# Data Science Interview Questions and Answers

Preparing for an interview isn’t simple there is huge uncertainty regarding the data science interview questions you will be inquired. Regardless of how much work experience or what data science certificate you have, an interviewer can throw you off with a lot of questions that you didn’t anticipate.

During a data science interview, the interviewer will pose inquiries crossing a wide range of points, requiring both strong specialized information and strong relational abilities from the interviewee. Your insights, programming, and data displaying aptitudes will be scrutinized through a variety of questions and question styles that are purposefully intended to keep you on your feet and force you to demonstrate how you operate under pressure.

Preparation is the way to progress while pursuing a career in data science, and that incorporates the interview process.

This guide contains the entirety of the data science interview questions you ought to expect while interviewing for a position as a data researcher.

We previously created a free data science interview direct, yet we despite everything felt we had more to explore. So we curated this rundown of real questions asked in a data science interview. From this rundown of data science interview questions, an interviewee ought to have the option to prepare for the extreme questions, learn what answers will positively resonate with an employer, and build up the certainty to pro the interview.

We’ve broken the interview questions for data researchers into six different categories: measurements, programming, displaying, behavior, culture, and problem-settling.

Measurements

- Programming
- General
- Big Data
- Python
- R
- SQL
- Demonstrating
- Behavioral
- Culture Fit
- Problem-Solving

Ever wonder what a data researcher really does? Look at Springboard’s comprehensive manual for data science. We’ll instruct you everything you have to think about turning into a data researcher, from what to concentrate to fundamental abilities, salary guide, and that’s only the tip of the iceberg!

## 1. Statistics Interview Questions

Factual computing is the process through which data researchers take raw data and create predictions and models. Without a serious information on measurements it is hard to prevail as a data researcher accordingly, it is likely a decent interviewer will try to probe your understanding of the topic with insights oriented data science interview questions. Be prepared to answer some essential insights questions as part of your data science interview.

Here are instances of rudimentary insights questions we’ve found:

**What is the Central Limit Theorem and for what reason is it important?**

“Assume that we are interested in assessing the average tallness among all individuals. Gathering data for every person in the world is outlandish. While we can’t acquire a stature measurement from everyone in the populace, we can at present example a few people. The inquiry currently becomes, what would we be able to state about the average stature of the entire populace given a solitary example. The Central Limit Theorem addresses this inquiry precisely.” Read more here.

**What is examining? What number of testing strategies do you know?**

“Data testing is a factual examination procedure used to choose, control and investigate a representative subset of data focuses to distinguish patterns and trends in the larger data set being inspected.” Read the full answer here.

**What is the difference between type I versus type II error?**

“A sort I error occurs when the invalid theory is true, yet is rejected. A sort II error occurs when the invalid speculation is bogus, yet erroneously neglects to be rejected.” Read the full answer here.

**What is linear regression? What do the terms p-worth, coefficient, and r-squared worth mean? What is the centrality of every one of these parts?**

A linear regression is a decent instrument for snappy predictive investigation: for instance, the price of a house relies upon a myriad of factors, for example, its size or its area. So as to see the relationship between these variables, we have to fabricate a linear regression, which predicts the line of best fit among them and can help finish up whether or not these two factors have a positive or negative relationship. Read more here and here.

**What are the presumptions required for linear regression?**

There are four major suppositions: 1. There is a linear relationship between the needy variables and the regressors, which means the model you are creating really fits the data, 2. The errors or residuals of the data are normally distributed and free from one another, 3. There is insignificant multicollinearity between explanatory variables, and 4. Homoscedasticity. This implies the variance around the regression line is the equivalent for all estimations of the predictor variable.

**What is a factual interaction?**

“Essentially, an interaction is the point at which the impact of one factor (input variable) on the needy variable (yield variable) differs among levels of another factor.” Read more here.

**What is choice bias?**

“Determination (or ‘testing’) bias occurs in a ‘functioning,’ sense when the example data that is gathered and prepared for demonstrating has characteristics that are not representative of the true, future populace of cases the model will see. That is, dynamic determination bias occurs when a subset of the data are deliberately (i.e., non-randomly) prohibited from examination.” Read more here.

**What is a case of a data set with a non-Gaussian distribution?**

“The Gaussian distribution is part of the Exponential group of distributions, however there are significantly more of them, with a similar sort of usability, as a rule, and if the person doing the machine learning has a strong grounding in insights, they can be used where appropriate.” Read more here.

**What is the Binomial Probability Formula?**

“The binomial distribution comprises of the probabilities of every one of the potential numbers of accomplishments on N trials for autonomous occasions that each have a probability of π (the Greek letter pi) of occurring.”

