How To Prepare For Data Science Interview

List Of Data Science Interview Questions From Top Companies

To give you an idea of some other questions that may come up in an interview, we compiled a list of data science interview questions from some of the top tech companies.

• Whats the difference between logistic regression and support vector machines? Whats an example of a situation where you would use one over the other?
• What is the interpretation of an ROC area under the curve as an integral?
• A disc is spinning on a spindle and you dont know the direction in which way the disc is spinning. You are provided with a set of pins. How will you use the pins to describe in which way the disc is spinning?
• What would you do if removing missing values from a dataset causes bias?
• What kind of metrics would you want to consider when solving questions around a products health, growth, or engagement?
• What metrics would you assess when trying to solve business problems related to our product?
• How would you tell if a product is performing well or not?
• How do you detect if a new observation is an outlier? What is a bias-variance trade-off?
• Discuss how to randomly select a sample from a product user population.
• Explain the steps for data wrangling and cleaning before applying machine learning algorithms.
• How would you deal with unbalanced binary classification?
• What is the difference between good and bad data visualization?
• How do you find percentiles? Write the code for it.
• Create a function that checks if a word is a palindrome.

Previous

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.

Should I Sign An Invention Disclosure Agreement

Your prospective employer might ask you to disclose previous inventions codes, algorithms, software programs and models you may have written or contributed to and they may ask you to sign an agreement that gives full or partial ownership to them for any inventions you create during the time you are employed with them.

In such instances, you might want to consult with an employment attorney, your academic counselor, a professor or mentor for advice. These people might also be able to help you fully understand your legal rights when it comes to signing pre-invention agreements and property and inventions agreements that are conditions for hire.

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Machine Learning Applications And Explanations

While preparing for a Data Scientist position, one should have a deep understanding of all machine learning models and learn how to explain the model with hands made diagrams, the purpose of the model, and also its advantage and disadvantage for a particular activity or task. Some of the main machine learning algorithms are linear regression, logistic regression, random forest, naïve bayes, decision tree, support vector machines, etc.

Q75 What Is Collaborative Filtering

The process of filtering used by most of the recommender systems to find patterns or information by collaborating viewpoints, various data sources and multiple agents.

An example of collaborative filtering can be to predict the rating of a particular user based on his/her ratings for other movies and others ratings for all movies. This concept is widely used in recommending movies in IMDB, Netflix & BookMyShow, product recommenders in e-commerce sites like Amazon, eBay & Flipkart, YouTube video recommendations and game recommendations in Xbox.

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Give Your Inbox A Nudge

Studies show that nudges towards your goals are great resources towards building positive habits. Just like how companies like Facebook send you notifications to get you addicted to Instagram, you can also do that with studying to help you achieve your goals!

If you sign up for Interview Query, well send you one question per week in your email for you to practice.

About Best Approaches Strategies And Tips To Get Hired

It doesnt matter if you are new to data science or have prior experience. Job interviews can bring anxiety to anyone. Each and every job interview is a different experience. While it is not possible to anticipate your interview questions or to guess the expectations of an interviewer. There are definitely some things that will ensure you are well prepared.

When I say preparing for an interview. Idont mean the night before your interview. It is about the journey that takes you to the interview. It is about the long preparation that will ensure everything goes well on the day of the interview.

In this article, I am going to share important tips that will increase your chances of success in a data science job interview.

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How Can You Avoid Overfitting Your Model

Overfitting refers to a model that is only set for a very small amount of data and ignores the bigger picture. There are three main methods to avoid overfitting:

What Are The Differences Between Supervised And Unsupervised Learning

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Be Prepared To Discuss Salary

If you find salary discussions awkward or discomforting, youâll want to practice your responses, or at the very least have a firm idea of what your expectations are. Itâs common for salary expectations to come up in an interview, and you should be ready for this to come up at any time sometimes they will come up in the first interview, and other times it wonât come up until the final interview.

It is best to use a salary range as opposed to a single number, and you should have a salary in mind going into it. This shouldnât just be an arbitrary amount that you expect, but a value that you can justify based on the requirements and responsibilities of the role, and the expertise and experience you bring to it. This means that your salary range will likely â and should â change depending on the role youâre interviewing for.

There are a number of services that are helpful in identifying a reasonable salary range for different jobs in various industries.

• Salary.com

In some cases, you wonât have enough information or wonât feel comfortable listing a salary range. If you donât want to, itâs okay to tell them you donât feel confident listing a salary. This is especially true if you donât have a lot of information about the requirements of the role, such as the weekly hours, vacation time, benefits, and more. The base salary doesnât always tell the whole story, so make sure to ask questions when appropriate.

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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What Do We Need To Know Beforehand

The most common mistake applicants make in data scientist interviews is that they are either over-prepared, or under-prepared.

Its hard to tell how much you need to prepare for an interview. The amount of preparation you need depends on the job and position you are applying for.

But you can make smart assumptions on what you need to prepare and how you should present your work.

For example, you know that you need to prove that you have the programming skills needed for the job.

That means you need to present a portfolio as evidence you cant show up to an interview empty-handed. But at the same time, your employers arent going to look through every single project youve done when they are going to meet twenty other candidates after you.

A good way to present your portfolio is to handpick your best projects that relates to job they need you to fulfill.

