Monday, July 8, 2024

Data Science Internship Interview Questions

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What Is The P

Data Science Interview Questions | Data Science Interview Questions Answers And Tips | Simplilearn

P-value is a number that ranges from 0 to 1. In a hypothesis test in statistics, the p-value helps in telling us how strong the results are. The claim that is kept for experiment or trial is called Null Hypothesis.

  • A low p-value i.e. p-value less than or equal to 0.05 indicates the strength of the results against the Null Hypothesis which in turn means that the Null Hypothesis can be rejected.
  • A high p-value i.e. p-value greater than 0.05 indicates the strength of the results in favour of the Null Hypothesis i.e. for the Null Hypothesis which in turn means that the Null Hypothesis can be accepted.

Common Data Science Interview Questions And Answers

Aside from the more subject-specific questions, there are a few common ones that are bound to come up during data science job interview questions.

Such common questions are usually asked to assess your soft skills or interest in the role you are applying for. Here are some of those data science job interview questions:

  • Why do you want to work here?For this question, we would advise you to do research on the organization and find out what they cater to. Instead of winging it, make sure that you have enough knowledge about what they expect from you and how that organization aligns with your data science career requirements.

Refrain from answering generically and do adequate research to personalize it for each company. If you send out applications based on the company, then you could delve into how you had been following their work.

However, if you send out applications based on the role, you need to find out more about the company and know a bit about the kind of work they do. Of all the applications, you could stand out if the recruiter understands that you are passionate about what you do.

  • Why should we hire you?Recruiters mainly ask this question to understand how your skills would benefit their company. The research you do will come handy in here too.

Even for this question, you need to have a basic understanding of what the company does. Assess the data scientist job description thoroughly to ensure that you are hitting at least some of the points that they mentioned.

What Is The Difference Between The Long Format Data And Wide Format Data

LONG FORMAT DATA: It contains values that repeat in the first column. In this format, each row is a one-time point per subject.

WIDE FORMAT DATA: In the Wide Format Data, the datas repeated responses will be in a single row, and each response can be recorded in separate columns.

Long format Table:

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What Was Your Most Successful/most Challenging Data Analysis Project

What theyâre really asking: What are your strengths and weaknesses?

When an interviewer asks you this type of question, theyâre often looking to evaluate your strengths and weaknesses as a data analyst. How do you overcome challenges, and how do you measure the success of a data project?

Getting asked about a project youâre proud of is your chance to highlight your skills and strengths. Do this by discussing your role in the project and what made it so successful. As you prepare your answer, take a look at the original job description. See if you can incorporate some of the skills and requirements listed.

If you get asked the negative version of the question , be honest as you focus your answer on lessons learned. Identify what went wrongâmaybe your data was incomplete or your sample size was too smallâand talk about what youâd do differently in the future to correct the error. Weâre human, and mistakes are a part of life. Whatâs important here is your ability to learn from them.

Interviewer might also ask:

  • Walk me through your portfolio.

  • What is your greatest strength as a data analyst? How about your greatest weakness?

  • Tell me about a data problem that challenged you.

What Are Rmse And Mse In A Linear Regression Model

Pin on Big Data Use Cases

RMSE: RMSE stands for Root Mean Square Error. In a linear regression model, RMSE is used to test the performance of the machine learning model. It is used to evaluate the data spread around the line of best fit. So, in simple words, it is used to measure the deviation of the residuals.

RMSE is calculated using the formula:

  • Yi is the actual value of the output variable.
  • Y is the predicted value and,
  • N is the number of data points.

MSE: Mean Squared Error is used to find how close is the line to the actual data. So, we make the difference in the distance of the data points from the line and the difference is squared. This is done for all the data points and the submission of the squared difference divided by the total number of data points gives us the Mean Squared Error .

So, if we are taking the squared difference of N data points and dividing the sum by N, what does it mean? Yes, it represents the average of the squared difference of a data point from the line i.e. the average of the squared difference between the actual and the predicted values. The formula for finding MSE is given below:

  • Yi is the actual value of the output variable
  • Y is the predicted value and,
  • N is the total number of data points.

So, RMSE is the square root of MSE.

