How to ace the data science coding challenge

How to ace the data science coding challenge

In this article, I will share some useful tips from my personal experience that would help you excel in the coding challenge project. Before delving into the tips, let’s first examine some sample coding exercises.

So, you’ve successfully gone through the initial screening phase of the interview process. It is now time for the most important step in the interview process, namely, the take-home coding challenge. This is generally a data science problem, e.g., machine learning model, linear regression, classification problem, time series analysis, etc.

Data science coding projects vary in scope and complexity. Sometimes, the project could be as simple as producing summary statistics, charts, and visualizations. It could also involve building a regression model, classification model, or forecasting using a time-dependent dataset. The project could also be very complex and difficult. In this case, no clear guidance is provided as to the specific type of model to use. In this case, you’ll have to come up with your own model that is best suitable for addressing project goals and objectives.

Generally, the interview team will provide you with project directions and a dataset. If you are fortunate, they may provide a small dataset that is clean and stored in a comma-separated value (CSV) file format. That way, you don’t have to worry about mining the data and transforming it into a form suitable for analysis. For the couple of interviews I had, I worked with 2 types of datasets: one had 160 observations (rows), while the other had 50,000 observations with lots of missing values. The take-home coding exercise clearly differs from companies to companies, as further described below.

In this article, I will share some useful tips from my personal experience that would help you excel in the coding challenge project. Before delving into the tips, let’s first examine some sample coding exercises.

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