How to Prepare for Data Science Interview in 2023?
The demand for data scientists will grow in the coming years, with a projected 11.5 million job openings by 2026 in the United States alone. The field of data science has rapidly gained popularity over the past decade, and as a result, competition for data science jobs has become fierce. For aspiring data scientists, it’s crucial to understand the interview process and what employers are looking for in potential candidates. Wondering ‘How to prepare for data science interview’? Read on to find the answer!
Table of contents
- Data Science Interview Rounds
- Common Interview Questions
- How to Prepare for Coding Challenges?
- Tips for Data Science Virtual Interviews
- How to Prepare for Data Science Technical Round?
- Enhancing Your Business Acumen
- Developing Your Soft Skills
- Preparing for Behavioral Questions
- Showcasing Your Projects
- Nail Your Upcoming Data Science Interview with Analytics Vidhya
- Frequently Asked Questions
Data Science Interview Rounds
Before you dive into how to prepare for data science interview, you should know there are certain types of interviews to prepare for:
|Coding Interviews||This interview assesses knowledge of various topics, including machine learning techniques, practical data extraction and manipulation challenges, and computer science principles.|
|Data Analysis||This interview involves assessing a sample data set and formulating solutions for resolving a business problem. Questions may involve engineering and modeling exercises, cleaning up data and extraction (SQL) issues, or exploratory assessment.|
|Case Study Interview||In this interview, you will discuss your strategy for solving an issue and offer recommendations in a business setting. Case studies may involve detecting metric changes, analyzing a successful outcome, assessing a characteristic with choices, or determining how to enhance a good or service.|
|Probability Interview||This interview evaluates your knowledge of probabilities and statistical applications.|
|Machine Learning||This interview covers the principles of machine learning and problem-solving strategies.|
|Non-technical Interview||During this interview, you will share your prior job history with the recruiting manager or potential coworkers. The majority of the questions are interpersonal in nature and non-technical.|
Common Interview Questions
You must take advantage of basic questions when learning how to prepare for data science interview. Here is a list of articles you can consider studying for your data science interview preparation:
- Interview questions on machine learning and data science
- Interview questions for data scientists
- Deep learning interview questions for data scientist
How to Prepare for Coding Challenges?
There are 4 types of coding questions asked in the technical round – data manipulation, machine learning algorithms, statistics and data structure. The interviewer expects the candidate to consider different approaches to enhance the solution. Follow the following points for data science interview preparation for the coding challenge:
- Study the foundational concepts associated with each one of the four question themes.
- Work on a lot of simple and difficult problems for each of the themes.
- You can have a practice interview with a friend. You can practice using actual interview questions through online coding setups in a constrained amount of time.
Tips for Data Science Virtual Interviews
Some of the data science interview tips for a virtual setup are as follows:
- Remember to have a good and stable internet connection
- Have the required software for the interview
- Ensure that you have a clean, tidy and professional setup for the interview
- Check your video quality before the interview
- The lighting in the room or surrounding should be good and in the right direction and angle
- Check the microphone and camera beforehand.
How to Prepare for Data Science Technical Round?
It is very important to be well-skilled in the technical aspect while you are doing your data science interview prep. Some of them are as follows:
Brushing-up on Programming Languages (Python, R, and other)
Irrespective of your organization, knowing how to code is necessary for data science positions. Interviewers will inquire about your previous experience with popular programming languages like Python, R, and SQL. These tests usually include manipulating data through code created to evaluate your programming, inventiveness, and problem-solving abilities. You’ll need to utilize a computer or the whiteboard at the interview to perform the tasks. However, you may also have to discuss the challenges out loud, along with your reasoning.
Reviewing Data Structures and Algorithms
You’ll be asked to discuss the goals of various algorithms and how they could help with various challenges and showcase your understanding of various machine learning algorithms during the interview process. As a result, you want to update your expertise with standard algorithms and data structures. There will be a single situation given to you during the interview, which will be the subject of several questions, from easy to difficult. Each query might deal with a different algorithm or data structure.
