What skills do I need to be a data scientist at Google or Facebook?

For Data scientist or Analytics Role one needs to be more statistically informed as compared to a software engineer; and a better software professional than a statistician. In order to crack the interview process of elite organizations such as Google or Facebook; prospective students must ensure that they cover all the three important facets of a data scientist – knowledge on mathematical algorithms, know-how of statistical tools & techniques to implement the mathematical models, and finally, hands-on exposure to software tools to present a story-telling visualization product to business.
In addition to that, companies like Google, Facebook tends to put substantial focus on logical reasoning, product knowledge & understanding and communication as well.

Follow the link to get a brief on the key questions asked by Google for data scientist roles.
https://www.glassdoor.co.in/Interview/Google-Data-Scientist-Interview-Questions-EI_IE9079.0,6_KO7,21.htm?countryRedirect=true

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  1. Google does a lot of work in Python and they use it as well for some of their data analysis. Also, uses R for reporting and analytics.
  2. Mapreduce for sure
  3. Facebook relies heavily on Hive/Cassandra, so learning that can help
  4. Mathematical concepts and Machine Learning basics is a must (statistical modules of Python and R like numpy, scikit-learn)
  5. Visualization ( matplotlib or similar)

and there is a long list of other supporting tools and technologies.

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Organizations like FB, Google, Uber etc. majorly look for a good analytical mind with basics to intermediate coding skills. To crack interviews of such organizations one needs a good approach to cover important Data Structures and Machine learning concepts.

  • For Data Structures/Algorithms , one can practice questions from Leetcode (majorly easy and medium) and GeeksforGeek. The major topics that they generally focus upon : Array, List, Dictionary (Hashmap), DP, Binary Tree, Binary Search Tree and Sorting algorithms.

  • From Statistical point of view, basic questions over Probability, CLT, Bayes Theorem, measure of central tendency etc.

  • For Machine Learning , be thorough with the ML algorithms which you have applied in your projects. Also the most important topics which many miss out is Evaluation metrics like R-squared (R2), AUC-ROC curve etc.

  • Also before going to interview for these organizations do check out interview section from Glassdoor as it can give you a better idea about the kind of questions you can expect from the interviewers.

Hope I will be able to answer you. Have a good day and keep learning :slight_smile: