As you think about applying to data science jobs, the first step is to make sure you have the requisite knowledge and skills beforehand. The more preparation you do ahead of time, the easier the interview process will be. We have provided resources here to help you review the needed topics (or learn them for the first time) to be as successful as possible in the interview process.
While not essential, it always helps to first make sure any prominent public facing accounts or profiles you have are up to date. This includes things like a traditional resume or cover letter, but for data science positions also includes things like a Github page or personal website.
- Preparing your (data science) Resume
- Resume Templates
- What makes a LinkedIn profile Great
- Grooming your Github (or other profile)
Given that Data Science is a broad field, reviewing every possible question you might get asked in an interview is infeasible, but there are some recurring themes and topics. As such we will try to provide resources to give you some foundational knowledge, but again the more you can prepare the better. For complete/holistic guides see the following:
While not as extensive as what you will encounter in a software engineering interview, most data science interview include a portion of live coding or whiteboarding somewhere in their process.
- Quora: How should I prepare for statistics questions for a data science interview
- Awesome Data Science (exhaustive resource list)
- A/B testing and website optimization
- Recommender Systems
- Topic modeling and NLP
- People Your May Know (who to follow)
- Fraud Detection
- Customer Churn prediction
- Anomaly Detection
- Time Series (Uber surge pricing)