Applying for a data scientist position can be tricky, given the different industries, domain knowledge required, tools, years of experience required, etc.
However, some general skills are expected throughout the technical rounds which can be applicable and useful to any of the above-mentioned.
Below, I’m sharing some resources which I found useful for my job hunt throughout the past years for data analytics/data science positions:
- Data Structures and Algorithms in Python
Problem-Solving with Algorithms and Data Structures using Python
(Note: Excellent resource to get started or go on a deeper level of understanding of fundamental python data structures.)
- Leetcode / Whiteboard Technical Interview
Blind 75 — Most Common Leetcode Questions
(Note: These questions should be sufficient for any technical round for Data Scientists but also Machine Learning Engineers / Data Engineers or similar positions.)
If you don’t have Leetcode Premium you can also use its slightly less attractive twin site LintCode!
- General Data Science / Statistics Questions
Theoretical interview questions
(Note: General refresher for theoretical knowledge required to pass the data science interview. Feel free to contribute to the given repo by adding more relevant topics :))
- Standard SQL Questions
After reading Multiple books on SQL Server (T-SQL Fundamentals, T-SQL Querying, and T-SQL Window Functions, etc.), I created a sample word (and pdf file) with some of the hardest questions for each book and the expected output shown as an image below the question (instead of showing the answer right away).
The questions are solved in SQL Server, and therefore T-SQL is used as the language syntax.
Steps to access and solve the exercises:
Use this link to my drive and download the zip file:
Run the TSQL 3,4,5 Scripts (found in the zip file) in SQL Server Management Studio to create the necessary databases for each exercise group.
Solve the questions and try to match your answers to the desired output.
- High-Level Statistics
Here I would recommend as a starting point (quick refresher), the following free interactive course
And then continuing with this amazing free book (ISLR2). For those who prefer to solve the exercises in Python can refer to the following repository.
- Cloud / Architecture / DevOps
As I’m trying to clear the Machine Learning Engineering Exam Specialty next month, I have compiled the following repository with many resources from AWS whitepapers, statistics books, and overall experience in the field. Feel free to add more :)!
I hope everyone finds the given resources I used useful. Please share this with anyone who is currently applying for new jobs or interviewing!
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