Monday, May 20, 2024

How To Crack Data Science Interview

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Q115 What Is A Boltzmann Machine

Job Interview Preparation for Data Science | Crack Data Science Interviews | Great Learning

Boltzmann machines have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimise the weights and the quantity for the given problem. The learning algorithm is very slow in networks with many layers of feature detectors. Restricted Boltzmann Machines algorithm has a single layer of feature detectors which makes it faster than the rest.

Q116. What Is Dropout and Batch Normalization?

Dropout is a technique of dropping out hidden and visible units of a network randomly to prevent overfitting of data . It doubles the number of iterations needed to converge the network.

Batch normalization is the technique to improve the performance and stability of neural networks by normalizing the inputs in every layer so that they have mean output activation of zero and standard deviation of one.

Data Science Interview Preparation Tip # 03

These questions intend to gain a better understanding of how you would respond to different workplace situations, and how you solve problems to achieve a successful outcome.

The main thing that the interviewers present you with is some sort of question that allows you to showcase how you encountered a conflict and then how you resolved that. The purpose of these questions is to let the interviewer know whether you are the best fit for their team or not.

Below given are some of the typical behavioural questions that are likely to come up in a data science interview:

  • How have you used data insights to persuade an opinion?
  • Have you ever made a mistake in a data science team project?
  • Give an example of a team conflict.
  • Describe a decision you made that wasnât popular.
  • Give an example of how you worked in a team.
  • How have you used data to elevate the customer experience?

A simple strategy to prepare and handle the data science behavioural questions is broken into the following two parts:

  • Select and refine stories
  • Implement Stories into STAR Framework

The second part is to implement the stories into a STAR technique to answer the question given. So, what is a STAR technique? STAR is how you set up a storyline in order to answer the question in a better and effective manner.

  • S – SituationFirst, start with a situation for the interviewers to understand what is the context of the storyline.
  • T – TaskLet the interviewers know about your roles and responsibilities in that storyline.
  • Data Scientist Technical Interview Questions

    Here are a few more technical interview questions for practicing for your data scientist interview:

  • What do you mean by cluster sampling and systematic sampling?
  • Describe the differences between true-positive rate and false-positive rate.
  • What is Naive Bayes? Why is it known as Naive?
  • What do you understand about the âcurse of dimensionalityâ?
  • What is cross-validation in data science?
  • What do you know about cross-validation?
  • How can you select an ideal value of K for K-means clustering?
  • What are the steps of building a random forest model?
  • What is ensemble learning?
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    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.

    What Is A/b Testing

    Cracking a Data Science In

    A/B testing is a kind of statistical hypothesis testing for randomized experiments with two variables. These variables are represented as A and B. A/B testing is used when we wish to test a new feature in a product. In the A/B test, we give users two variants of the product, and we label these variants as A and B.

    The A variant can be the product with the new feature added, and the B variant can be the product without the new feature. After users use these two products, we capture their ratings for the product.

    If the rating of product variant A is statistically and significantly higher, then the new feature is considered an improvement and useful and is accepted. Otherwise, the new feature is removed from the product.

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    What Do You Understand About Linear Regression

    Linear regression helps in understanding the linear relationship between the dependent and the independent variables. Linear regression is a supervised learning algorithm, which helps in finding the linear relationship between two variables. One is the predictor or the independent variable and the other is the response or the dependent variable. In Linear Regression, we try to understand how the dependent variable changes w.r.t the independent variable. If there is only one independent variable, then it is called simple linear regression, and if there is more than one independent variable then it is known as multiple linear regression.

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    Be Thorough With Your Data Science Resume

    The absolute basics of any interview, and especially a data science one. You should be able to explain everything listed on your resume. Anything that you could possibly reference, you should be able to speak about it.

    If youve listed an NLP project for example, and are unable to explain the details thats a MAJOR red flag for the interviewer.

    Use the day before the interview to edit and revise your resume. Cut details that are not required and add new ones if required. Think about each experience and project that you list does it add something relevant?

    That means your experience with a marketing firm as a non-technical person might not be very relevant for a data science role. You should consider keeping details like that off your resume. Mentioning it will just give the interviewer a sense that you are not clear about what you want from the job.

