If you’re one of the many data science students who are about to enter the workforce, congratulations! You’ve worked hard to earn your degree and you’re about to embark on a very exciting career.

There are a lot of different directions you can take your career in data science. To help you get started, I put together a list of 10 ways you can make the most of your data science degree.

1. Stay up to date with the latest trends, but pick one niche. The field of data science is constantly evolving, so it’s important to stay up to date with the latest trends. A few ways to do this: participate in both academic and industry conferences, find meetup groups that have to do with the field of machine learning you’re most excited about, follow good content sources (like TDS). It’s extremely important that you specialize in some of the several areas involved in data science, although having a good foundation on the horizontal you should not try to learn everything about anything, pick one class of problems (e.g. NLP, Finance, MLOps, Data Engineering) and go deep on that.
2. Get experience working with data. One of the best ways to learn about data science is to get experience working with data. There are many ways to do this, such as taking on internships or participating in data science competitions. You can also find datasets online and try to analyze them yourself. Kaggle could be your friend here.
3. Develop your coding skills. Data scientists should not be expected to be as advanced in coding as hardcore backend engineers, however, coding is a vital skill. If you don’t have much experience coding, now is the time to learn. There are many resources available online, such as Codeacademy and Udacity. In addition, most data science programs offer courses in coding. If you’re familiar enough with coding, then start looking for specific courses on machine/deep learning.
4. Learn statistical techniques. Statistics is a key part of data science. If you want to be a successful data scientist, you need to be well-versed in statistical techniques. There are many resources available to help you learn, such as online courses and textbooks. Not only do statistics play a key role in many algorithms, but is also a useful skill for exploring your data and/or comparing models in production.
5. Develop your machine learning skills. Machine learning is another important part of data science. If you want to get ahead in your career, it’s worth taking some time to learn about machine learning algorithms and models. We are fortunate enough to have a few mature ML frameworks that are open source and have plenty of documentation to support you. By using frameworks like PyTorch, Tensorflow, Lightning, etc, you may not learn how to create your own machine learning model from scratch, but unless you’re creating the new GPT-3 this will not be a problem. There are many problems that these tools can solve. If you want to go deeper in ML, start reading the framework’s codebase to familiarize yourself with the style of software architecture and programming style.
6. Learn about data visualization. One of the most important skills for data scientists is data visualization. Data visualization allows you to communicate your findings to others in a clear and concise way. If you want to learn about data visualization, there are many resources available, such as online courses and books.
7. Get experience with different data types and sources. Data scientists need to be able to work with different data sources. Especially if you want to make a career out of it, you most likely will not receive ready-to-use datasets in CSV. In the real world, the data will be stored in some cloud storage, database, or even feature store. Learn feature-engineering techniques and try to get experience working with different types of data, such as text data, audio, video, and tabular data.
8. Develop your communication skills. Data scientists need to be able to communicate their findings to others. This means that you need to develop your communication skills. One way to do this is to practice presenting your findings to others. A good data scientist is a good storyteller. When working for companies you will learn that it’s not straightforward to communicate with people that are not in your team and need to be updated, so if you expect them to understand what you’re doing it’s your responsibility to communicate properly.
9. Find a mentor. A mentor can be a valuable asset for any data scientist. A mentor can help you navigate the data science field, offer advice on your career, and provide guidance on your professional development. If you’re not sure how to find a mentor, you can start by asking your professors, colleagues, or friends if they know someone who would be a good fit.
10. Get involved in communities. There are many professional organizations for data scientists, such as the American Statistical Association and the Institute for Electrical and Electronics Engineers. Joining a professional organization can help you stay up to date with the latest trends in data science and connect you with other data scientists. Join competitions on Kaggle, although they do not necessarily represent the exact dynamics of a professional environment, they usually will help you to face some challenges that you would in the real life. Get involved in Discord servers, Reddit, stackoverflow, etc.

Working as a data scientist is an amazing thing, you will have the opportunity to apply state-of-art techniques to solve real-world problems. It is different from any other technical work that I’ve seen so far, mainly because you’re not only able to solve complex problems with code and algorithms, but you are also able to experiment with your solutions with real subjects and clearly see direct results for the companies that you’re working for.

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Originally published at https://medium.com on March 30, 2022.