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Boost your Data Science career with 7 simple actions

Data science illustrated with women on laptop

ARTICLE SUMMARY

Darya Petrashka, data scientist, shares her tips on how you can super-charge your data science career.

There are many ways to level up your technical career, and it is not just doing research and accomplishing work tasks. Let’s have a look at several easy activities that will bring you to the next level.

BLOG POSTING

You don’t have to be a professional writer to start a blog: it is not a novel, just technical writing. Don’t know what to write about? Then, think about it: when you studied a new topic or gained a new skill, or got certified, how would you guide a past self? For example, you learned how to write unit tests and successfully covered your project. Or maybe you faced dirty code issues, stuck, and then had to rewrite many modules? Describe your experience as like you are talking to a past self. Many people are in a place where you have been 3, 4, or 6 months ago!

Blogging has the advantage of not only helping people, but it helps you as well! To create a post, you need to make a plan, structure your thoughts, search for the corresponding terminology, write sample code, and finally test it. By doing so, you memorize better, gain a better understanding of the concept, and do additional research.

Hint: As a platform, you can use your personal website (consider creating one on GitHub Pages) or pick a blogging platform like medium or dev.to.

SOCIAL NETWORKS ACTIVITIES

This one is far easier than blogging: just share your progress with the world. Whether you take a course, participate in a hackathon, or find a solution for a complex issue, share it. This will help to find like-minded people and make you visible in a community. 

Hint: Want to learn new technology? Look for a corresponding group on Meetup. Want to find a team for a hackathon? Consider LinkedIn or Twitter. Want to be a big community part? Find it on Slack.

CONTRIBUTING TO OPEN SOURCE

It provides you with a way to improve your technical skills. A huge advantage is that you can choose a project corresponding to your interests and it’s completely voluntary: no deadlines or pressing. 

Too scared to contribute? Search for ‘good first issue’ on GitHub. There are many easy fixes specially tagged for first-time contributors. Pick up one, clone the project, and start working on it.

Hint: Another good idea is to start from the project documentation: check if it is complete, accurate, and provided in all languages you can speak (yes, translations will also be a big help).

MENTORING

The best way to learn something and progress fast is to teach someone. Consider helping a person who is one step back from you in a given career path or technology. You still remember how it was, but already have useful pieces of advice. 

Hint: You can find mentees on your work or via social media (if you are active enough, most probably they will ask you about mentorship). There is also a good free platform, the mentoring club, where you can find a mentor and become one.

DOING INVITED TALKS

Public speaking is a crucial skill for Data Scientists. You need to know both the topic and your auditory. While preparing a talk, always keep in mind the attendees’ technical level and talk timeline. The same topic could be presented from different angles for non-technical managers, teammates, or other aspiring Data Scientists.

How to find a speaking opportunity? Keep an eye on announcements of Data Science events, their websites often include a ‘Call for speakers’ page. Most probably, organizers will include a description of the desired talk: time limit, level, and possible topics. Information if the event is open for first-time speakers can also be found there. 

Hint: Feel worried? Consider practicing public speaking in front of a smaller group of non-strangers and ask for their feedback. This will help you to calm down and act more naturally during bigger events.

NETWORKING

Networking is a powerful tool and you need to learn how to use all its potential. You can interact with people during both offline and online events. Try to share your background, interests, and pet projects, so you can find a possible match. Listen to your conversational partner carefully, relax, and enjoy the interaction.

Hint: If there is no match at the moment, consider connecting to them on social networks. Later, when you need a domain expert in a complete stranger field, you will know whom to refer to.

TAKING PART IN DATA SCIENCE RELATED EVENTS

Nowadays, there are plenty of Data Science events! Offline and online, located in different countries, dedicated to different levels, technology or business focused – there is definitely what to choose from! There you can learn the latest industry news, network, or meet a potential employer.

For inspiration, check these upcoming events of Women Who Code or search for Data Science on eventbrite. You may also find it interesting to attend the annual global Open Data Science Conference.

Hint: before going to an in-person event, share about it on social networks, so you can meet more people there (and do some great networking!). You can also share your insights and takeaways afterward.

You may think that all mentioned activities are time-consuming and difficult. But in reality, you don’t have to do all of them – it’s up to you to choose. Secondly, you can do several activities simultaneously: find a cool open-source project, study it, contribute to it, and describe your experience in a blog post. Or give a talk. Then share your thoughts on social media. The more you do, the more topics you have.

And for sure, there are plenty more ways to shine in your Data Science career! We didn’t even mention participating in hackathons, doing certification, or learning at school/bootcamp/courses. Ways are different, but the main goal is to make your work and impact visible, and this is as important as your technical skills.

About Darya:

Darya Petrashka, data scientist

AWS Community Builder, works as a Data Scientist at SLB. She is passionate about data and its usage for problem-solving. The area of interest includes classical ML and NLP, as well as working with AWS services.

An eternal student, she likes taking part in online schools, courses, and workshops. She shares insights on her Linkedin page and medium blog.

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