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What Kind Of Projects Are Included As Part Of The Training

Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.

You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

What Is Star Schema

It is a traditional database schema with a central table. Satellite tables map IDs to physical names or descriptions and can be connected to the central fact table using the ID fields these tables are known as lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes, star schemas involve several layers of summarization to recover information faster.

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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.

Data Scientists Need To Be Able To Develop Their Own Questions That Guide Their Research What Is Your Process For Developing Questions Can You Provide An Example From Your Previous Role

How to get Data Science Internship | Shivani Singh | GeeksforGeeks

Unlike Data analysts who research and sort through data based on company management’s questions, data scientists are responsible for generating their own questions to contribute to various company knowledge. This question allows interviewers to gauge a candidate’s creativity and their ability to develop questions that relate to company operations or industry trends. A candidate’s answer should emphasize:

  • Creative-thinking skills
  • Knowledge of their employer and their industry
  • Communication with team members


“First, I review changes to company operations or procedures, research industry trends and look over company products or service offerings. I also consult with department heads or management professionals within the company to see if they have current issues or concerns. I use my findings to generate a few key questions to guide my research. If I have difficulty developing questions, I seek advice from other data professionals. In my previous role, I wanted to determine whether my employer’s marketing campaigns received more engagement than their competitors. I chose two campaigns that launched within the same time frame and reviewed sales data over the span of four months.”

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What Would Previous Coworkers And Managers Say About You

This classic question is all about self-awareness and honesty. The interviewer wants to get a better sense of your personality and work style based on others opinions. Answer truthfully, as theres a good chance your interviewer will follow up with your references to check for accuracy. This question also allows the interviewer to see whether you have a realistic self-image, so a solid response could also include two positive traits and one needs improvement. Be sure to support your response with evidence.

I think my previous coworkers would say that Im very detail-oriented, but able to see the big picture. As a data scientist, its vital to pay attention to every detail at the same time, you cant get too caught up in the single digits and forget what it all means. Because of this, my coworkers were always impressed by the creativity and curiosity I brought to our business problems. I tried to balance accurate models with creative problem-solving to yield unique insightsand Ill do that here, too.

Common Data Science Interview Questions From Students

Here are nine of the most frequently asked data science interview questions:

These are the questions I got when I interviewed for big companies

  • What is the difference between snowflake and star schema?
  • When you type in a URL into your browser, what happens?
  • Data structure questions
  • complicated SQL questions that involve Joins and sub-queries
  • How you would test certain features and create metrics for them
  • What is A/B Testing?
  • Also, look for frequently asked questions on the internet.
  • Some technical issues were re-examined.
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    How Does Hierarchical Clustering Work

    Let’s consider that we have a few points on a 2D plane with x-y coordinates.

    Here, each data point is a cluster of its own. We want to determine a way to compute the distance between each of these points. For this, we try to find the shortest distance between any two data points to form a cluster.

    Once we find those with the least distance between them, we start grouping them together and forming clusters of multiple points.

    This is represented in a tree-like structure called a dendrogram.

    As a result, we have three groups: P1-P2, P3-P4, and P5-P6. Similarly, we have three dendrograms, as shown below:

    In the next step, we bring two groups together. Now the two groups P3-P4 and P5-P6 are all under one dendrogram because they’re closer together than the P1-P2 group. This is as shown below:

    We finish when were left with one cluster and finally bring everything together.

    You can see how the cluster on the right went to the top with the gray hierarchical box connecting them.

    The next question is: How do we measure the distance between the data points? The next section of the Hierarchical clustering article answers this question.

    Learn data analysis, data visualization, machine learning, deep learning, SQL, R, and Python with the Data Science Course with Placement Guarantee. Check out the course now!

    How Is Data Science Different From Data Analytics

    10 questions machine learning engineers can expect in a job interview ...

    When we deal with data science, there are various other terms also which can be used as data science. Data Analytics is one of those terms. The data science and data analytics both deal with the data, but the difference is how they deal with it. So to clear the confusion between data science and data analytics, there are some differences given:

    Data Science:

    Data Science is a broad term which deals with structured, unstructured, and raw data. It includes everything related to data such as data analysis, data preparation, data cleansing, etc.

