Yes, our problems are a mix of both actual questions asked at top-tier tech companies, as well as tailored problems created from our panel of experts who have interviewed at top companies.

While these problems do rely on a basic understanding of statistics, Python/Pandas, SQL, and ML concepts, it is not necessary to have a degree in Data Science to be able to answer them. In fact, data scientists and analysts hired at top tech companies have varied backgrounds, with some folks having formal degrees and many folks self-taught in the field of data science. If you're willing to put in the necessary effort and practice, you should be able to pick up the basics of data science through these questions.

Great observation! Since the term 'data science' means many different things to many different folks (and even to hiring managers), we break our questions into the following categories:

• Data manipulation (using Python/Pandas)
• Classic programming/function questions (with solutions written in Python)
• Stats/ML concepts
• SQL/database querying

You'll find these core skills are useful for a range of job titles: data scientists (of course!), data analysts, business analysts, quantitative analysts, and many more.

All solutions will contain a clean static web page walking you through the code/logic needed to solve the problem. Additionally we offer interactive environments to run our solutions where relevant:

• Our programming and data analysis solutions will contain a link to a Google Colab (Jupyter) notebook allowing users to directly interact with the solution in their web browser (similar to example 2 on the home page).
• ~1/2 of our SQL questions will come with a SQL Fiddle link, allowing users to test their query/run the output against the data covered in the question. The other ~1/2 are intended to be solved whiteboard style (based on table schema, example rows), similar to what folks see in many interview situations.

Full-time students and employees are busy. Our list allows you to attempt a few questions per week, gaining consistent practice. We've found that because these questions come at a regular cadence through email, it helps folks to get into a rhythm and improve at interviewing over time, rather than all in one go. By breaking up the time in between the questions, you'll also find that you retain the information better.

You'll find that many of our questions can translate well to a data analyst or data scientist's daily work. Even as we've built out the questions/solutions we've been finding several valuable nuggets that have carried over in our own jobs. So, whether you're actively looking for a job, looking to keep your interview skills fresh over time, or simply looking to pull out a few applicable tricks to use in your current role -- our list can be helpful. If you're on the fence we'd recommend trying our list out (it's free!) and if you find some of the questions could be relevant (particularly as we progress with real-world datasets, models) you're always welcome to sign up for the solutions.

You can email us at team@interviewqs.com and we'll get back to you within a day.