Six Ways to Get More Exposure as an Aspiring Data Scientist | by Terence S | Nov, 2020
If you don’t know what Kaggle is, I extremely suggest that you simply take the time to discover it and see what it has to supply. In my opinion, Kaggle for Data Scientists is like Leetcode for Software Engineers.
Kaggle permits you to showcase your data science tasks, your underlying code, and the way lively you’re! Here are a few methods which you could make the most of it:
Compete in competitions
In my opinion, there’s no higher manner of exhibiting that you simply’re prepared for a data science job than to showcase your code by way of competitions. Kaggle hosts quite a lot of competitions which entails constructing a mannequin to optimize a sure metric.
Two competitions which you could attempt proper now are:
Create and share datasets
In order to be a very good data scientist, you’ve gotten to have good information to begin with! Creating datasets by way of net scraping or different means and sharing these datasets with the remainder of the group is an effective way to apply supplying clear and usable information to use.
Conduct EDA and construct fashions to share with others
Perhaps one of the best a part of Kaggle is that there are millions of datasets for you to discover and construct fashions. Not too way back, to give an instance, I construct an extraordinarily easy suggestion system for cooking recipes utilizing pairwise affiliation. I additionally took benefit of one of many coronavirus datasets to see how the unfold of COVID-19 advanced for the reason that starting of the yr (test it out right here.)
The second factor that I like to recommend is that you simply create a GitHub repository to retailer all your code and data science tasks. Why?
- It’s good apply to use a model management system, like GitHub, to arrange your code and tasks — each single tech firm will anticipate you to be accustomed to GitHub.
- In parallel with studying how GitHub works, you must also learn the way to use Git (which might be one other talent that each hiring supervisor will anticipate you to know!)
Therefore, constructing good practices with GitHub and Git, and displaying that by way of your organized GitHub repository is an implication to hiring managers that you simply’re already skilled in these areas!
Yes, I’m biased, however hear me out. You’d be shocked what number of data-related professionals are on Medium. They like to see informative, insightful, and fascinating materials. Take benefit of Medium to weblog about your learnings, to clarify a fancy subject in easy jargon, or to stroll by way of your data science tasks!
Specifically, I like to recommend that you simply write for the publication Towards Data Science, as they presently have a follower base of virtually 500,000 followers.
If you’d like some inspiration, try my undertaking walkthrough on Wine Quality Prediction.
Recently, I got here throughout a resourceful article written by Susan Currie Sivek, which gives a number of organizations the place you’ll be able to alternatives to work on real-life data science tasks.
If you’re attempting to discover extra experiences to add to your resume, I extremely suggest that you simply verify this out.
Speaking of resumes, just be sure you sharpen your resume and your LinkedIn profile in order that it highlights all your work, achievements, and contributions.
Specifically, I like to recommend that you simply think about the next:
- Add a piece in your resume referred to as “Personal Projects” or “Data Science Projects”, the place you’ll be able to clearly outline the issues that you simply tackled, the way you approached every downside, and what the outcomes have been.
- As properly, just be sure you have a piece that highlights the abilities and instruments that you’re proficient in, like Python, SQL, Pandas, and so on…
- If you’ve been profitable in any data science competitions, make certain to embrace these as properly.
- Lastly, make certain to embrace all the issues that I talked about earlier on this article, like a hyperlink to your Kaggle profile, GitHub profile, and/or your private web site.