The nine tips above covered the most obvious tips for beginning data scientists.
The next round of interview tips addresses more nuanced aspects of how to present yourself as the best candidate for the position.
Building on my previous article, these additional tips will increase your chances in data science beginner job interviews.
Creating a portfolio of data science projects is one of the best ways to demonstrate how good a data scientist you are.
It can be difficult for beginners to choose suitable projects for their portfolio. Are here some data science project ideas to start. You can also go deeper into Datacamp Suggestions either data projects we have in StrataScratch.
Domain knowledge means you have knowledge of a specific industry, sector, or subject area. This knowledge includes complexities, challenges, terminologies, processes and nuances of that particular domain.
It has to reflect on your coding skills as you will use them to solve problems for a particular company within a particular industry.
When practicing coding, it would be ideal if you did so with real questions from the company you are interviewing for. I mentioned StrataScratch and Leet code in the first article.
Of course, you can practice with challenges that don’t come directly from the interviews. But when you choose them, try to find data science interview questions and relevant industry data sets. Let’s say you’re interviewing for Meta (tech industry) and Pfizer (pharmaceutical industry). These companies work with completely different data, which behaves differently. Naturally, the questions will also be different. So for Meta, use tech/social media data, and for Pfizer, pharma data.
That way, you will also ensure that you improve your domain knowledge. You may come across specific data that you are not familiar with, so you will need to learn about it and its importance within the industry.
You are now connecting coding with domain knowledge!
Data storytelling means you can communicate insights from your data projects in a clear and understandable way. Think about why you started a certain project and what you achieved; There’s always a story there.
By creating a story from your project, you will make the data more accessible to non-technical people. In return, you will have more influence in decision-making.
Here are some tips to demonstrate this skill.
Create a narrative: Any good story has an arc: exposition, a problem, rising action, climax, falling action, and resolution. Include this when telling your story using data.
You could start with the business context, for example, “The company launched five new products in the last three years.” So, the problem. He has noticed that sales are increasing, but customer retention is not. Now you will take action by digging deeper into the data and trying to find the reasons for the retention problem. Here, your story should delve into the technical aspects of the project: that you did it and because. The climax is when you find a product with high sales but also high return rates. Downstream action is when you look at possible reasons for high returns. In the resolution, you make a recommendation to improve the product. In the resolution, directly relate what your project did and quantify your achievements. Don’t let your story end with recommendations to improve the product, but instead talk about the increase in sales of that product, how much money it brought to the company, etc.
Use clear visualizations: Use visualizations that support your story.
In your project on sales trends over the past few years, don’t simply show a table with monthly sales figures. Instead, use a line chart to visually represent the ups and downs in sales. This way, the audience will catch the trend. For significant spikes in sales, use a bar chart to break down sales by product or product categories, highlighting which products drove the spike.
Avoid jargon and simplify complex concepts: Use technical terminology only when necessary. The point is to “sell” (sometimes even literally) your idea and project to business people, so that you simplify complex concepts for them. Don’t say, “Heteroscedasticity in the residuals indicated that our linear regression model may not be the best fit.” Instead, say: “Patterns in our data suggested that our initial model may not be capturing all the information effectively.” Better!
We all make mistakes. They are necessary in the learning process. Interviewers are not looking for a perfect candidate; They are looking for someone who wants and can learn.
Let the interviewer know that side of you. If he honestly shares his failures and what he learned from them, he will build confidence and demonstrate his resilience in the face of setbacks.
Here are some tips on how to talk about this.
Avoid the blame game: Avoid blaming everyone and everything for your mistakes. Of course, provide context of circumstances beyond your control, but don’t play the victim. Take responsibility for your part, show what you learned from these circumstances, and talk about what you should have done differently.
Emphasize the learning part, not the failure part: Talking about failures should only serve to present how and what you learned from them, so focus on that.
Speak from experience: Find a real example from your previous work. Even if it’s not in data science, it can be applicable if you show your focus on learning and self-awareness. If you don’t have work experience, talk about the mistakes you made in your data projects and what you learned.
Talk about the steps you took: This relates to what you did to correct your mistake or minimize its impact, for example changing the data, adjusting the algorithm, or completely abandoning the project and starting a new one.
This is what the conversation between you (Y) and the interviewer (I) could be like.
YO: “Can you tell me about a time when a project or task didn’t go as planned and how you handled it?”
AND: Of course! During my previous role as a data scientist, I was responsible for a project aimed at predicting customer churn. I chose the k-nearest neighbor algorithm based on my initial understanding and ran it. However, the results were not as accurate as I expected.
YO: As? What did you do when you realized that?
AND: There were some inconsistencies in the data and the deadline was very tight, so my EDA was not very thorough. Despite that, I now realize that I should have done a more detailed EDA. After learning about the inconsistencies, I collaborated with the data quality team to better understand them. I also explored other algorithms and evaluated them. Finally, I switched to the XGBoost algorithm, which significantly improved the model’s prediction accuracy. I learned not to underestimate the importance of EDA. I’m also glad that I wasn’t afraid to admit mistakes and start from scratch considering that a model we couldn’t trust would be of no use to us.
Data science is more than handling data and writing meaningless code. This means being able to translate your work into the language of mere mortals through data visualizations and storytelling.
You will demonstrate it by talking about it in the interview. You need to make sure you can talk the talk, but also demonstrate that you can walk the talk. The best way to do this is to have a strong portfolio of data projects, where your coding, storytelling and visualization skills will be evident.
As you work on projects, you will make mistakes. Don’t hide them. Talk about them openly and seek feedback from your interviewer.
It comes down to two simple things: be competent and honest about how you did it. Easier said than done!
But with some tips I gave you in this article, I’m sure you’ll do well in your next data science interview!
Nate Rosidi He is a data scientist and in product strategy. He is also an adjunct professor of analysis and is the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real questions from top companies. Connect with him on Twitter: StrataScratch either LinkedIn.
