Guide for Data Science Interns & New Grads

Data Science roles can vary widely depending on the company, but you can often predict the nature of the interview from the job posting itself.

Here's a brief guide:

• Product-focused Data Science Roles: If you spot keywords like "dashboarding", "SQL", "experiments", and mentions of basic machine learning in the job description, you're likely looking at a product-focused role. Interviewers for these positions usually expect candidates to demonstrate solid skills in SQL and a good grasp of statistics, while machine learning knowledge can be at a foundational level.
• ML-Heavy Data Science Roles: Generally found in startups or mid-sized companies, these roles prioritize deeper understanding of machine learning theories. The job descriptions for these positions often list specific libraries (e.g., sklearn, spacy) and advanced modeling techniques (like clustering, decision trees, etc.) as preferred experiences.

We recognize that many candidates are looking for short-term preparation without being overwhelmed by excessive resources. This isn’t a comprehensive deep-dive into data science fundamentals. Instead, the focus here is on immediate preparation.

The two best resources for intern or new grad data scientists when it comes to coding are Stratascratch and Leetcode. My preference is StrataScratch as it offers real coding questions that were given during interviews. Additionally, the datasets used for the coding questions are typically relevant to the companies domain. If you have already taken a database class or feel comfortable with basic SQL, start off with Medium level difficulty on StrataScratch. However, depending on your skill level you may need to start with easy on Leetcode and work your way up.

Check out these resources:Statistics PrimerStatistics Interview QuestionsDS Interview Questions GitHubProbability Cheatsheet

ML Probability CheatsheetML Cheatsheet on GitHub