How To Ace Data Science Interviews

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The number of openings for data scientists has surged by 650 percent according to a recent analysis on LinkedIn. Trend analysts and marketers in businesses are looking at new prospects and experts in data science for creating profit and growth as over 70% of the world's population is online and generating tons of data.

There are several stages that must be conducted in order to recruit or onboard data scientists in an organization, but the interview is the one that is seen to be the most important, it helps the hiring managers to check the individual's skills, expertise, and various other aspects that might not be mentioned in a resume.

This blog will take you through basic know-how, key subjects, the knowledge/skills needed, and much more before appearing for an interview.

Most Important Tips For Data Science Interview

Before diving deep into the technical aspects of a data science interview let’s understand a few most important and basic tips.

1.  Create an Ideal CV    

A CV is something that the interviewer has in hand before s/he even sees you. It is your first impression, it helps the interviewer to have a pre-analysis of your profile and later check you on certain domains in the interview with respect to your CV. If you are targeting a particular job profile in your favorite company, make a job/company-specific resume. This might increase your chances of getting shortlisted.

An ideal data science resume should briefly describe its Introduction, Soft Skills, Certifications, Skills and Technologies, Work Experience, Education, and Accomplishments.

2. Create Unique Data Science Portfolio Projects

A Data Science Projects portfolio helps the interviewer analyze whether your resume is just you boasting about your achievements or whether you have used your skills to demonstrate something in real life as well.

When hiring managers review various profiles, "the value that their job could potentially provide for organizations" is the most important criterion they use to evaluate prospects. Applicants must therefore make sure that they concentrate on building a portfolio with pertinent talent and skills that could help the firm find opportunities to make money.

3. Acquiring Algorithmic Knowledge and Programming Skills

Regarding the core competencies, candidates for a data science interview can be checked on concepts like distributed computing, data structures, and programming languages like Python, R, and SQL. Once mastering a programming language one must also attempt to implement it algorithmically.

An aspirant will benefit from this not just in terms of understanding how to build and use sophisticated machine learning algorithms, but also in terms of the real-world use of the language and algorithm concepts.

4. Participate in Hackathons

Hackathons in data science may be challenging, especially for newcomers. And if you enter a hackathon with the attitude of winning at any cost, you probably end up disappointed. So keep your first step as small as possible. It's crucial to concentrate on tiny successes and approach hackathons as learning curves.

Participating in hackathons is a fantastic method to improve your data science abilities. Companies are leveraging the hackathon concept to hire the top data science talent thanks to platforms like Kaggle and MachineHack.

5.  Interview Questions          

There are a few possible data science interview questions the interviewer might ask the candidate during the interview. For instance, what workplace procedures have you improved? Can you give an example that you felt went entirely outside the box and helped the project turn around? These questions may have a purpose that includes information on the composition of the present data science team, the projects they are working on, and how important initiatives are given priority.

Interviewer Can Ask Questions On the Following Domains

1. Probability

You should anticipate being quizzed on probability fundamentals, conditional probability, and probability distribution. Basic concepts in probability include things like expectation, variance, permutations, and combinations. You will need to be familiar with Bayes' rule for conditional probability and certain often used discrete and continuous distributions, like the binomial, normal, and long-tailed distribution, for a probability distribution.

This may seem like a lot, but for interviews, the probability is usually the subject matter where you'll require the most in-depth preparation. There are consequently many potential questions the interviewer might ask you, and it is always preferable to be overprepared than underprepared.

2. Statistics

You need to be able to define terms like confidence intervals, maximum likelihood estimators, and null hypotheses. Data processing and the selection of the most significant figures from a large dataset require the use of statistics. This is crucial for making decisions and creating experiments.

You must be familiar with the vocabulary, including power, p-value, and confidence interval, as well as the various testing methodologies, including parametric tests like the z-test and t-test and non-parametric tests like the chi-squared test.

Regression is the last topic you need to be familiar with in statistics. You must be familiar with both linear and multiple regression. Regression will probably come up less frequently in interviews, but you should still make sure you feel at ease with it.

3.  Machine Learning

You should be able to describe ensemble methods, random forests, and K-nearest neighbors. R or Python is frequently used to apply these strategies. These algorithms demonstrate to employers that you are familiar with the most useful applications of data science.

Apart from these, you should also be comfortable with:

  • Natural Language Processing helps organizations realize the power of how ML can be used to gain actionable insights from the data that exists in the form of text.
  • Tree-Based Models are one of the most esteemed algorithms in machine learning and data science. They are open, simple to comprehend, strong in character, and broadly applicable. You can actually observe the algorithm in action and the steps it takes to arrive at a solution.
  • Support Vector Machines to work with smaller datasets and create effective models.
  • Dimensionality Reduction to handle a large dataset with millions of rows and thousands of columns. It is helpful in situations to understand when to utilize different dimensionality reduction approaches.
  • Clustering Technique which is crucial for extracting information from unlabeled data. It organizes the data into groups that are related to one another, which enhances various business judgments by supplying meta-knowledge. It is employed in many different industries, including marketing and banking. It's another essential skill that you need to understand well.

4. SQL & Database Design

To access, clean, and analyze data, various data science roles utilize SQL to interface with relational databases. More than 230,000 jobs on LinkedIn were listed as requiring SQL and Database Design as of the time of writing. Because it's such a crucial ability, data science interviews frequently involve a technical SQL and database screening.

Although you may be asked some definitional questions that are more typical of a routine interview, the main goal of this question is for the interviewer to confirm that you can use SQL in practice rather than simply in theory.

  1. Coding questions: Coding questions are all about your execution abilities. You'll need to apply the theories you know about in order to answer these questions. For instance, you might be asked to create and implement a coin simulation problem in a coding question that deals with probability. Another illustration might be a question requiring you to write code in R or Python to calculate the results of a hypothesis test. For excelling in this area you need to be good at understanding problems and framing the right solutions for them using your coding skills.  
  2. Product Sense: This is debatable, but people who are familiar with the product will be able to identify which KPIs are most crucial. A data scientist focused on products will choose the best metrics to test out of the many numbers that are available. Understanding the following terms can give you an upper hand:
  • Wireframing,
  • Retention and Conversion Rates,
  • Traffic Analysis,
  • Customer Feedback,
  • Internal Logs, and
  • A/B testing.

Conclusion

After you have completed the in-person interviews, you should send a thank-you note as a follow-up. Additionally, be sure to keep any promises you may have made (such as sharing a previous presentation or a piece of code). You've put in the hard work; it's time to finish up and bring home that dream job!

Additionally, you can visit the Live Virtual Mock Interview of a Data Science Aspirant. This might acquaint you with Data Science interviews at prestigious companies and undoubtedly assist you in determining the topics you need to concentrate on!

Also please feel free to enroll in our Data Science Course to learn from the best in the industry and gain hands-on experience and placement assistance.