{"id":7454,"date":"2016-01-31T10:31:23","date_gmt":"2016-01-31T05:01:23","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=7454"},"modified":"2016-09-04T11:31:05","modified_gmt":"2016-09-04T06:01:05","slug":"data-science-job-interview-types-sample-questions-preparation-strategies","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/data-science-job-interview-types-sample-questions-preparation-strategies\/","title":{"rendered":"7 Data Science Job Interview Types, Sample Questions, and Preparation Strategies"},"content":{"rendered":"
\n
\"Data<\/a>

Data Science Job Interview Types<\/p><\/div>\n

Are you preparing for a data science job interview? To help you,\u00a0in this article I\u00a0will explore some of the most common techniques used by data scientists to select their future colleagues. Additionally, I will also share many sample questions for data science job interviews and suggest a few strategies to prepare. Moreover, please post your comments, questions, and challenges in the discussion section at the bottom. I will be more than happy to help.<\/p>\n

Data science job interviews and selection techniques can be categorized into the following 7 classes with 3 levels.<\/p>\n\n\n\n\n\n\n\n\n\n\n
Data Science Job Interview Type<\/span><\/strong><\/td>\nLevel and Significance for Selection<\/span><\/strong><\/td>\n<\/tr>\n
1. Puzzles & Riddles<\/td>\nLevel 1 :<\/strong>\u00a0These are usually the\u00a0warm-up interview questions to assess logical and analytical aptitude\u00a0of the candidate. You will rarely\u00a0get a job offer after clearing only this level.<\/td>\n<\/tr>\n
2. Quick Math<\/td>\n<\/tr>\n
3. Guesstimation<\/td>\n<\/tr>\n
4. Programming & Data Preparation Challenges<\/td>\nLevel 2<\/strong>\u00a0: Things get serious from this stage onward since these are part of daily activities for a data scientist. Some entry level candidates may get a job offer after clearing this level.<\/td>\n<\/tr>\n
5. Statistics & Machine Learning Questions<\/td>\n<\/tr>\n
6.\u00a0Case Study Problems \/ Problem Solving Experience<\/td>\nFinal level 3 <\/strong>: This is where the hiring authority is seriously considering you for the position. You will mostly\u00a0secure an offer after clearing this level.<\/td>\n<\/tr>\n
7. Analyze This \/ Take Home Analysis<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

If you are preparing for a data science job interview it is a good idea for you to know what you might face in these interviews. I will discuss details of these categories of interviews later in the\u00a0article. I will also introduce a few sample questions from each of these category, and suggest ways to prepare for these interviews. Please share your solution approaches and thoughts for these sample questions in the discussion section of this article.<\/p>\n

However, before we explore the selection criteria for data science job interviews & screening process,\u00a0let us learn about the group of people who have mastered the art of right selection & screening, and they are..<\/p>\n

Nigerian Scamsters<\/span><\/h2>\n
\"Data<\/a>

Data Science Job Interview – by Roopam<\/p><\/div>\n

I am sure at some point of time you have received a Nigerian scam mail\u00a0in your Gmail or Yahoo mail box. These mails are typically from an unknown person desperate to transfer a huge sum of money to your bank account because of turmoil in some African country. The senders of these mails often\u00a0portray themselves as a high ranking banker or the offspring of some rich person. These emails always\u00a0have laughable sentences like these<\/p>\n

“ …Permit me to inform you of my desire of going into business relationship with you …please feel free to contact ,me via this email address\u00a0wumi1000abdul@yahoo.com<\/em>” – source for some sample scam mails<\/a><\/p>\n

Most of us brush these mails off as ridiculous because of their\u00a0ludicrous language and content. However, the\u00a0success of these scam mails can be assessed\u00a0by the following statistics from the report by\u00a0ultrascan-agi<\/a>. In\u00a02013 Nigerian scams have duped people with close to 13 billion dollars in financial losses. Moreover between\u00a02006-13 the losses were\u00a0roughly 82 billion dollars. Now that’s a lot of money and am sure these scam mails have lured\u00a0a lot of victims.\u00a0Scamsters are clearly\u00a0an extremely clever lot, and one wonders why they would write such laughable mails to catch victims.<\/p>\n

Nigerian scamsters are indeed a clever bunch. They use these mails\u00a0as\u00a0an initial screening process for the intriguing modus operandi<\/em> of Nigerian scams which eventually requires the victims to transfer money into an unknown bank account that belongs to the scamsters. They are essentially applying this first level of filter to identify people stupid enough to go the distance in this scam. Someone who can’t sight the ridiculous\u00a0language and absurd content of these mails as suspicious is certainly a great target for these scamsters. The motive of Nigerian scamsters is completely wrong and illegal but their selection process is immaculate to identify and select the right candidate who\u00a0will go the distance.<\/p>\n

