Wednesday, September 21, 2022

How To Prepare For A Data Science Interview

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How to prepare for a Data Scientist Interview

Interviewing for a new job can be intimidating. Potential hires are expected to know about the open position and their field of interest and convince the interview panel that theyre the right fit for the open role. For many data scientists, there may be the added challenge of live coding problems and technical questions.

Its a competitive process. The average job opening receives hundreds of applicants, but only about 2% make it to the interview stage, according to a Glassdoor report on HR and recruiting .

But the competition can be worth it. The median annual wage for data scientists was $98,230 as of May 2020, according to the Bureau of Labor Statistics. Thats more than double the median annual salary for all occupations.

So, how do prospective hires move from one of hundreds of applicants to get an offer? Key factors include where you live and your level of experience. As you prepare for your data science interview, consider how best you can prove your skills. Take note that the more experience you have, the more leverage you may have to negotiate and share examples of how you put your skillset to use in real-world settings.

Navigate the resources below to become familiar with important skills, practice interview questions and more.

Last But Not Least Go With Your Gut And Go To Interviews Only If Youre Truly Interested In That Particular Job

Becoming a data scientist is not a competition. So, before you send out your application to any and all Fortune 500 companies, think about whether a data science position there would give you a sense of achievement and satisfaction. If the answer is yes, go for it! Sooner or later, success will follow.

Enthusiastic to explore more data scientist interview questions? Follow the link to our comprehensive article Data Science Interview Questions And Answers. In case you feel that you lack some of the fundamental skills required for the job, check out the all-around 365 Data Science Training. If you arent sure whether you want to turn your interest in data science into a full-fledged career, we also offer a free preview version of the Data Science Program. Youll receive 12 hours of beginner to advanced content for free. Its a great way to see if the program fits your goals and needs.

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What Is The Difference Between The Test Set And Validation Set

Test set : Test set is a set of examples used only to evaluate the performance of a fully specified classifier. In simple words, it is used to fit the parameters. It is used to test the data which is passed as input to your model.

Validation set : Validation set is a set of examples used to tune the parameters of a classifier. In simple words, it is used to tune the parameters. Validation set is used to validate the output which is produced by your model.

Kernel Trick

A Kernel Trick is a method where a linear classifier is used to solve non-linear problems. In other words, it is a method where a non-linear object is projected to a higher dimensional space to make it easier to categorize where the data would be divided linearly by a plane.

Lets understand it better,

Lets define a Kernel function K as xi and xj as just being the dot product.

K = xi . xj = xTixj

If every data point is mapped into the high-dimensional space via some transformation

->

K = xTixj

Box Plot and Histograms

Box Plot and Histogram are types of charts that represent numerical data graphically. It is an easier way to visualize data. It makes it easier to compare characteristics of data between categories.

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Data Science Interview: Top Data Science Interview Questions And Answers

Applying for a data science job? Preparing for the interview can be nerve-wracking. Luckily, a data science job interview is not as bad as you may think. While it may seem intimidating, proper preparation will help eliminate your pre-interview jitters.

Not sure where to start? In this data science interview guide, well teach you which topics you need to study and what some of the most common types of questions are during the interview process

Give One Example Where Both False Positives And False Negatives Are Important Equally

Step

In Banking fields: Lending loans are the main sources of income to the banks. But if the repayment rate isnt good, then there is a risk of huge losses instead of any profits. So giving out loans to customers is a gamble as banks cant risk losing good customers but at the same time, they cant afford to acquire bad customers. This case is a classic example of equal importance in false positive and false negative scenarios.

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Probability & Statistics Interview

This interview will test your understanding of applied statistics and probability .

Example Questions

  • How would you describe a p-value / confidence interval to a 10 year old?
  • What is the probability of a person being infected of a disease given that the test is positive and the probability of having a disease is 0.1%?
  • What is the mean and variance of a binomial distribution?
  • How to simulate a biased coin using a fair coin

Top Tips

  • You may not need to know the exact formula of calculating a specific metric. However, you should know what the variables are and the direction in which it moves the output metric .
  • For probability questions, you can often answer the question by drawing out the permutations and adding up the probabilities. It is okay if you dont get the exact right answer , especially if your approach is right.

Resources

  • Describe the Random Forest classifier.
  • When will you use L1 vs. L2 regularization?

