Tips For A Successful Data Science Interview

Tips For A Successful Data Science Interview

On December 15, 2021, Posted by , In Interview Questions, With Comments Off on Tips For A Successful Data Science Interview
Tips For A Successful Data Science Interview

Introduction

It can be an intimidating and nerve-wracking experience to get interviewed for a job as a data scientist. You need to know how to showcase your technical and soft skills to potential employers. Apart from completing technical challenges and going through hard interview sessions, the candidates are often put through the wringer as companies try to determine whether they possess the creativity, technical chops, and right attitude to join their data science teams. 

Examples of Possible Data Science Questions

Following are some questions that might be asked from an applicant in a data science interview.

  1. What do you know about the working of K-means, and what distance metric would you use?
  2. What are the discriminative and generative algorithms? Do you know about their strengths and weaknesses?
  3. What do you know about Model Overfitting?
  4. What is the importance of gradient checking?
  5. How can you implement a circular queue using an array?
  6. Can you list some of the assumptions about logistic and linear regression?

Apart from machine learning and general data science questions, SQL coding questions are also asked from a data aspirant in his/her interview.

Tips to Crack a Data Science Interview

Let’s have a look at the following tips that a data aspirant must follow to get through the interview successfully:

  • Master the Programming Language

The candidate must learn fundamental programming languages like Python, R, and SQL to get through the data science interview. He must have a keen knowledge of essential topics such as data structures and distributed computing. 

The importance of the programming languages that the data scientist must master is as:

  • Python

In today’s world, Python is the most popular general-purpose and dynamic language in the IT industry, so the candidate must have a strong command of Python along with problem-solving skills. It is the easiest language to read and learn as it combines fast improvement with the ability to interface with high-performance algorithms written in C or Fortran. It is used widely in scientific computing, data mining, web development, and many other areas. Therefore, the demand for Python experts is significantly rising with the advancement of machine learning, artificial intelligence, and predictive analytics.

  • R

R is also a popular language in the world of data science. It is widely used to analyze structured and unstructured data. For this reason, it is considered a standard language for performing statistical operations. Numerous data analysts have composed their applications in R as it provides many statistical models.

The candidate must master this programming language because of the following reasons:

1. R is an interpreted language, so you can run your code without a compiler. It interprets the code and makes its development easier.

2. Many calculations are done with vectors, and R is a powerful vector language. You can easily add functions to a single vector without putting them in a loop by using it.

3. R is a statistical language used to perform any task and solve almost all problems.

  • SQL

Structured Query Language (SQL) is an essential language in the data science field used for storing and retrieving, querying, and editing the data stored in a relational database. It efficiently manages large datasets, thus reducing the turnaround time for online requests. Also, SQL is used for data wrangling and preparation. Therefore, you will need SQL when working with Big Data tools as it is the biggest asset of the data science and machine learning professionals.

  • Algorithmic Implementation of Programming Language

After mastering it, the candidate must know how to implement programming languages like Python, R, and SQL. This way, he can understand how to deploy complicated machine learning algorithms.

For instance, many SQL questions can be asked from the applicant during his/her data science interview. In general, the SQL questions can be bucketed into the following categories: 

  • Basic SQL questions
  • Definition based SQL questions
  • Analytics SQL questions
  • Database design questions
  • Logic-based SQL questions

Below is an example of an SQL question that can be asked from a candidate to check how he can implement a language given a scenario.

Consider that you are given two tables: a user table with demographic information and the neighborhood table indicating the neighborhood users live in. 

Here is the user table:

columnstype
idint
namevarchar
neighbourhood_idint
created_atdatetime

Here is the neighbourhood table:

columnstype
idint
namevarchar
city_idint

You have to write a query that returns all neighbourhoods having zero users.

Strategy to Solve the Question

Whenever the question asks about finding values with zero employees, users, posts, etc., think of the concept of Left Join.

We use an inner join to find any values that are present in both tables. However, a left join keeps only the values in the left table.

We have to find all neighbourhoods without users. So we must do a left join from the neighborhood table to the user table for doing this.

Afterward, we add a where condition to get every neighborhood with zero users, as shown in the code below.

SELECT n.name 
FROM neighbourhoods AS n
LEFT JOIN users AS u
ON n.id = u.neighbourhood_id
WHERE u.id IS NULL
  • Hands-on Experience

The candidate applying for the data scientist role must have hands-on experience of projects to get to know the practical depth of data science knowledge and develop better skills to understand and solve any given problem scenario. So, he must take up different data science projects and create models that provide him with in-depth knowledge of the domain. 

  • Active Digital Participation

We know that the data science community is growing swiftly on platforms such as Twitter, LinkedIn, Facebook, and many more. So, the candidate can take various initiatives to show his/her active digital participation. For instance, he/she can write LinkedIn posts related to the data science knowledge domain. He/she can also showcase technical skills for the community to get noticed. Also, the candidate can start his/her blog, and he must try to participate in competitions like hackathons and bootcamps. He must also try to contribute to different open-source projects on GitHub.

Furthermore, he must be aware of the latest tools and technologies being used around. This way, he can keep his pace the same as the emerging technologies. He can improve his learning and knowledge-seeking pace by identifying new trends and giving forward-looking opinions to help him get through the interview. These initiatives will ensure that the candidate is actively participating and contributing to the digital world.

  • Familiarity with Production Deployment Environments

Today, almost all organizations use the cloud as an infrastructure model. We can also say that the cloud is ruling the data science space. Well-known and popular cloud vendors like Amazon Web Service (AWS), Google Cloud Platform (GCP), and Azure have made it pretty easy for data scientists to instantly set up a machine learning environment and work without paying heed to the enormous mass of data. 

Conclusion

We discussed how you can crack a data science interview if you showcase technical skills, good communication, and professionalism. But if the recruiter points out any mistake during the interview, do not get afraid of accepting it as it will portray you as a person open to learning and criticism.

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