## 2. Programming

To test your programming skills, employers will typically include two specific data science interview questions: they’ll ask how you would solve programming problems in theory without writing out the code, and then they will also offer whiteboarding exercises for you to code on the spot. For the latter types of questions, we will provide a few examples below, but if you’re looking for in-depth practice solving coding challenges, visit HackerRank. With a “learn by doing” philosophy, there are challenges organized around core concepts commonly tested during interviews.

**2.1 General**

With which programming languages and environments are you most comfortable working?

What are some pros and cons about your favorite statistical software?

Tell me about an original algorithm you’ve created.

Describe a data science project in which you worked with a substantial programming component. What did you learn from that experience?

Do you contribute to any open-source projects?

How would you clean a data set in (insert language here)?

Tell me about the coding you did during your last project?

**2.2 Big Data**

What are two main components of the Hadoop framework?

The Hadoop Distributed File System (HDFS), MapReduce, and YARN. Read more here.

Explain how MapReduce works as simply as possible.

“MapReduce is a programming model that enables distributed processing of large data sets on compute clusters of commodity hardware. Hadoop MapReduce first performs mapping which involves splitting a large file into pieces to make another set of data.” Read more here.

How would you sort a large list of numbers?

Say you’re given a large data set. What would be your plan for dealing with outliers? How about missing values? How about transformations?

**2.3 Python**

What modules/libraries are you most familiar with? What do you like or dislike about them?

In Python, how is memory managed?

In Python, memory is managed in a private heap space. This means that all the objects and data structures will be located in a private heap. However, the programmer won’t be allowed to access this heap. Instead, the Python interpreter will handle it. At the same time, the core API will enable access to some Python tools for the programmer to start coding. The memory manager will allocate the heap space for the Python objects while the inbuilt garbage collector will recycle all the memory that’s not being used to boost available heap space. Read more here.

What are the supported data types in Python?

“Python’s built-in (or standard) data types can be grouped into several classes. Sticking to the hierarchy scheme used in the official Python documentation these are numeric types, sequences, sets and mappings.” Read more here.

What is the difference between a tuple and a list in Python?

“Apart from tuples being immutable there is also a semantic distinction that should guide their usage.”

**2.4 R**

What are the different types of sorting algorithms available in R language?

There are insertion, bubble, and selection sorting algorithms. Read more here.

What are the different data objects in R?

“R objects can store values as different core data types (referred to as modes in R jargon); these include numeric (both integer and double), character and logical.” Read more here.

What packages are you most familiar with? What do you like or dislike about them?

How do you access the element in the 2nd column and 4th row of a matrix named M?

“We can access elements of a matrix using the square bracket [ indexing method. Elements can be accessed as var[row, column].” Read more here.

What is the command used to store R objects in a file?

save (x, file=”x.Rdata”)

What is the best way to use Hadoop and R together for analysis?

“Hadoop and R complement each other quite well in terms of visualization and analytics of big data. There are four different ways of using Hadoop and R together.” Read more here.

How do you split a continuous variable into different groups/ranks in R?

Read about this here.

Write a function in R language to replace the missing value in a vector with the mean of that vector.

**2.5 SQL**

Often, SQL questions are case-based, meaning that an employer will task you with solving an SQL problem in order to test your skills from a practical standpoint. For example, you could be given a table and asked to extract relevant data, then filter and order the data as you see fit, and finally report your findings. If you do not feel ready to do this in an interview setting, Mode Analytics has a delightful introduction to using SQL that will teach you these commands through an interactive SQL environment.

What is the purpose of the group functions in SQL? Give some examples of group functions.

Group functions are necessary to get summary statistics of a data set. COUNT, MAX, MIN, AVG, SUM, and DISTINCT are all group functions.

Tell me the difference between an inner join, left join/right join, and union.

“In a Venn diagram the inner join is when both tables have a match, a left join is when there is a match in the left table and the right table is null, a right join is the opposite of a left join, and a full join is all of the data combined.” Read more here.

What does UNION do? What is the difference between UNION and UNION ALL?

“UNION removes duplicate records (where all columns in the results are the same), UNION ALL does not.” Read more here.

What is the difference between SQL and MySQL or SQL Server?

“SQL stands for Structured Query Language. It’s a standard language for accessing and manipulating databases. MySQL is a database management system, like SQL Server, Oracle, Informix, Postgres, etc.” Read more here.

If a table contains duplicate rows, does a query result display the duplicate values by default? How can you eliminate duplicate rows from a query result?

Yes. One way you can eliminate duplicate rows with the DISTINCT clause.