It is not how much you prepare that matters, but what you prepare before the interview.

Here are things you must prepare and know about before you enter your interview.

Q64 Explain Svm Algorithm In Detail

SVM stands for support vector machine, it is a supervised machine learning algorithm which can be used for both Regression and Classification. If you have n features in your training data set, SVM tries to plot it in n-dimensional space with the value of each feature being the value of a particular coordinate. SVM uses hyperplanes to separate out different classes based on the provided kernel function.

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What Helped Me Interview Successfully With Fang As Well As Unicorns

Preparing for data scientist/data analyst interviews is a time-consuming activity, but the prepping time can be significantly decreased if you have prior experience in the field and/or have a list of the right resources for each topic that potentially will show up in the interviews so that you can focus your efforts. Even though data scientists and data analysts are different career paths in the data world , there are a lot of overlaps when it comes to topics covered in interviews.

In myprevious post, the first part of my interview prep guide, I outlined the commonly tested areas that appear in most data-related job interviews . In this post, Im going to zoom in and focus specifically on the interviews for data scientists and data analysts I have been interviewed by dozens of companies in the Valley for those roles and have compiled a list of useful resources along the way. If you are interviewing for a data scientist/data analyst position, make sure you brush up on these topics in addition to the ones I outlined in my previous post.

Q110 What Are The Variants Of Back Propagation

• Stochastic Gradient Descent: We use only a single training example for calculation of gradient and update parameters.

• Batch Gradient Descent: We calculate the gradient for the whole dataset and perform the update at each iteration.

• Mini-batch Gradient Descent: Its one of the most popular optimization algorithms. Its a variant of Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is used.

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How To Prepare For A Data Scientist Interview

Are you struggling to get into data science and wondering what the interviews will be like? You might even know data science. You might also know about Data Science tools and techniques but still getting rejected in the interviews. It is essential to brush up your skillset to become a Data Scientist in demand. These are some times where many of us are hoping to change or find some job. Interview preparation has become an important step to land a good job. Whats more, interviews are a serious thing for everybody. Uncertainty, randomness, and human blunders make an interview damn unnerving. Preparing for an interview is the only way to limit your misfortunes during an interview.

Heres the step by step guide for Data Science Interview Process

The Interview process begins directly from the point you begin investigating the various job positions that allure you. Furthermore, it goes up to the stage of in-person interviews.

Remember that this is a crucial interview procedure. You probably wont need to experience every single step in your interview procedure.

• Comprehend and follow the Different Roles, Skills and Interviews
• Update your Resume and Start Applying!
• Telephonic Screening

• AI Engineer
• PC Vision Engineer

You need to have great correspondence and critical thinking skills. You need not know Python and technicals skills.

A data architect will probably be tested on his/her programming skills. Get prepared as per the companys expectations.

Telephonic Interview

How Do You Usually Identify Outliers Within A Data Set

Successful data scientists need to be able to use their theoretical knowledge to produce practical, real-world outcomes and conclusions. This question is your opportunity to showcase your analytical skills and the ways you use them to determine outliers and other data impacts in a variety of contexts. For an effective answer, use a specific professional experience that best illustrates your knowledge.

Example:Typically, I use practical methods and first analyze the raw data to understand the general trends. I can then determine which model will enable me to detect any outliers. For example, I recently compiled data of all professional basketball players in the state based on their points-per-game average. I managed to successfully identify outliers by creating histograms for each player and used statistical techniques such as quartiles and inner and outer fences to check the accuracy of my findings.

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Tell Me About A Time When You Had To Clean And Organize A Big Data Set

Studies have shown that Data Scientists spend most of their time on data preparation, as opposed to data mining or modeling. So if you have any experience as a Data Scientist, it is almost certain that you have experience cleaning and organizing a big data set. It is also true that this is a task that few people really enjoy. But data cleaning is also one of the most important steps for any company. So you should take the hiring manager through the process you follow in data preparation: removing duplicate observations, fixing structural errors, filtering outliers, tackling missing data, and data validation.

• Tell me about a data project you have worked on where you encountered a challenging problem. How did you respond?
• Have you gone above and beyond the call of duty? If so, how?
• Tell me about a time you failed and what you have learned from it.
• How have you used data to elevate the experience of a customer or stakeholder?
• Provide an example of a goal you reached and tell me how you achieved it.
• Provide an example of a goal you did not meet and how you handled it.
• How did you handle meeting a tight deadline?
• Tell me about a time when you resolved a conflict.

Q60 What Is Unsupervised Learning

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses.

Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent Variable Models

E.g. In the same example, a fruit clustering will categorize as fruits with soft skin and lots of dimples, fruits with shiny hard skin and elongated yellow fruits.

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Describe A Few Different Types Of Functions In Machine Learning

The three most common functions you will use in data science and machine learning are loss functions, activation functions, and cost functions. A loss function is used to measure a true value and how far off the estimated value is from it. Loss functions are used in non-linear algorithms. They are also key in measuring the learning rate of a machine learning model.

An activation function is used in an artificial neural network. These are used to help determine whether a neuron should fire to another neuron, allowing the neural network to learn complex patterns.

A cost function can be found in a linear regression model. A cost function is used to determine the effectiveness of models. In a less effective linear model, cost functions help identify errors between predicted outcomes and actual outcomes.