Getting a data science job may be one of the most career-changing decisions usually taken, for most of the time through an extremely toll-taking interview. Tools together, data science interviews are flooded with technical, analytical, and behavioral questions. It's a test of knowledge, problem-solving skills, and attributes for the delivery of complex ideas.
Presented here are the ten ways to ace that data science job interview and stand at the top of the lot.
First of all, you must learn the basics of data science. These include statistics, probability, linear algebra, and calculus. Be prepared to explain how all of the basics work on real-world data problems.
Tip: You should review crucial concepts and work through practice problems on Khan Academy or MIT OpenCourseWare.
First of all, in any Data Science job, knowledge of at least one programming language, preferably Python or R would come first. With this regard, you will be expected to write clean and effective code, and know several libraries, and what they do, like pandas, NumPy, Scikit-learn, and so on.
Tip: Complete coding projects that are available for free on various websites like Coursera, Datacamp, etc.
Every week on LeetCode, HackerRank, or Kaggle. You can Review common machine learning algorithms, including applications, strengths, and weaknesses. Be prepared to tell how you used these algorithms in past projects or how you could in hypothetical scenarios.
Tip: One needs to make a cheat sheet with key algorithms and characteristics so that you can do a quick review of it before the interview.
You may find these in the form of coding challenges or case studies which might go on in a data science interview. Practice problems go all the way from exercises to exploratory data analysis, to model building. Find the top questions to prepare for data science job interviews and their answers.
Tip: You can use online resources like DataCamp or Coursera for practice problems and case studies.
This will be the case most of the time in your portfolio of projects. Be prepared to take them through every project. From stating the problems to your approach: what tools and techniques were used, and the results.
Tip: Put them up on GitHub or your website for easy access by interviewers.
While interviewers truly care about your approach, they often just care about your solution. So spell out for them how you are thinking: break down a problem, describe how you selected your methods, and describe how you validate your results.
Tip: Run through describing your thinking out loud: by teaching a friend, by recording yourself.
These behavioral questions search for your soft skills like teamwork, communication, and problem-solving. Be prepared to give examples of working in teams, managing conflicts, and adjusting to new situations. You can also figure out the frequently asked interview questions for data science roles to prepare better.
Tip: While answering behavioral questions, use the STAR method: Situation, Task, Action, Result.
Research the Organization and Sector: Be secondly aware of their products and services, and what types of news have recently been covered. This type of knowledge shall let you make your answers interesting by showing interest in the company.
Tip: You will find this information on the company website, LinkedIn, and Reports on Industries.
And finally, there will be time for you to ask one or two questions. Just think in advance, in particular about the sorts of things you might ask that would show your interest in the job and the firm's work with data more generally. Do your research on mistakes to avoid when applying for data science jobs, this way you will know the right questions to ask your interviewer.
Tip: You could ask about the infrastructure that the firm has for working with its data, workflow, or other major projects.
Get your friends, mentors, or even online services to do mock interviews with you. So you can become comfortable with how an interview goes and you will also get meaningful feedback.
Tip: You can practice mock Interviews with real data scientists on Pramp or Interviewing.io.
Here are some useful articles on LinkedIn to help you ace your data science job interviews:
1. How to Prepare for a Data Science Interview by Yiwen Chen
2. Top Data Science Interview Questions and Answers by Julie Zhu
Nail that data science job interview; instill technical knowledge in problem-solving and communication. You would want to practice coding, have a very strong base in the basics and machine learning algorithms, and be prepared for technical and behavioral questions in the interview. You also need to share your projects, walk them through your thought process, and understand the needs of the company. You can ace an interview with practice and good preparation.