Data Science Job Interview\u00a0<\/span><\/h2>\n

On some level any job interview process, including data science, is far from\u00a0perfect and is full of flaws. The idea with interviews is to measure the ability\u00a0of a candidate to run a marathon based on asking them to run a few 100 meter dashes. This is almost impossible. However as we have noticed with Nigerian scammers, they have figured out a quick yet extremely effective strategy to select the right candidates for their purpose. They could do it right because they know clearly who is the right target for them. Even in data science job interviews, the goal is to know clearly what one is looking for in a candidate and design selection procedures around this. Now we will discuss the current practices in data science job interviews.<\/p>\n

Level 1 – Data Science Job Interview<\/span><\/h2>\n

Level 1 of data science job interviews is about assessing the logical and analytical aptitude of the participant.<\/p>\n

1.1 Puzzles & Riddles<\/p>\n

1.2 Quick Math<\/p>\n

1.3 Guesstimation<\/p>\n

If typical interviews are like a 100 meter dash, these level 1\u00a0interviews tend to become a 10 meter ultra fast sprint. They have their place and importance but it is absolutely impossible to assess a candidate purely on these interviews. However, for someone preparing for a data science interview it is a good idea to brush up their skills on these questions.\u00a0I will suggest a few books later in this article to enhance your skills with puzzles and guesstimations.<\/p>\n

1.1 Puzzles & Riddles<\/span><\/h4>\n

Puzzles and riddles are an integral part of interviews at tech companies like Google and Microsoft.\u00a0In data science interviews expect a few puzzles around probability theory as mentioned in the sample questions.<\/p>\n

\n\n\n\n
Sample Questions:<\/strong><\/p>\n

1) You are at Ranthambore National Park<\/em>\u00a0where the probability of sighting a tiger\u00a0is 95% in\u00a0every\u00a0day trip i.e. 8 hours long. What is the probability that you will see a tiger in a half day trip i.e.\u00a04 hours long?\u00a0<\/em><\/p>\n

2. Can you explain the solution to Monty Hall Problem? (read the problem and solution approach at this link<\/a>)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Useful books to prepare for puzzles and riddles<\/strong><\/div>\n
1.\u00a0How Would You Move Mount Fuji?: Microsoft’s Cult of the Puzzle<\/a> – <\/span>William Poundstone<\/span><\/span><\/div>\n
2.\u00a0Are You Smart Enough to Work at Google?<\/a>–\u00a0<\/span>William Poundstone<\/span><\/span><\/div>\n
\n
3.\u00a0Professor Stewart’s Hoard of Mathematical Treasures<\/span> Paperback<\/span><\/a> \u2013\u00a0<\/span>Ian Stewart<\/span><\/span><\/div>\n<\/div>\n

1.2\u00a0Quick Math<\/span><\/h4>\n

Again, expect a few quick maths questions tossed at you during an interview. They are not tough if you have a calculator with you. However during an interview, you will have to solve them without the help\u00a0of a calculator.\u00a0What the interviewer looks for in your solution is the ball park figure and not the exact solution to the decimal level.<\/p>\n

\n\n\n\n
Sample Questions:<\/strong><\/p>\n

1. What percentage is 7 of 24?<\/p>\n

2. Is 23 times 17 greater than 450?<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

These questions are actually useful because data scientists need to check the validity of their results on a regular basis. Having these skills help you sight any glaring discrepancy in your results upfront. It’s highly embarrassing if your customer points them out to you during a presentation.<\/p>\n

1.3 Guesstimation Questions<\/span><\/h4>\n

These questions are directly borrowed from\u00a0the interviews of consulting companies like McKinsey and BCG. \u00a0Data science profiles\u00a0usually have a significant amount of work in consulting and\u00a0customer management. Hence there is a fair overlap between data science and consulting interviews.<\/p>\n\n\n\n
Sample Questions:<\/strong><\/p>\n

1. Estimate market size : How many laptops are sold in India every year?<\/p>\n

2. How many tennis balls can you fit in a Boeing 747?<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

These two questions for guesstimation on the surface may seem different but they are solved using the same approach. You will find the following books useful for such questions.<\/p>\n

Useful books to prepare for guesstimations & case based interview<\/strong><\/div>\n
1. Case In Point: Complete Case Interview Preparation<\/a>–\u00a0<\/span>Marc P Cosentino<\/span><\/span><\/div>\n
2.\u00a0Interview Math: Over 50 Problems &\u00a0Solutions for Quant Case Interview Questions<\/a> –<\/span>\u00a0Lewis C. Lin<\/span><\/span><\/div>\n

Level 2 – Data Science Job Interview<\/span><\/h2>\n

Level 2 of a data science job interview often has:<\/p>\n

2.1 Statistics and Machine learning Questions<\/p>\n

2.1 Programming and Data Preparation Challenges<\/p>\n

2.1 Statistics and Machine Learning Questions<\/span><\/h4>\n

Statistics and machine learning\u00a0are the key concepts\u00a0you need to have a good grasp of to be a good data scientist.<\/p>\n

\n\n\n\n
Sample Questions:<\/strong><\/p>\n

1.\u00a0What are\u00a0K-mean clusters? Suggest at least a couple of ways to\u00a0select the optimal value of K.<\/p>\n