Top Tips

  • Give a concise definition in 2 to 3 sentences. Provide one or two examples to convince the interviewer that you have both the theoretical knowledge and experience.
  • If necessary, provide some common solutions to the problem.

Resources

  • The Ultimate Guide to Acing Machine Learning Interviews
  • 4 kinds of machine learning problems in data science interviews
  • Implementation of machine learning algorithms from scratch

Preparing For The Interview

Jennifer Raimone, director of career and student support for Metis, recommends getting into the habit of finding out more about the company during the initial phone screen.

“It’s surprising how many job seekers are afraid to ask what the interview process entails, but understandably so since we aren’t really taught how to navigate this process,” she said.

Here are some good questions to ask during the initial phone screen:

  • What is the timeline for filling the role?
  • Is the position new or backfilled?
  • What does the interview process entail?
  • What is your preferred communication style for follow-up and status updates?

Asking about their timeline helps you schedule your time better so you are not overworked and can be your best self. If the position is backfilled, you can think about what skills to highlight. Asking the steps in the interview process helps you prepare technically, and understanding their communication style can help you to manage your expectations.

Sean Downes, Ph.D. director at the Pasayten Institute, recommends brainstorming the kinds of problems that the organization might face and charting out possible concrete problems with concrete solutions. For example, a social networking company might be seeking ways to curate the best clusters on a graph a retail company might want help setting up or improving a recommendation system.

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Why Do You Want To Work At This Company As A Data Scientist

This question allows you to describe what interests you in data science, the specific job listing and the company as a whole. You can demonstrate your passion for technology and analytics or your interest in utilizing big data to achieve company goals. You can also state that you are specifically interested in the way that particular company gathers and analyzes large amounts of data.

Example:I have a degree in computer science and a passion for solving issues by processing and analyzing data. Thats why I am looking for a forward-thinking and data-driven company that has a rich history of using data to improve the quality of its products. Im eager to serve in a position that allows me to achieve my career goals while excelling at work Im passionate about.

Read more:Interview Question: Why Do You Want to Work Here?

What Do You Understand About The True

How to prepare for your Microsoft Interview: Data Science

The true-positive rate is the ratio between the number of true positives and the sum of the number of true positives and false negatives . The false positive rate is the ratio between the number of false positives and the sum of the number of false positives and true negatives.

TPR = TP/TP+FN

FPR = FP/FP+TN

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How Would You Approach A Dataset Thats Missing More Than 30 Percent Of Its Values

The approach will depend on the size of the dataset. If it is a large dataset, then the quickest method would be to simply remove the rows containing the missing values. Since the dataset is large, this wont affect the ability of the model to produce results.

If the dataset is small, then it is not practical to simply eliminate the values. In that case, it is better to calculate the mean or mode of that particular feature and input that value where there are missing entries.

Another approach would be to use a machine learning algorithm to predict the missing values. This can yield accurate results unless there are entries with a very high variance from the rest of the dataset.

Data Science Interview Preparation: 7 Tips To Succeed

In this article

Theres never been a better time to launch a career in data science. The number of data science jobs is estimated to grow by 30% this decade, and its also one of the most lucrative tech roles, with median salaries for data scientists being around a hundred thousand dollars a year.

But before you can start earning that six-figure salary, youre going to need to ace your data science interview. And this kind of interview is more than just a show of your technical skills.

Weve put together this guide, so that youll know what to expect from your data science interview, and how to prepare accordingly. Keep reading to learn how to ace the interview and land the job of your dreams.

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Research The Role And Identify Your Fit

Read the entire job description thoroughly, and consider what the responsibility and tasks youâll be performing are. From there, you can gauge the soft and technical skills that youâll need for the job. To really nail the interview and prepare properly, youâll need to have a clear idea of what the role is and what the requirements will be.

Look up what the interviewer does at the company in most cases, the main interviewer will be an immediate â or close â supervisor for the position you are applying to. Researching them, their role, and critically thinking about how your roles will interact will be helpful during the interview .

With a clear idea of what the role and job description are, youâll be able to predict which topics will be covered in the interview, and better determine which topics to focus on when preparing. If you havenât performed a relevant task since since you last left school, you may want to brush up on it before the interview so you know how to discuss it with confidence.