    Data science is not focused on answering particular queries. Instead, it focuses on exploring a massive amount of data, sometimes in an unstructured way.

    Data Analytics:

    Data analytics is a process of analysis of raw data to draw conclusions and meaningful insights from the data. To draw insights from data, data analytics involves the application of algorithms and mechanical process.

    Data analytics basically focus on inference which is a process of deriving conclusions from the observations.

    Data Analytics mainly focuses on answering particular queries and also perform better when it is focused.

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    How To Find Data Science Course In Top Cities

    Data Science is the most in-demand job in the IT industry and is a profession that provides an opportunity to make money by solving complex problems. In this post, I am going to answer your question on how you can get into the Data Science course in top cities like Chicago, London, San Francisco, and other big cities around the world.

    India Top Cities
    Data Science Course London

    I would suggest that you look for online courses which provide practical training from experts who have been working with companies like Google, Microsoft, Amazon and many more. These are organizations that need skilled data scientists to solve their business problems. If you cant find any courses available for your city then its time to start looking at different options such as MOOCs or Private Tutoring where students learn online under one instructor who has taught thousands of students worldwide.

    Once you have found a suitable course then enroll in it and start learning everything about Data Science including Python programming language, R programming language, statistics, machine learning, etc Try joining forums where people discuss topics related to Data Science so that you dont miss anything out while learning the subject. Keep yourself updated with all important news on Data Science because its constantly changing every day and there will be new trends emerging every day so keep up with it!

    About Linda-G-Wells

    How And By What Methods Data Visualisations Can Be Effectively Used

    Ans. Data visualisation is greatly helpful while creation of reports. There are quite a few reporting tools available such as tableau, Qlikview etc. which make use of plots, graphs etc for representing the overall idea and results for analysis. Data visualisations are also used in exploratory data analysis so that it gives us an overview of the data.

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    What Are The Major Industries In Chicago

    Chicago has created a brand for itself in a multitude of sectors. Manufacturing, transportation, information technology, and health services & technologies are all booming industries in Chicago these days. IT is quickly becoming the fastest expanding sector, and there is a high need for people with Data Science certification in Chicago.

    Common Behavioral Data Science Interview Questions

    Data Science Interview Questions (with answers)

    With behavioral interview questions, employers are looking for specific situations that showcase certain skills. The interviewer wants to understand how you dealt with situations in the past, what you learned, and what you are able to bring to their company.

    Examples of behavioral questions in a data science interview include:

    Question: Do you recall a situation when you had to clean and organize a big data set?

    Answer: Studies have shown that Data Scientists spend most of their time on data preparation, as opposed to data mining or modeling.

    If you have any experience as a Data Scientist, it is almost certain that you have experience cleaning and organizing a big data set.

    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
    • Data validation

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    Which Data Scientists Do You Admire Most And Why

    Great data scientists know their way around data, but they also stay up-to-date on the latest industry trends and names. With a question like this, the interviewer is checking that youve done your homework and know whats going on in the data science sphere. In your response, name one or two industry titans, and then share how theyve impacted your work. This is also an opportunity to share a bit about what drives you.

    I really admire Jake Porway from DataKind and Rayid Ghani from U. Chicago/DSSG. Both of them have done incredible work using data science contributions for social good. A lot of great organizations just dont have access to the data they need to have a greater impact, and data scientists like Jake and Rayid are changing that and making a better world. Theyve really inspired me, and theyre why I started volunteering with OurPaws, a local animal shelter, to make visualizations for their newsletter and, hopefully, up their adoption rates.

    What Is Root Cause Analysis

    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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    Intellipaats Data Science Courses

    • What are some recommended data science courses by Intellipaat?

    Intellipaat has collaborated with top-rated institutions to bring you several Data Science programs tailored to individuals and professionals who wish to become successful Data Scientists. Here are a few recommended courses that you may find is suitable for you:

    • Advanced Certification in Data Science and AI by CCE, IIT Madras
    • PG certification in Data Science and Machine Learning by MNIT, Jaipur
    • Masters in Data Science online program
    • Data Science online course

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