Practicing Data Manipulation and Analysis Techniques
The initial step when dealing with data is to collect it. You will have to show that you understand how you can select data sources and retrieve the information you require. The next stage is cleaning the data, so be capable of showing these abilities. Lastly, you must manipulate and analyze your data using certain tools. Additionally, you must have a solid knowledge of mathematical and statistical principles if you plan to pursue a career in data science. Applicants are usually tested on certain fundamentals by interviewers. So be sure you comprehend the statistical significance, logistic regression, regression analysis, probabilistic distributions, and variance.
Learning Common Machine Learning Models and Techniques
You might need to deal with certain tasks involving machine learning according to the requirements of your position. Explore some popular ML techniques, including K-Nearest Neighbors, decision trees, random forests, and linear and logistic regressions. Then, use your newfound knowledge by going over some sample machine-learning problems. Depending on the circumstance, the number of samples, requirements, and other factors, you will probably be questioned about various data modeling techniques. Ultimately, it’s crucial to explain both your approach and the justification behind it.
Enhancing Your Business Acumen
An important aspect during data science interview preparation is good knowledge of the organization you will work for. Learn the following things before your data science interview prep:
Understanding the Industry and Company you’re Interviewing with
Do your research and get to know the business, its industry, and the interviewers before the interview. You must review the particulars of the data science position you are applying for because you have probably applied to many different ones. Consider the required qualifications, attributes, and any organization’s information so everything remains newly formed in your thoughts. Look at the goods or services they provide. Choose a couple of them, and consider how you may use your data science skills to improve how they work.
Familiarizing yourself with Business and Financial Concepts
The simplest way for you to begin your data science interview preparation is to research the organization and the position you will be pursuing. Browse the organization’s web pages, social networking site pages, and feedback. If you can, attempt to chat with some of the current employees. The more information you can get about the organization’s ideals, procedures, and structures, the more likely you can adapt your responses and show that you align with their objectives. Study the job description to see what duties you will perform, as this will probably be the basis for your evaluation.
Developing Your Soft Skills
Soft skills play an important role when one works in an organization. When jotting down the points on how to prepare for data science interview, do not forget the following:
Practicing Effective Communication and Collaboration
A data scientist is supposed to be competent at integrating the technical, scientific, and analytical aspects with the business-oriented aspects. They must provide what they have discovered to stakeholders and business clients while demonstrating how these discoveries might benefit the company. Data scientists have to be skilled at data analysis and communicate their conclusions to audiences that are technical and non-technical. By doing so, they can encourage the use of data throughout the company, highlighting their contributions and increasing their visibility across divisions.
Highlighting your Ability to Work in a Team
Data scientists must appreciate the value of cooperation and efficiently cooperate with their peers because they don’t function independently. Regardless of the specifics of your job, you should ensure you’ve got yourself in a role that constantly enables you to develop as a data scientist. Discuss the team you’ll work with, comprising the manager and the other employees.
Demonstrating your Problem-solving and Critical-thinking Skills
When you are a data scientist, you must have the aptitude and dedication to solve a problem. Being a data scientist requires problem-solving aptitude and motivation. As important as understanding how to tackle a challenge to resolve it is having the urge to go thoroughly into a topic. In the discipline of data science, critical thinking includes:
- Thinking about the data source.
- All potential solutions to an issue.
- Maintaining an ongoing sense of curiosity.
Preparing for Behavioral Questions
Every interview involves a set of behavioral questions. When discussing how to prepare for data science interview, you ought to consider preparing for behavioral questions:
Tips for Answering Common Behavioral Questions
Data science is a human-centered endeavor. Organizations hire data scientists to add a more empathetic distinctive perspective and unique set of skills that enhance their IT architecture. Therefore, data scientists need to develop technical and soft skills to reach their career potential. Your choice of examples while answering behavioral questions should demonstrate to the interviewers how you offer the essential qualities they are searching for.
