How to Explain Data Science Project in Interview

Introduction:-

Explaining your project to the recruiters is the best way to showcase your Data Science knowledge. In this blog, We will share how you can explain your data science project to the recruiter. Let’s go ahead explore each step.

Divide your data science project into the below steps and explain accordingly

• Explain the business problem you have solved.

• Data Collection

To implement any Data Science project you need data, so here you need to explain how you collected the data, data source, client data, web scraping, free APIs, open-source sites (Kaggle, Github Repos ) etc.

Once data collection is done, now turn to explain how you stored that data(Excel.csv, databases) because from here you will use data to train your model.

If data is live stream data you need to explain the entire setup you have done, infrastructure, and sources you have selected like Amazon S3 bucket, Big Data etc.

Once you have explained this, now comes the challenge which you have faced. Explain what was the challenge and how did you overcome it. Explain your learnings also.

• Explain the insights which you have discovered from data, basically explain the entire EDA you did.

• Explain the feature engineering pipeline. Specifically explaining how were the features distributed(normal, skewed, peaked) and how the methods you applied to tune the feature helped you in getting a good model performance

Give all the methods you applied like scaling, balancing the dataset, outlier treatment, multiple categories, encoding. This will make the interviewer ask questions only what you know ultimately getting more chances of selection.

• Explain the feature selection and which features were highly impacting to predict the target. Explain the domain study and the challenges you faced. Explain how you overcome the challenges.

• Explain how you created the model and explain all algorithm you used and how they performed. Explain what metrics you used to evaluate the model performance. What hyperparameters have been tuned?

• Explain the retraining if it was required.

• Model Deployment Strategy. Explain the platform used end to end. Explain the deployment strategy How did you store the predictions and how you are showing them on the front end.

If you are able to explain all these things, the interviewer will be more interested in asking questions based on what you have done.
Explaining the project will give interviewers an impression about your knowledge and ability to deal with challenges.

Also, you need to focus on preparing a resume that briefly explains your education, experience. Along with this make sure that resume is done such that it matches the job requirements. Hence it’s advisable to have a unique resume for every job application.

Prepare a GitHub repository where you should put all your code and mention this account link on the resume.

Explaining your project should be like storytelling where you have to tell each and every step you have done.