Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

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Data science is a complex and multi-faceted field. That can make data science interviews feel like a serious test of your knowledge – and it can be tempting to revise like you would for an exam.

Authored by two Ex-Facebook employees, Ace the Data Science Interview is the best way to prepare for Data Science, Data Analyst, and Machine Learning interviews, so that you can land your dream job at FAANG, tech startups, or Wall Street. How do you prepare for a Data Science Interview? What topics do Data Science interviews cover? You might start with a phone or video interview before moving on to an in-person chat. Alternatively, you might have a few in-person interviews, depending on the role and company. Here are some tips: What is an API? It is an acronym for Application Programming Interface that can be used as a middleman between any two machines or applications that want to connect with each other within a set of rules for a specified task. VII) Other ApplicationsBody language is important in face-to-face interaction. Practice beforehand. You should look like a friendly and open person. There’s lots of depth to this topic in terms of how different data types are stored and encoded, but generally you don’t need to know this stuff for an interview. Just be aware that any given unit of data needs to fall into one of these types and the types will dictate what operations you are able to perform on your data. For example, you can multiply two numeric variables together, but of course you won’t be able to do that for two strings. First you’ll learn about data science and data science companies. From there you’ll explore how to acquire your data science skills and build a portfolio. Next you’ll learn how to find that data science job. This includes searching for the right job, resumes and cover letters, and even what to expect at the data science interview. After that, Build a Career in Data Science covers what to expect the first few months on the job. A strong portfolio can set you apart from the competition. Showcase your best projects, highlighting the impact your work has had on real-world problems. Be prepared to discuss each project in detail, explaining the challenges you faced, the techniques you used, and the results you achieved. Be Ready for Behavioral Questions Unsurprisingly, data scientists have to be adept at working with data. But recruiters want to know that you understand, and are comfortable working with, the entire data science process.

You don’t need to have an advanced understanding of statistics, but recruiters will often quiz candidates on some of the basics. So make sure that you have a good understanding of variability, probability distributions, logistic regression, linear regression, and statistical significance. Working with Data For each R and Python there is really only one option for visualizing your data: in Python you want to learn the matplotlib and in R you want to learn ggplot2. Both of these libraries provide flexible interfaces for creating nice-looking data visualizations that are far better than the base capabilities of either language. While technical skills are vital, don’t forget that soft skills are just as important. Be prepared to answer behavioral questions that explore your ability to work in a team, handle conflicts, and adapt to new situations. Use the STAR method (Situation, Task, Action, Result) to structure your answers and show how you’ve successfully navigated similar situations in the past. Suggested Pre-Reading Sometimes, especially at smaller companies, they may not fully know why they need a data scientist. If this is the case, emphasize how you can improve the company’s visibility and profits by enhancing the existing products or creating new solutions. Industries differ Data Science Projects with Python by Stephen Klosterman is a useful tool for aspiring data scientists who need to brush up on their project-making skills in Python.If the number they cite is amenable to you, then great. But if it’s not, ask if there’s room for them to budge. Make the case for yourself. Tell them why you deserve to earn more, and persuade them that paying you what you deserve will ultimately save them money. Are You Open to Making Less Than You Did at Your Last Job? Data Science interviews cover Probability, Statistics, Machine Learning, SQL & Database Design, Python Coding Questions, & Product Sense. That's why Ace the Data Science Interview has a chapter dedicated to each topic - it's everything you need for Data Science, Data Analyst, and Machine Learning interviews. Where can I get Data Science interview questions? If you already know who will interview you, Google them, and search for them on LinkedIn. Find out what type of person they are. Do they contribute a lot to open-source projects? Volunteer at some social organization? Who knows, maybe you went to the same college? This knowledge will help to choose the best communication strategy to reach out to your interviewers. Data Science Interview Prep: Interview Types Discuss the difference between Decision Trees and Random Forest Algorithms. A Decision Tree is a supervised algorithm mainly used for Regression and Classification where it breaks down a data set into smaller and smaller subsets by splitting data into sub-regions and predicting the average value at the leaf node, whereas a Random Forest trains an ensemble of trees by repeatedly resampling training data with replacement, and voting the trees for a final prediction. Now it is time for a data science technical interview. Depending on the role, different skills may be required, such as SQL, Python, R, and machine learning. Here, we will cover a rather wide range of skills you may need and provide resources to master them. Basic coding