## 3. Modeling

Data displaying is where a data researcher provides an incentive for an organization. Turning data into predictive and noteworthy information is troublesome, discussing it to a potential employer much more so. Practice describing your previous experiences building models–what were the strategies utilized, challenges overcome, and triumphs accomplished in the process? The group of questions underneath are intended to uncover that information, just as your formal instruction of different displaying methods. On the off chance that you can’t describe the theory and suppositions related with a model you’ve utilized, it won’t have a decent impression.

Investigate the questions beneath to practice. Not the entirety of the questions will be relevant to your interview–you’re not expected to be a master all things considered. The best utilization of these questions is to re-familiarize yourself with the demonstrating strategies you’ve learned previously.

- Tell me about how you designed a model for a past employer or client.
- What are your favorite data visualization techniques?
- How would you effectively represent data with 5 dimensions?
- How is k-NN different from k-means clustering?
- k-NN, or k-nearest neighbors is a classification algorithm, where the k is an integer describing the number of neighboring data points that influence the classification of a given observation. K-means is a clustering algorithm, where the k is an integer describing the number of clusters to be created from the given data.

- How would you create a logistic regression model?
- Have you used a time series model? Do you understand cross-correlations with time lags?
- Explain the 80/20 rule, and tell me about its importance in model validation.
- “People usually tend to start with a 80-20% split (80% training set – 20% test set) and split the training set once more into a 80-20% ratio to create the validation set.”

- Explain what precision and recall are. How do they relate to the ROC curve?
- Recall describes what percentage of true positives are described as positive by the model. Precision describes what percent of positive predictions were correct. The ROC curve shows the relationship between model recall and specificity–specificity being a measure of the percent of true negatives being described as negative by the model. Recall, precision, and the ROC are measures used to identify how useful a given classification model is.

- Recall describes what percentage of true positives are described as positive by the model. Precision describes what percent of positive predictions were correct. The ROC curve shows the relationship between model recall and specificity–specificity being a measure of the percent of true negatives being described as negative by the model. Recall, precision, and the ROC are measures used to identify how useful a given classification model is.
- Explain the difference between L1 and L2 regularization methods.
- “A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term.”

- What is root cause analysis?
- “All of us dread that meeting where the boss asks ‘why is revenue down?’ The only thing worse than that question is not having any answers! There are many changes happening in your business every day, and often you will want to understand exactly what is driving a given change — especially if it is unexpected. Understanding the underlying causes of change is known as root cause analysis.”

- What are hash table collisions?
- “If the range of key values is larger than the size of our hash table, which is usually always the case, then we must account for the possibility that two different records with two different keys can hash to the same table index. There are a few different ways to resolve this issue. In hash table vernacular, this solution implemented is referred to as collision resolution.”

- What is an exact test?
- “In statistics, an exact (significance) test is a test where all assumptions, upon which the derivation of the distribution of the test statistic is based, are met as opposed to an approximate test (in which the approximation may be made as close as desired by making the sample size big enough). This will result in a significance test that will have a false rejection rate always equal to the significance level of the test. For example an exact test at significance level 5% will in the long run reject true null hypotheses exactly 5% of the time.”

- In your opinion, which is more important when designing a machine learning model: model performance or model accuracy
*.* - What is one way that you would handle an imbalanced data set that’s being used for prediction (i.e., vastly more negative classes than positive classes)?
- How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression?
- I have two models of comparable accuracy and computational performance. Which one should I choose for production and why?
- How do you deal with sparsity?
- Is it better to spend five days developing a 90-percent accurate solution or 10 days for 100-percent accuracy?
- What are some situations where a general linear model fails?
*Read about this*

- Do you think 50 small decision trees are better than a large one? Why?
*Read about this*

- When modifying an algorithm, how do you know that your changes are an improvement over not doing anything?
- Is it better to have too many false positives or too many false negatives?
- It depends on several factors

## 4. Past Behavior

Employers love behavioral questions. They reveal information about the work experience of the interviewee and about their demeanor and how that could affect the rest of the team. From these questions, an interviewer wants to see how a candidate has reacted to situations in the past, how well they can articulate what their role was, and what they learned from their experience.

There are several categories of behavioral questions you’ll be asked:

- Teamwork
- Leadership
- Conflict management
- Problem-solving
- Failure

## 5. Culture Fit

If an employer asks you a question on this list, they are trying to get a sense of who you are and how you would fit with the company. They’re trying to gauge where your interest in data science and in the hiring company come from. Take a look at these examples and think about what your best answer would be, but keep in mind that it’s important to be honest with these answers. There’s no reason to not be yourself. There are no right answers to these questions, but the best answers are communicated with confidence.