Preparing Examples to Demonstrate your Experience and Skills
The interviewers are interested in your abilities to respond to challenges and pressure. Pick an instance during which you were stressed for time, and have a detailed answer to the problem and a compelling argument for your decision. Explain to the interviewers any technical problems you have encountered and the actions you followed to work with each of the various non-technical departments of the company to solve them. In addition to showcasing your problem-solving abilities, you may highlight your exceptional communication abilities and managerial efforts while responding to a question regarding resolving disagreements.
Addressing Potential Red Flags or Weaknesses
No matter how much you want to sell yourself and come across as desirable, avoid lying regarding or overdoing your technical expertise or computer software experiences. The transparency and boldness regarding your skills will help you in the organization in the long run. Truthfulness, credibility, and commitment cannot be learned, but technical skills can. You can persuade them that you are worth training if you demonstrate these.
Showcasing Your Projects
Highlighting Personal Projects and their Relevance to the Job
You’ll have to have experiences regardless of whether you’re coming straight from college to data science, from another field, or simply seeking another type of data science job. A portfolio of projects will show your employment experience in your job applications. It demonstrates to prospective managers that you can perform the data science duties you are applying for. Executing the job well involves more than only demonstrating technical expertise. Also, ensure you include your behavioral skills.
Preparing a Portfolio of Projects to Share with the Interviewer
It plays an essential role in having a strong portfolio. Invest an adequate amount of time finding a few ideal projects. Any excellent project will be helpful in both your learning and job hunt. The primary skills that data scientists must possess are creativity and a natural aptitude for problem-solving. A strong project portfolio might serve as a demonstration of these abilities. As you create the portfolio, you’re taking your expertise in technical skills to a completely new dimension. Customize your comprehensive portfolio to the job for which you are applying if you’re required to. Choose a few projects that best fit the job description.
Discussing Project Goals, Methodology, and Outcomes
Applicants can display how they work when answering these questions, and the manager in charge of hiring can learn more about the candidates’ approaches to teamwork and problem-solving abilities. They may also have close attention to a worker’s communication style. The STAR model can organize your responses for data science behavioral interviews. You would format your responses as follows:
- Situation: Give a general description of the scenario you faced
- Task: Describe the task you have to complete
- Action: Specify the steps you took
- Results: Concentrate on the immediate consequences of your activities.
Nail Your Upcoming Data Science Interview with Analytics Vidhya
Interviews are unpredictable. When deciding on how to prepare for data science interview, many factors must be considered. Data science is a field of study that explores various disciplines of ML, statistics, data structure, programming, etc. To get a progressive and exciting data science job, one should consider enrolling in online courses or programs which have an extensive syllabus and curriculum to teach one about data science. Analytics Vidya is one of the leading platforms that provide excellent data science certification to help individuals land a job in this field.
Frequently Asked Questions
A. The preparation time for your data science interview depends upon your skills. If you keep updating yourself with the latest practices, it will not take you more than a week to prepare for your data science interview. However, you will take a lot more time if your concepts are unclear. Sign up for a data science course to help you learn the best skills quickly.
A. Here are the top data science skills that you must master before your next interview:
2. Statistics and probability
3. Data wrangling and database management
4. Machine learning and deep learning
5. Data visualization
6. Cloud computing
7. Interpersonal skills
A. Data Science interviews can be tough for many candidates as they must demonstrate a wide range of skills to get the job. They must be sound with technical, problem-solving and communication skills.
A. Here are a few ways that can help you crack your next data science interview:
1. Purpose of Coding Questions
2. Practice Coding Questions
3. Communicate your thought process
4. Focus on theory and learn how to implement it
5. Explain your projects to the interviewers
A. Yes. Data Science professionals use coding languages like Python and R to create ML models and large datasets.