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

Data Science, AI and ML
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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:

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From Google, Microsoft, and Facebook to Swiggy, Zomato, and Byju’s, everyone wants to be a part of the Data Science and Machine Learning bandwagon. Data Science is certainly one of the most rapidly expanding fields in terms of employment opportunities and pay. A comprehensive range of talents in a variety of sectors would be required. A few talents needed to work as a data scientist in any company are listed below:

  • Technical Skills: Algorithm in a Procedural Programming Language, Advanced SQL, Software Systems’ Understanding of Large Datasets
  • Statistics, probability, and combinatorics are examples of math skills.
  • Capacity to create an investigative path, ability to frame problems, and cohesive thinking that leads to answers are all examples of analysis reasoning talents.
  • Product knowledge: An approach to the product in terms of business and user-interaction goals. One’s capacity to predict the product’s success.
  • Communication abilities: The ability to communicate in a systematic and orderly manner. You should be able to clearly communicate your abilities.

Interested in Data Science? Check out this https://www.boardinfinity.com/learning-path/data-science by Board Infinity.

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  1. Yes you are correct

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To become a data scientist at Google or Facebook, you need a blend of technical and analytical skills. Proficiency in programming languages like Python or R is crucial for data manipulation and analysis. Strong knowledge of SQL is essential for querying databases. You should be adept in statistical analysis and machine learning techniques, including supervised and unsupervised learning, and familiar with frameworks like TensorFlow or PyTorch.

Additionally, experience with data visualization tools such as Tableau or D3.js is important for presenting insights. Solid understanding of algorithms and data structures, as well as experience with big data technologies like Hadoop or Spark, is beneficial. Effective communication skills are vital for translating complex data findings into actionable strategies for non-technical stakeholders. A strong background in mathematics, statistics, or a related field, along with a proven track record of solving real-world problems using data, is highly valued.