Itâs also important to research industry, company, and technical terminology so you sound informed, can follow along, and can engage throughout the interview.

How Did Your Previous Work Experiences Prepare You For A Role As A Data Scientist

4 Key Aspects for Preparing Well for Data Science Interview

The diverse skill set required for this position may require you to demonstrate relevant experience in both technical skills and interpersonal communication. The best way to describe how your previous experiences prepared you for a role in data science is by using the STAR interview response technique by describing a situation, talking about what your task was in that particular context, discuss the actions you took to complete the task, as well as the results of your actions.

Example:My previous job was for a tech company where I gathered customer feedback on their applications from multiple platforms and filed monthly reports to management, outlining my findings. My main task was to find common issues that applied to most customers, no matter what device they were using to access the companys applications.

To most effectively collect the data, I created an algorithm that gathered all customer feedback and organized it based on certain keywords included in customer entries. I managed to streamline the process of gathering and analyzing these large amounts of data, making it easier to group the information and draw relevant conclusions from it.

Read more: How to Use the STAR Interview Response Technique

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Questions To Expect In Your Data Science Job Interview

Matt Przybyla is a senior data scientist with Favor.

Matt Przybyla is a senior data scientist with Favor.

Rather than list incredibly specific questions that you can quickly memorize, I wanted to highlight some more conceptual questions that may seem trivial at first but that can end up being a key to landing a data science job. Youll notice that these questions arent purely technical, but rather show how you approach your work as a data scientist. Everyone can study SQL, Python, R and so on, but what sets you apart is how you work with data science projects and problems and the people who are also dealing with those same projects and problems alongside you. Here, I will be discussing some more behavioral data science interview questions. Perhaps you will encounter some of these questions and tasks in the future or ask them yourself as the interviewer.

Q81 Describe In Brief Any Type Of Ensemble Learning

Ensemble learning has many types but two more popular ensemble learning techniques are mentioned below.

Bagging

Bagging tries to implement similar learners on small sample populations and then takes a mean of all the predictions. In generalised bagging, you can use different learners on different population. As you expect this helps us to reduce the variance error.

Boosting

Boosting is an iterative technique which adjusts the weight of an observation based on the last classification. If an observation was classified incorrectly, it tries to increase the weight of this observation and vice versa. Boosting in general decreases the bias error and builds strong predictive models. However, they may over fit on the training data.

Q82. What is a Random Forest? How does it work?

Random forest is a versatile machine learning method capable of performing both regression and classification tasks. It is also used for dimensionality reduction, treats missing values, outlier values. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.

In Random Forest, we grow multiple trees as opposed to a single tree. To classify a new object based on attributes, each tree gives a classification. The forest chooses the classification having the most votes and in case of regression, it takes the average of outputs by different trees.

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Get Some Idea About What To Expect

Normally, data science interviews consist of one to three screening conversations followed by all-day onsite. Because of this, you might have to speak to several individuals before heading into the office. And, if this is your first time attending an all-day onsite, prepare yourself mentally for numerous questions regarding your technical talents as well as your capacity to interact with others.

What Is Root Cause Analysis

How I Prepare For My Data Science Interviews (From a Data Scientist)

Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from recurring.

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Can You Avoid Overfitting Your Model If Yes Then How

Yes, it is possible to overfit data models. The following techniques can be used for that purpose.

  • Bring more data into the dataset being studied so that it becomes easier to parse the relationships between input and output variables.
  • Use feature selection to identify key features or parameters to be studied.
  • Employ regularization techniques, which reduce the amount of variance in the results that a data model produces.
  • In rare cases, some noisy data is added to datasets to make them more stable. This is known as data augmentation.

Data Science Interview Preparation: Top Tips For How To Nail It

Youâve refined your resume, sent out an application, and got the call back – now you just need to nail your interview. To put your best foot forward, youâll want to properly prepare for the interview so you can make a great first impression.

To help you do just that, we outline how to prepare for your data science interview by covering the following:

  • The main topics covered in a data science interview
  • Data scientist roles and responsibilities: what to expect
  • How to prepare for your data science interview
  • Common questions youâll be asked in a data science job interview

Before we dive into the best strategies and things to consider when preparing for your interview, weâll cover some of the things that are likely to come up in your interview.

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