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Data Science Interview Questions Classifier

Background

The objective is to build a large database of data interview questions that can be used to develop a model that can suggest key questions that prospective candidates may want to focus on based on the content of the job advertisement, resume, company and/or key focus areas. This database can help focus time and effort in the interview preparation process. Key stages to building the project:

  1. Initial research to identify key resources that can be used to develop the database
  2. Building of the database via explicit import of data files, web scraping and collation
  3. Creating a set of tagged questions that can serve as the ground truth in modeling activities
  4. Developing suggested answers to key questions.
  5. Exploratory data analysis that looks at the distribution of data questions relative to data type jobs
  6. Building a classification model using various NLP techniques
  7. Developing a Web API that people that are preparing for interviews can interact with to easily access the suggested list of questions and help manage their learning process.

Key Resources

Database Building

TODO

Question Classification

A classifier that categorizes Data Science questions into the following:

  • Communication
  • Data Analysis
  • Predictive Modeling
  • Probability
  • Product Metrics
  • Programming
  • Statistical Inference

Note: The classifier could assign multiple tags to a given data science question.

TODO