    Also, think of how you will go about explaining your work experience. Your account should depict your skills and how they led to progress. Consider the following statements:

    • Used LSTMs to predict the companys stock prices.
    • Used LSTMs to predict the companys stock prices with 40% more accuracy than the historical average.

    Doesnt the second statement sound way more impressive than the first?

    Make sure to make your achievements are measurable and quantifiable. This will leave a better impression on the minds of your data science interviewer.

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    Q122 What Are The Important Skills To Have In Python With Regard To Data Analysis

    The following are some of the important skills to possess which will come handy when performing data analysis using Python.

    • Good understanding of the built-in data types especially lists, dictionaries, tuples, and sets.

    • Familiarity with Scikit-learn. **Scikit-Learn Cheat Sheet**

    • Ability to write efficient list comprehensions instead of traditional for loops.

    • Ability to write small, clean functions , preferably pure functions that dont alter objects.

    • Knowing how to profile the performance of a Python script and how to optimize bottlenecks.

    The following will help to tackle any problem in data analytics and machine learning.

    I hope this set of Data Science Interview Questions and Answers will help you in preparing for your interviews. All the best!

    Got a question for us? Please mention it in the comments section and we will get back to you at the earliest.

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    Practice Solving Puzzles A Key Data Science Skill

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    Puzzles are a fairly popular way of evaluating a candidates quick thinking and analytical acumen. You need to be logical, creative and good with numbers to solve puzzles.

    Many organizations use puzzles for testing their candidates on their problem-solving skills. They want to know about your thought process and how you approach a problem.

    I cannot give you a complete guide to solving each puzzle, but I do have a few tips for you to proceed towards puzzle-solving:

    • Approach the problem slowly and understand all the details. Ask for any assumptions if they are not explicitly mentioned
    • These are meant to showcase your thought process. So make sure to walk your interviewer through your solution while you think
    • Do not stick with an approach for too long. Take cues from your interviewer and modify your approach accordingly
    • Realize that it is okay if you were not able to completely solve the puzzle. Different puzzles have different levels of difficulty and not all of them are meant to be solved in one sitting

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    Q118 Why Is Tensorflow The Most Preferred Library In Deep Learning

    Tensorflow provides both C++ and Python APIs, making it easier to work on and has a faster compilation time compared to other Deep Learning libraries like Keras and Torch. Tensorflow supports both CPU and GPU computing devices.

    Q119. What Do You Mean by Tensor in Tensorflow?

    A tensor is a mathematical object represented as arrays of higher dimensions. These arrays of data with different dimensions and ranks fed as input to the neural network are called Tensors.

    Readying Yourself For Atypical Data Science Questions

    The next step in this order is getting ready for the questions beyond your regular Data Science interview questions.

    For example, you might be asked questions like:

    • Who are the influencers that you follow?
    • What are some of the blogs that you read?

    If you have answers to these questions the interviewer will get the impression that you know how important it is to stay updated in the Data Science field.

    As a matter of fact, you should actually read blogs. Dont just claim to follow Andrew NG and know nothing about the latest updates he shared.

    You should be prepared for the questions that are not directly related to your prowess in Data Science, but rather your interest in the area.

    These were the three steps before the interview begins.

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    Stay Sharp With Our Data Science Interview Questions

    For data scientists, the work isn’t easy, but it’s rewarding and there are plenty of available positions out there. These data science interview questions can help you get one step closer to your dream job. So, prepare yourself for the rigors of interviewing and stay sharp with the nuts and bolts of data science.

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    Q87 If You Are Having 4gb Ram In Your Machine And You Want To Train Your Model On 10gb Data Set How Would You Go About This Problem Have You Ever Faced This Kind Of Problem In Your Machine Learning/data Science Experience So Far

    How to crack a Data Science Interview

    First of all, you have to ask which ML model you want to train.

    For Neural networks: Batch size with Numpy array will work.


  • Load the whole data in the Numpy array. Numpy array has a property to create a mapping of the complete data set, it doesnt load complete data set in memory.

  • You can pass an index to Numpy array to get required data.

  • Use this data to pass to the Neural network.

  • Have a small batch size.

  • For SVM: Partial fit will work


  • Divide one big data set in small size data sets.

  • Use a partial fit method of SVM, it requires a subset of the complete data set.

  • Repeat step 2 for other subsets.

