Skip to main content

20 Must know Data Science Interview Questions by kdnuggets

The Most important questions which is generally asked by the technical panel :

1. Explain what regularization is and why it is useful.
2. Which data scientists do you admire most? which startups?
3. How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression.
4. Explain what precision and recall are. How do they relate to the ROC curve?
5. How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything?
6. What is root cause analysis?
7. Are you familiar with pricing optimization, price elasticity, inventory management, competitive intelligence? Give examples.
8. What is statistical power?
9. Explain what resampling methods are and why they are useful. Also explain their limitations.
10. Is it better to have too many false positives, or too many false negatives? Explain.
11. What is selection bias, why is it important and how can you avoid it?
12. Give an example of how you would use experimental design to answer a question about user behavior.
13. What is the difference between "long" and "wide" format data?
14. What method do you use to determine whether the statistics published in an article (e.g. newspaper) are either wrong or presented to support the author's point of view, rather than correct, comprehensive factual information on a specific subject?
15. Explain Edward Tufte's concept of "chart junk."
16. How would you screen for outliers and what should you do if you find one?
17. How would you use either the extreme value theory, Monte Carlo simulations or mathematical statistics (or anything else) to correctly estimate the chance of a very rare event?
18. What is a recommendation engine? How does it work?
19. Explain what a false positive and a false negative are. Why is it important to differentiate these from each other?
20. Which tools do you use for visualization? What do you think of Tableau? R? SAS? (for graphs). How to efficiently represent 5 dimension in a chart (or in a video)?

Answers from kdnuggets : https://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html

Happy Learning...!!

Comments

Popular posts from this blog

Deep Learning Interview Questions - Part 1

Q1. What do you mean by Deep Learning?  Deep Learning  is nothing but a paradigm of machine learning which has shown incredible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the human brain. Q2. What is the difference between machine learning and deep learning? Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning can be categorised in the following three categories. Supervised machine learning, Unsupervised machine learning, Reinforcement learning Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Q3. What, in your opinion, is the reason for the popularity of Deep Learning in recent times? Now although Deep Learning has been around for many years, the major breakthroughs from these te...

How to deal with missing values in data cleaning

The data you inherit for analysis will come from multiple sources and would have been pulled adhoc. So this data will not be immediately ready for you to run any kind of model on. One of the most common issues you will have to deal with is missing values in the dataset. There are many reasons why values might be missing - intentional, user did not fill up, online forms broken, accidentally deleted, legacy issues etc.  Either way you will need to fix this problem. There are 3 ways to do this - either you will ignore the missing values, delete the missing value rows or fill the missing values with an approximation. Its easiest to just drop the missing observations but you need to very careful before you do that, because the absence of a value might actually be conveying some information about the data pattern. If you decide to drop missing values : df_no_missing = df.dropna() will drop any rows with any value missing. Even if some values are available in a row it will still get dropp...

Mathematics & Statistics for Data Science

Converting raw and quantitative data into organized and informative information needs a lot of brain power and understanding. It is true that everyone can’t be Aryabhatta but, you can be hardworking, focused and dedicated. So, it is time to show your dedication and hard work for learning maths and statistics for data science. Mathematics and Statistics are two of the most important concepts of Data Science. Data Science revolves around these two fields and draws their concepts to operate on the data. Today, we will explore the various concepts that build up data science and their practical usages in this field.Data Science has become a trending technology in the world today. In order to learn data science, you must reinforce your knowledge of mathematics and statistics. So let’s first explore how much maths is required for data science – Math for Data Science Mathematics has created an impact on every discipline. The magnitude of the usage of mathematics varies according to the ...