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https://github.com/milaan9/datascience_interview_questions

My Solutions to 120 commonly asked data science interview questions.
https://github.com/milaan9/datascience_interview_questions

data-analysis data-science interview-preparation interview-questions machine-learning predective-modeling probability product-metrics python-jupyter-notebooks python-tutorial-github python4datascience statistical-inference tutor-milaan9

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My Solutions to 120 commonly asked data science interview questions.

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# Data_Science_Interview_Questions 



## Introduction πŸ‘‹



Here are the answers to [120 Data Science Interview Questions](http://www.datasciencequestions.com/)



The above answer some is modified based on Kojin's original collection: [kojino/120-Data-Science-Interview-Questions](https://github.com/kojino/120-Data-Science-Interview-Questions)



Another solution is from: [Nitish-McQueen](https://github.com/Nitish-McQueen): [Data Science Interview Questions](./DataScience_Interview_Questions.pdf)



Quera has a good list of questions: [https://datascienceinterview.quora.com/Answers-1](https://datascienceinterview.quora.com/Answers-1)



Feel free to send me a pull request if you find any mistakes or have better answers.



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## Table of contents πŸ“‹



| **No.** | **Name** | 

| ------- | -------- | 

| 01 | **[01_120_Python_Basics_Interview_Questions](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/01_120_Python_Basics_Interview_Questions.ipynb)** |

| 02 | **[02_Predictive_Modeling](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/02_Predictive_Modeling.ipynb)** |

| 03 | **[03_Programming](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/03_Programming.ipynb)** |

| 04 | **[04_Probability](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/04_Probability.ipynb)** |

| 05 | **[05_Statistical_Inference](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/05_Statistical_Inference.ipynb)** |

| 06 | **[06_Data_Analysis](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/06_Data_Analysis.ipynb)** |

| 07 | **[07_Product_Metrics](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/07_Product_Metrics.ipynb)** |

| 08 | **[08_Communication](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/08_Communication.ipynb)** |

| 09 | **[09_Coding](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/09_Coding.ipynb)** |

| 10 | **[10_Linkedin_Skill_Assessment_Python](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/10_Linkedin_Skill_Assessment_Python.ipynb)** |

| 11 | **[DataScience_Interview_Questions](https://github.com/milaan9/DataScience_Interview_Questions/blob/main/DataScience_Interview_Questions.pdf)** |



These are online **read-only** versions. However you can **`Run β–Ά`**  all the codes **online** by clicking here ➞ binder



---



## Frequently asked questions ❔



### How can I thank you for writing and sharing this tutorial? 🌷



You can Star Badge and Fork Badge Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.



Go [**`here`**](https://github.com/milaan9/DataScience_Interview_Questions) if you aren't here already and click ➞ **`✰ Star`** and **`β΅– Fork`** button in the top right corner. You will be asked to create a GitHub account if you don't already have one.



---



### How can I read this tutorial without an Internet connection? GIF



1. Go [**`here`**](https://github.com/milaan9/DataScience_Interview_Questions) and click the big green ➞ **`Code`** button in the top right of the page, then click ➞ [**`Download ZIP`**](https://github.com/milaan9/DataScience_Interview_Questions/archive/refs/heads/main.zip).



    ![Download ZIP](img/dnld_rep.png) 



3. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.

    

4. Launch ipython notebook from the folder which contains the notebooks. Open each one of them

  

    **`Kernel ➞ Restart & Clear Output`**

    

This will clear all the outputs and now you can understand each statement and learn interactively.



If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.



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## Authors ✍️



I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcomeπŸ™



See [github's contributors page](https://github.com/milaan9/DataScience_Interview_Questions/graphs/contributors) for details.



If you have trouble with this tutorial please tell me about it by [Create an issue on GitHub](https://github.com/milaan9/DataScience_Interview_Questions/issues/new). and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.



If you like this tutorial, please [give it a ⭐ star](https://github.com/milaan9/DataScience_Interview_Questions).



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## Licence πŸ“œ



You may use this tutorial freely at your own risk. See [LICENSE](./LICENSE).