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Lorena Ciutacu
Lorena Ciutacu

Posted on • Originally published at lorenaciutacu.com

5 ways to keep your skills fresh after finishing a coding bootcamp

One year ago at this time, I was nervously making last-minute changes to slides for my final project of a Data Science Bootcamp.

Today, I work as a technical writer at a startup that is developing a low-code workflow automation tool. Though in this role I don't use my data science and Python skills on a daily basis, I still apply them occasionally in data analyses and personal projects.

In this post, I'll share with you five tips for maintaining and even developing your coding skills after you're done with formal education.

1. Improve your school projects

Bootcamps are fast-paced. So much so that you might barely complete some projects before the deadline and if you somehow do, they still won't be perfect. There will always be things left to improve, and you should take the time to do them.

One way to improve your projects and coding skills is to try new models and libraries. For example, if you did classification with logistic regression, try also with random forest; if you used Tensorflow, now try Keras; if you scraped a website with BeautifulSoup, now do it with Scrapy. You get the point.

2. Work on new projects

Even with all the mandatory projects you'll need to complete in the bootcamp, you'll probably get a lot of ideas for others. After the bootcamp is the time to explore them!

Ideally, work on real-life projects or some that have business value for the field you're targeting. There are many data sets available for marketing, finance, medicine, and other fields. Find a relevant data set and apply different models to derive insights from raw numbers. For more ideas, check out the Kaggle data sets and competitions.

3. Code regularly

What you do every day matters. Small things add up: actions turn into habits turn into skills. That's why it's important to code regularly. It doesn't have to be a complex project, even a 15-minute coding session or a short exercise counts.

For example, you can block one hour every Saturday to practice algorithms and data structures on LeetCode, Codewars, or HackerRank. You'll not only sharpen your coding skills, but also get a confidence boost as you progress through levels and get badges.

4. Keep on learning

In data science, machine learning, and AI, research and applications are advancing fast! New papers, models, libraries, and business applications are coming out almost every day. That's why it's important to keep up with the news and advances in the field.

There are many resources for this. You can read blogs (like Towards Data Science and Data Science Central) and papers, watch videos (for NLP enthusiasts I recommend the YouTube channel AI Coffee Break with Letitia), listen to podcasts, and take online courses.

5. Document your learnings

I find the best way to learn something is by teaching it. Explaining something to others helps you structure the learned information and identify problems or issues that are unclear.

To document your learnings, you can create a blog on Medium or dev, where you write about your projects. If you're into web development, you can even build your own blog (I made mine with Jekyll).

With these five tips in mind, keep on coding and learning!

Discussion (2)

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steelwolf180 profile image
Max Ong Zong Bao • Edited on

Read books like Designing Data Intensive Applications or Machine Learning with TensorFlow 2nd Edition you get tap to on the knowledge or advice at a fraction of the cost.

For the amount of time they had spend in researching and creating the book after multiple revisions.

It's much better than just a article and if you pick the correct writer and expert. You take on their thinking caps on actual problem these people has been there and done that before data sci was cool.

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lorena profile image
Lorena Ciutacu Author

Thanks for the recommendations, books like these are definitely useful as well.