An interview is a venue where you can display your knowledge and get the job you have always dreamed about. Preparing yourself before the interview can make you confident.
Data Science interview preparation seems a big deal. As there are so many topics to cover, one is likely to get nervous and confused about what to prepare, and how to prepare. In this article, I will share the important topics, a few important questions from each topic that can be asked, and a few tips to consider while preparing for data science interviews.
Having an idea about the interview process can make your preparation easy. The Data Science interview process can have multiple rounds. The most common rounds are:
- Basic programming round in Python and SQL.
- Technical rounds, you can expect two to three technical rounds.
- HR round, which may include behavioral questions and salary discussion.
- Some companies also ask for assignments, a task, or a project related to the position.
Most Data Science interviews have one round in Python. So knowledge of basic to intermediate Python is required. A few questions that can be asked are:
- To solve a problem using Python to check your problem-solving skills.
- Pandas, Numpy, or Matplotlib libraries.
- String functions like split(), strip() etc.
- lambda functions, decorator, generator, etc.
#Tip, if a problem-solving question is asked, you should communicate your idea (logic you are thinking for solving that problem) as the interviewer is not really interested in the output but the way you solve a problem. You can take hints from the interviewer if required.
You can be asked to explain any of your projects from end to end. It is recommended to prepare it in advance, go through the project, recall all the steps, and make notes. You can expect more detailed questions from each step, hence select the project wisely. A few questions that can be asked are:
- Problem Statement, what is the problem you were trying to solve?
- What was your role in this project?
- What was the size of the team?
- Source of data.
- All data preprocessing steps in detail.
- Which model did you choose and Why?
- Duration of the project.
- Tools and Technologies used in the project.
- The outcome of the project.
Usually, this is a technical round. Therefore you can expect all the technical questions. This is the most crucial part of the interview which can change your game if communicated properly.
Statistics is the key to Data Science, candidates who are good with statistics are considered better. A few questions that can be asked are:
- Central Tendency
- Central Limit Theorem
- Distributions like Normal Distribution
- Advanced topics like Hypothesis Testing and p-value
- Methods to find outliers
Preparing your data well before applying a machine learning algorithm is the most time taking process in the lifecycle of any data science project. Hence there can be a lot of questions from data preprocessing like
- Different ways to handle missing values.
- Methods to convert a categorical variable to numerical variables.
- How to treat outliers?
- Why is data scaling required?
- How to handle imbalanced data?
Model Buiding is a part of your project. You can be asked to explain a machine learning algorithm of your choice or the one you have used in your project. A few questions that can be asked are:
- Why did you select this model?
- What other model did you try?
- What performance metrics did you use to evaluate your model?
- Did you face an overfitting or underfitting problem? How did you resolve it?
- Mathematics behind the machine learning algorithm.
Databases are required in Data Science. SQL queries like joins can be asked in the interview. It is good to have NoSQL database knowledge. A few questions that can be asked are:
- Which all databases do you know?
- Which database was used in your last project and why?
- Difference between SQL and NoSQL databases.
- Basic SQL queries like joins.
It is good to have domain knowledge of the industry you have applied for. For example, if you have an interview in a fintech company, having finance knowledge is good. If you have a project in the same domain, the interviewer will get more confidence in you.
You can look at the job description to understand the requirement better and can include those topics as part of your preparation. For example, if you are preparing for NLP, or Computer Vision roles then focus mostly on topics related to it and keep your project in the same tech. Some roles demand knowledge of cloud technologies like AWS, Microsoft Azure, etc. Other demands for API integration with web frameworks like Flask. You can prepare according to the demand.
There are some general questions that can be asked like
- Scenario Based Questions
- Solving a Business Problem
Below are a few tips that you can make your interview process better.
1. Perfect Resume
Creating a perfect resume is so vital. First of all, you will get an interview call based on your resume. You should make your resume more aligned with the job description. Keep skills and projects required for the role. Interviewers assume you are perfect in the skills mentioned in your resume and can ask anything from it, hence put them wisely.
2. Prepare your Introduction
The very first question asked in any interview round is to introduce yourself. You can make the flow of the interview according to your comfort with a perfect introduction. Try to keep the introduction more technical, you can include the projects, tools, technologies, experiences, or any new skill you are learning which is required in the job profile and be ready to discuss them in detail.
2. Be Confident
Confidence is the key to crack any interview. Sometimes we are not able to explain properly even though we know the answer due to a lack of confidence. Having knowledge about the interview process in advance and preparing accordingly can boost your confidence. You can check the interview process for any company on Glassdoor. Trust the hard work you have put in your preparation and give your best in the interview.
3. Prepare Interview Questions
All the skills and important questions are already discussed above in this article. If you prepare them well, you will surely pass the interview. It is good to do a last-time revision for the important topics. You can check the questions asked by that company for the same role on Glassdoor.
4. Ask Questions
Make the interviewer your friend and don’t keep it a one-way communication. If you get stuck somewhere don’t hesitate to ask questions.
5. Say No if You don’t Know
Saying a clear no for the topics you don’t know can save time for other important questions that you may know. If you try to explain a topic you don’t know properly, it can make you nervous and can ruin the whole process.
If you prepare all the above-mentioned topics well and follow the tips, trust me you are ready for the interview process. Sometimes we are not confident enough to face the interview due to fear of rejection. There are so many opportunities in data science so even if you don’t smash one there are many others waiting for you.