  • However, you could actually face such an issue in reality. So, you could check out the best laptop for Machine Learning to prevent that. Having said that, lets move on to some questions on deep learning.

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    How Are Data Science And Machine Learning Related To Each Other

    Data Science and Machine Learning are two terms that are closely related but are often misunderstood. Both of them deal with data. However, there are some fundamental distinctions that show us how they are different from each other.

    Data Science is a broad field that deals with large volumes of data and allows us to draw insights out of this voluminous data. The entire process of Data Science takes care of multiple steps that are involved in drawing insights out of the available data. This process includes crucial steps such as data gathering, data analysis, data manipulation, data visualization, etc.

    Machine Learning, on the other hand, can be thought of as a sub-field of Data Science. It also deals with data, but here, we are solely focused on learning how to convert the processed data into a functional model, which can be used to map inputs to outputs, e.g., a model that can expect an image as an input and tell us if that image contains a flower as an output.

    In short, Data Science deals with gathering data, processing it, and finally, drawing insights from it. The field of Data Science that deals with building models using algorithms is called Machine Learning. Therefore, Machine Learning is an integral part of Data Science.

    What Do You Understand By Logistic Regression

    Logistic regression is a classification algorithm that can be used when the dependent variable is binary. Lets take an example. Here, we are trying to determine whether it will rain or not on the basis of temperature and humidity.

    Temperature and humidity are the independent variables, and rain would be our dependent variable. So, the logistic regression algorithm actually produces an S shape curve.

    Now, let us look at another scenario: Lets suppose that x-axis represents the runs scored by Virat Kohli and the y-axis represents the probability of the team India winning the match. From this graph, we can say that if Virat Kohli scores more than 50 runs, then there is a greater probability for team India to win the match. Similarly, if he scores less than 50 runs then the probability of team India winning the match is less than 50 percent.

    So, basically in logistic regression, the Y value lies within the range of 0 and 1. This is how logistic regression works.

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    What Is Deep Learning

    Deep Learning is a kind of Machine Learning, in which neural networks are used to imitate the structure of the human brain, and just like how a brain learns from information, machines are also made to learn from the information that is provided to them.

    Deep Learning is an advanced version of neural networks to make the machines learn from data. In Deep Learning, the neural networks comprise many hidden layers that are connected to each other, and the output of the previous layer is the input of the current layer.

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    Data Science Behavioral Interview Questions

    Ep.22 How to Ace A Data Science Interview | Data Science As A Career

    While there will be a heavy focus on your data science knowledge and skills, data scientist interviews also include behavioral rounds. Following are some behavioral interview questions you can practice to ace your data scientist interview:

  • Describe a time when you used data for presenting data-driven statistics.
  • Do you think vacations are important? How often do you think one should take a vacation?
  • Did you ever have two deadlines that you had to meet simultaneously? How did you manage that?
  • Describe a time when you had a disagreement with a senior over a project. How did you handle it?
  • How will you handle the situation if you have an insubordinate team member?
  • Why do you want to work as a data scientist with this company?
  • Which is your favorite leadership principle?
  • How do you ensure high productivity levels at work?
  • Have you ever had to explain a technical concept to a non-technical person? Was it difficult to do so?
  • How do you prioritize your work?
  • That concludes the comprehensive list of data scientist interview questions. Make sure you practice these frequently asked questions to prepare yourself for the interview.

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    Q59 What Is Supervised Learning

    Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples.

    Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor Algorithm and Neural Networks

    E.g. If you built a fruit classifier, the labels will be this is an orange, this is an apple and this is a banana, based on showing the classifier examples of apples, oranges and bananas.

    Differentiate Between Data Analytics And Data Science

    Data Analytics Data Science
    Data Analytics is a subset of Data Science. Data Science is a broad technology that includes various subsets such as Data Analytics, Data Mining, Data Visualization, etc.
    The goal of data analytics is to illustrate the precise details of retrieved insights. The goal of data science is to discover meaningful insights from massive datasets and derive the best possible solutions to resolve business issues.
    Requires just basic programming languages. Requires knowledge in advanced programming languages.
    It focuses on just finding the solutions. Data Science not only focuses on finding the solutions but also predicts the future with past patterns or insights.
    A data analysts job is to analyse data in order to make decisions. A data scientists job is to provide insightful data visualizations from raw data that are easily understandable.

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