Previously, when I was studying at university, I wanted to go into the Data Science field so badly.
I kept on study a lot of statistics with the hope that I could ace my data science interview.
However, I kept failing.
After that, I spent some time, trying to recall is there any common areas that most of the hiring managers are looking for.
I figured out that I actually do not have a nice data science project to showcase.
Hence, I spent some time, tried to craft a data science project, and thought I was going to succeed this time.
However, I still failed. Because I did not pay attention to some of the details of the project.
After making improvements to my projects again and again, I finally managed to secure a data science internship.
In this article, I will walk you through some of the improvements I made that led to a data science internship opportunity.
1. Understand your machine learning models
You may think that implemented some fancy kind of models will increase your chance of scoring an Interview. In fact, without really understanding your model, it actually will hurt you.
For instance, you implemented a very complicated deep learning model. However, you do know why it works. You just clone other people’s work from GitHub and got a high accuracy score.
Then, when the interviewer asks you to explain your implemented models, or in which kind of scenarios, your model will not work well, you will be stunned and leave the interview.
Here is the message I want to share.
You need to prepare some questions about your models that hiring managers will question you. If you do not have much time to prepare for your data science interview, but at least, make sure that you understand the models you used clearly.
Below are some examples of questions that you can prepare.
- Do you have any benchmark performance to compare?
- In which circumstances that your model will not be able to give accurate results?
- If you have more time, what kind of improvement you will make to your model?
You can either include this as a side note on your presentation slides or markdown file which you will be presenting. Either way works!
2. Keep your analytics concise
There will always be some insights that you could find interesting when you are doing your project. You will spend some time, plot different kinds of graphs, and hope to impress the hiring manager.
Plotting some graphs are great. However, do plot some important graphs. For example, plot some graph that is related to your result or the features for your model, or the distribution of each category in your training examples.
One thing to note is also to prepare a story for your plot or analysis.
For instance, don’t just say, the sales spikes in May. Instead, you can say the conversion of the sales is much higher across each channel during May, and thus it leads to spikes in sales.
When you find some interesting findings, spend some time to find out the reason behind it.
3. Explain why do you choose those features
Features are extremely vital to our machine learning models. Having the right features, you can produce excellent accuracy without using complicated models.
There will be various features you can choose from. Explain the underlying reason for choosing the subset of available features will show that you know the importance of feature extraction in machine learning. In other words, you are not taking random inputs, and throwing them to the models.
Besides, you may want to include the process of choosing the right feature. For instance, you are trying to predict the housing price. The features you are considering to use include, the minimum distance to the nearest public transportation station, the number of bedrooms, the size of the house, etc.
Let’s say you find that the minimum distance to the nearest transportation station is not an important feature. It hurts the performance of your model. Do include this in your data science project report. Data science is not all about the final result, sometimes, it is the process of finding the solution that matters.
4. Prepare Both Powerpoint slides and Jupyter Notebook
You may ask, why do we need to prepare both?
I don’t have time to prepare both, I thought presenting the code to the interviewers would be enough?
It depends actually on the situation you have. For me, it is better to have both. For instance, while you are presenting using PowerPoint slides, the interviewer interested to know how you process your data and for some reason, you did not put this information on the presentation slide.
If you have prepared your code with markdowns and comments, you will be able to show him or her on the spot.
Another scenario would be you only prepare Jupyter Notebook and expect to explain the code to the interviewer line by line. Base on my experiences, this is not a good way to present. The interviewer just wants to know how you tackle the problem, therefore, summarize your thought process in PowerPoint will be a better idea.
Hence, my suggestion is to use PowerPoint as the main presenting material. At the same time, make your clean and tidy jupyter notebook available as the supporting material.
Final Thought
Having a great data science project to showcase is definitely one of the most important factors to stand out among other candidates.
In the process of preparing the project, you will find that there is a lot of work required to put in. While you already put in a lot of effort into it, don’t overlook some of the important details as it might ruin your presentation.
All these tips are provided based on my experience. If you have any questions or other different thoughts, feel free to comment!
Thank you for reading till the end, and see you in the next post!
About the Author
Low Wei Hong is a Data Scientist at Shopee. His experiences involved more on crawling websites, creating data pipeline and also implementing machine learning models on solving business problems.
He provides crawling services that can provide you with the accurate and cleaned data which you need. You can visit this website to view his portfolio and also to contact him for crawling services.
You can connect with him on LinkedIn and Medium.
