engaged!

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

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

Great collection of questions and great answers.

thank you! this was of great help!