Live Breakdown of Common Data Science Interview Questions | Kaggle




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Live Breakdown of Common Data Science Interview Questions | Kaggle
https://www.youtube.com/watch?v=aXUsrKPTBvY

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http://www.youtube.com/user/kaggledotcom

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Comment List

  • Kaggle
    November 24, 2020

    And I am a bad programmer

  • Kaggle
    November 24, 2020

    I have interview after 8 days . Somebody take my mock interview please

  • Kaggle
    November 24, 2020

    That's really useful, thanks!

  • Kaggle
    November 24, 2020

    Rachel Tatman is amazing!

  • Kaggle
    November 24, 2020

    can't we use the 25%ile and 75%ile formula along with IQR for outlier detection ?

  • Kaggle
    November 24, 2020

    very informative, i just imagined my self giving an interview to the panel !!

  • Kaggle
    November 24, 2020

    copy paste monster will always be there to haunt you

  • Kaggle
    November 24, 2020

    My kaggle name is black nurse.Thank you very much.

  • Kaggle
    November 24, 2020

    DataTrain's coaches are professional Data Scientists, who train you to excel in technical interviews, and mock interview you. They connect you with other full-time data scientists, so you can get your "foot in the door" more easily. You don't pay until you get a data science job.

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  • Kaggle
    November 24, 2020

    So what's the difference between machine learning and statistics again?

  • Kaggle
    November 24, 2020

    29:42 The Max is supposed to be +1.5 STD's, she just copy/pasted from the min…

    P.S.: Could Rachael Tatman be more positive? 🙂

  • Kaggle
    November 24, 2020

    Sorry, but I don't think you addressed the veterinarian problem at all. You got all hung up on NA and did not even address the important considerations. Namely, 1. How do you compare rows to determine who needs to see the vet? 2. How do you score your formula against actual outcomes, which horses really needed to see the vet? You could start with a formula that takes values from each column.

    You did not even mention the difference between categorical variables and numeric variables. The formula should assign numeric values to the categories and multipliers on the numeric ones. I think the NA entries should just get a 0 contribution to its formula, because you are getting no information from that column for that horse, other than perhaps the suggestion that the trainer didn't think it was important enough to measure that variable for that horse.

    Then to make the formula useful, you should compare actual illnesses to the values in each column to find a correlation. You should us that to adjust your numeric values and multipliers so that your model is realistic.

  • Kaggle
    November 24, 2020

    I'd say statistics is descriptive and machine learning is predictive form of analytics.

  • Kaggle
    November 24, 2020

    Thank you for sharing the vidio. About to have my interview in 2 days.

  • Kaggle
    November 24, 2020

    Well done thank you so much for great knowledge.
    regards, Ibrahim Khalil from Egypt

  • Kaggle
    November 24, 2020

    DATA SCIENCE TRAINING INTERVIEW QUESTION KINDLY VISIT HERE
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  • Kaggle
    November 24, 2020

    Rachel Tatman <3

  • Kaggle
    November 24, 2020

    33:04, try using sql

  • Kaggle
    November 24, 2020

    My explanation on Statistics vs Machine Learning: They are two perspectives on the same world. For example, let's say we have a dataset of house prices and their square footage. We can plot them on a scatterplot and we should see a clear relationship. We can even draw a line to fit that data. Now, in statistics, we are interested in the properties of that line. What is its inclination and how sure are we that it is not flat? Then, in machine learning, we are not so much interested in the properties, but rather in predicting the price of a house, which we have not seen, but we know its size. With those simpler models, the predictions are in general less accurate, and we can only deal with a limited amount of variables. With more complicated models, for example Neural Nets, we can deal with a large amount of variables, even images, the predictions are more intelligent, but we have a lot harder time telling what the algorithm is doing and what it is telling us about the data.

  • Kaggle
    November 24, 2020

    Begins at 5:40

  • Kaggle
    November 24, 2020

    Thank you for organizing such an insightful conference 🙂 Proved really useful.Will be sharing the link on pages I manage on fb.

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