2.\u00a0How are artificial neural networks (ANN) different from\u00a0logistic regression? Can you create a logistic regression model through ANN? How?<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Useful Free Ebooks to Prepare for these Interviews<\/strong><\/div>\n
1. StatSoft Textbook\u00a0<\/a><\/div>\n
2.\u00a0Basic of Statistics<\/a> –\u00a0Jarkko Isotalo<\/div>\n
3.\u00a0Pattern Recognition and Machine Learning <\/a>– Christopher M Bishop<\/div>\n

2.2 Programming & Data Preparation Challenges<\/span><\/h4>\n

Data science professionals need to have proficiency in\u00a0programming languages like R, Python, or SAS. Moreover, they need to understand different strategies to handle and pre-process data. This type of interview usually is an effort to understand the candidate’s proficiency with these skills.<\/p>\n

\n\n\n\n
Sample Questions:<\/strong><\/p>\n

1.\u00a0What is the largest data\u00a0size you have handled? What are the challenges you faced while handling that data?<\/p>\n

2. What is the command to create histogram in R?<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Personally, I am not a huge fan of asking programming questions (like question 2) in an interview. All\u00a0data science languages are scripting languages with inbuilt functions for most tasks. I don’t think data scientists need to memorize these functions. Most of the time Google is there\u00a0to help at work. For instance the answer to 2nd question is hist() – that’s a bit dull. However, you will find a few interviewers\u00a0asking such questions. You will find many such questions on this site :\u00a0R Interview Questions<\/a>. I suggest that while preparing,\u00a0focus on conceptual and logical angles of algorithms.<\/p>\n

Final Level 3 \u00a0– Data Science Job Interview<\/span><\/h2>\n

The final level of a data science job interview involves:<\/p>\n

3.1 Case Study Problems<\/p>\n

3.2 Analyze This \/ Take Home Analysis<\/p>\n

3.1 Case Study Problems<\/span><\/h4>\n

These questions are the real deal for many\u00a0data science job interviews. It makes sense since\u00a0data scientists need\u00a0to solve business problems as their primary role.<\/p>\n\n\n\n
Sample Questions:<\/strong><\/p>\n

1. A retail chain\u00a0is not happy with their return on marketing investment (ROMI); how could you help them improving (ROMI) using business analytics?<\/p>\n

2. How could you help a telecommunication company improve on their profitability through data science?<\/p>\n

3. How will\u00a0you build a credit scorecard for a bank?<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

These case study interviews often start with broad problem statements like the ones shown above. However, they get extremely detailed as the time progresses during the interview. The candidates are expected to focus more on their approach to solve the problem rather than the final solution.<\/p>\n

You will find these case studies on YOU CANalytics useful while preparing for case study problems.<\/p>\n\n\n\n\n
\"Slide4\"<\/a><\/td>\n\"Slide1\"<\/a><\/td>\n<\/tr>\n
\"Slide2\"<\/a><\/td>\n\"Slide3\"<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

3.2 Analyze This \/ Take Home Analysis<\/span><\/h4>\n

Analysis of real data is another form of selection procedure that is highly popular among data scientists.\u00a0In this form of screening process a large data set is provided to the candidate who has to analyse this data. Many times output of this analysis is a complete model e.g. logistic regression, decision tree etc. The candidate is assessed based on the presentation of her thought process,\u00a0data preparation strategy, exploratory data analysis, and model results. There are several variants of\u00a0this form of interview. In an elaborated form,\u00a0the candidate is allowed to take the data back home and is given a few days to work on the data and analysis.<\/p>\n

\n\n\n\n
Sample Questions:<\/strong><\/p>\n

1. Analyse this graph and suggest at least 3 key findings.<\/p>\n

\"Matrix<\/a><\/p>\n

2. Consider y as the dependent variable, and x1 – x4 as the independent variables in the multiple linear regression model. What are your expectations about the regression coefficients for this data? Is there something you need to be careful about while generating your regression equation?<\/p>\n

3. Attached is dataset<\/a> for a lending company. Do your analysis and report significant\u00a0factors responsible for credit defaults. Moreover, prepare a short report \/ presentation of your approach.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Sign-off Note<\/span><\/h4>\n

Please share your solution approaches and thoughts for the sample questions discussed in this article in the discussion section at the bottom. Also, feel free to ask your questions, doubts, and suggestions while preparing for a data science job interview. Would love to help.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Are you preparing for a data science job interview? To help you,\u00a0in this article I\u00a0will explore some of the most common techniques used by data scientists to select their future colleagues. Additionally, I will also share many sample questions for data science job interviews and suggest a few strategies to prepare. Moreover, please post your<\/p>\n

Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":7488,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[62,78],"tags":[],"jetpack_publicize_connections":[],"yoast_head":"\n7 Data Science Job Interview Types, Sample Questions, and Preparation Strategies – YOU CANalytics |<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ucanalytics.com\/blogs\/data-science-job-interview-types-sample-questions-preparation-strategies\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"7 Data Science Job Interview Types, Sample Questions, and Preparation Strategies – YOU CANalytics |\" \/>\n<meta property=\"og:description\" content=\"Are you preparing for a data science job interview? 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