From Linguistics to Data Science
5 lessons I learned along the way.
Before obtaining my full-time job as a Data Science Analyst, I participated in an interview on the LangBytes show, hosted by João Victor, a fellow computational linguist. In this interview, we discuss the journey from linguistics → computational linguistics → to data science, and lessons learned along the way.
Check it out. No time to watch? More down below!
So, what’s the TL;DW? Here are five takeaways, with a bonus exercise at the end.
1. Balancing Skill Sets
While it’s essential to develop technical skills in math, statistics, and programming, don’t neglect your main area of expertise.
For me, that is linguistics. For the linguists who are reading this, your deep understanding of language is what sets you apart from other data scientists and computer scientists. Don’t abandon your background, leverage it!
Continue to advance your knowledge in your domain to maintain a strong, well-rounded skill set.
2. Permission to Make Mistakes
Coming from a non-technical background, I always wanted my technical work to be perfect. The truth is that the more mistakes you make, the more you learn.
Here was my aha! moment.
After graduating with a BA in Spanish, I decided to take Python courses online. I kept quitting every time I didn’t get the coding exercises correct on the first try.
I was fed up, but not with the fact that I was making mistakes.
I was fed up with my inability to let myself make them.
So, I put a sticky note on my monitor: “IT’S FINE. YOU’RE LEARNING!”
Then I dove back in. Every time my code didn’t work, I looked at the sticky note.
Four hours flew by. I learned more in that time than I had in months of starting and quitting every course I came across.
Give yourself permission to make mistakes. Failure is necessary and perfection is the enemy. Each obstacle offers a unique opportunity for growth.
3. Curiosity
Always be curious.
Remember — you don’t need to master everything! Get exposure to different tools and techniques, as well as the problems they can help you solve.
Staying curious and eager to learn about the latest technologies and methodologies will keep you agile, informed, and equipped with a variety of tools to choose from when approaching new problems.
4. Communication Skills
One of your greatest assets from the humanities is your ability to communicate complex ideas.
In data science, this means communicating the importance of your work by translating your experiments and their findings into insights that are easy to grasp for non-technical audiences and connecting these insights to the bigger picture.
Communication principles to follow:
Know your audience
Simplify complex topics
Practice active listening
If you want a deeper dive into these tips, respond to this email and let me know!
5. Networking and Online Presence
Building a strong network is a crucial component of career development and personal growth, especially in fields like computational linguistics and data science. It allows you to connect with like-minded individuals, share your knowledge, and discover new opportunities. Here are some practical ways to enhance your online presence and build a meaningful network:
Learning something new? Share what you’re learning.
Working on a project in class? Share what you’re working on.
Finish a data-related project? Share a quick overview and how it might be applied in a business context.
It’s not about likes or followers; it’s about the potential impact. You never know if something you’ve shared might help or inspire someone else in their work.
Exercise: Two Birds, One Stone
Here is how you network AND practice communication skills.
Communicating Technical Projects to a Non-Technical Audience
Select Your Project: Choose something you've recently worked on. If you haven't tackled a technical project yet, find a concise online tutorial in an area that interests you and follow it through to completion. It doesn’t have to be too long or in-depth, so don’t sweat this part.
Identify Your Audience: Find a friend or family member who isn’t familiar with the technical aspects of your project.
Prepare Your Explanation. Focus on conveying two key aspects to them:
Impact: Describe how this project or tutorial makes a difference. What problem does it solve? How does it simplify or enhance something?
Relevance: Explain why they should care about this project. Connect the outcomes of the project to something relevant in their life or to broader real-world applications.
Post a Deep Dive of Your Project: After your discussion, share a detailed post on your social networks like LinkedIn or a personal blog. Describe the project, its impact, and its relevance, and ask for feedback from your professional network. If you do this, tag me, I’d love to see it!
Why?
This exercise forces you to not only tackle a technical project, but to take a step back from the code and the math and connect what you are doing to a bigger picture! All while providing you with something to share online to showcase your learning journey and skills.
Good luck!
Thanks for Reading
I hope you’ve found this bonus issue of Dive Into Data insightful. I really enjoyed putting this together for you!
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