5 ways to keep your skills fresh after finishing a coding bootcamp
Tags: datascience, career, beginners, python
Published: 2021-11-28 09:37:25

One year ago at this time, I was nervously making last-minute changes to slides for my [final project]([https://github.com/lorenanda/speech-emotion-recognition](https://github.com/lorenanda/speech-emotion-recognition)) 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](https://www.tensorflow.org/), now try [Keras](https://keras.io/); if you scraped a website with [BeautifulSoup](https://beautiful-soup-4.readthedocs.io/en/latest/), now do it with [Scrapy](https://scrapy.org/). 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](https://www.kaggle.com/) 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](https://leetcode.com/), [Codewars](https://www.codewars.com/), or [HackerRank](https://www.hackerrank.com/). 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](https://towardsdatascience.com/?gi=73ee6fa159ba) and [Data Science Central](https://towardsdatascience.com/?gi=73ee6fa159ba)) and [papers](https://arxiv.org/list/stat.ML/recent), watch videos (for NLP enthusiasts I recommend the YouTube channel [AI Coffee Break with Letitia](https://www.youtube.com/c/AICoffeeBreak)), 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](https://lorenaciutacu.medium.com/) or [dev](http://dev.to/lorena), where you write about your projects. If you're into web development, you can even build your own blog (I made mine with [Jekyll](https://jekyllrb.com/)).

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