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Six Ways to Get More Exposure as an Aspiring Data Scientist

Learn how to market yourself and stand out from the rest of the crowd

Photo by Rupert Britton on Unsplash
Photo by Rupert Britton on Unsplash

Introduction

No matter how much you learn as a data scientist, if you don’t market yourself, you will be faced with limited opportunities.

To give an example, Sony has built one of the best phones from a technical standpoint, with a 4K screen, expandable storage, a tool-less SD card tray, a headphone jack, and more. And yet, it has a market share of basically zero percent, so what gives?

One of Sony’s main problems is that they have a poor marketing strategy, building the hype for a new model, only for the model to release several months later when the hype dies.

My point is…

A portion of your time should be spend marketing yourself in conjunction with learning Data Science so that you can increase your chances of being known to those with opportunities for you.

I’m not the most technical or knowledgeable data scientist, but because I’ve marketed myself, I’ve received several opportunities from professors, startups, and even the Singaporean government!

So with that said, here are six ways that you can increase your exposure as an aspiring data scientist.


1. Use Kaggle!

If you don’t know what Kaggle is, I highly recommend that you take the time to explore it and see what it has to offer. In my opinion, Kaggle for Data Scientists is like Leetcode for Software Engineers.

Kaggle allows you to showcase your data science projects, your underlying code, and how active you are! Here are a couple of ways that you can take advantage of it:

Compete in competitions

In my opinion, there’s no better way of showing that you’re ready for a data science job than to showcase your code through competitions. Kaggle hosts a variety of competitions which involves building a model to optimize a certain metric.

Two competitions that you can try right now are:

  1. Titanic: Machine Learning from Disaster
  2. House Prices: Advanced Regression Techniques

Create and share datasets

In order to be a good data scientist, you have to have good data to start with! Creating datasets through web scraping or other means and sharing these datasets with the rest of the community is a great way to practice supplying clean and usable data to use.

Conduct EDA and build models to share with others

Perhaps the best part of Kaggle is that there are thousands of datasets for you to explore and build models. Not too long ago, to give an example, I build an extremely simple recommendation system for cooking recipes using pairwise association. I also took advantage of one of the coronavirus datasets to see how the spread of COVID-19 evolved since the beginning of the year (check it out here.)

2. Create a GitHub account

The second thing that I recommend is that you create a GitHub repository to store all of your code and data science projects. Why?

  1. It’s good practice to use a version control system, like GitHub, to organize your code and projects – every single tech company will expect you to be familiar with GitHub.
  2. In parallel with learning how GitHub works, you should also learn how to use Git (which is probably another skill that every hiring manager will expect you to know!)

Therefore, building good practices with GitHub and Git, and displaying that through your organized GitHub repository is an implication to hiring managers that you’re already experienced in these areas!

3. Start a blog on Medium

Yes, I’m biased, but hear me out. You’d be surprised how many data-related professionals are on Medium. They like to see informative, insightful, and interesting material. Take advantage of Medium to blog about your learnings, to explain a complex topic in simple jargon, or to walk through your data science projects!

Specifically, I recommend that you write for the publication Towards Data Science, as they currently have a follower base of almost 500,000 followers.

If you’d like some inspiration, check out my project walkthrough on Wine Quality Prediction.

4. Create a Personal website

Creating a personal website is another great way to showcase your data science projects and/or any other achievements related to data science. I especially recommend creating a personal website if you have some experience with HTML, CSS, and JavaScript, so you can show that you’re well-versed in multiple coding languages (JavaScript can definitely come in handy for data scientists!).

5. Non-Profit Opportunities

Recently, I came across a resourceful article written by Susan Currie Sivek, which provides several organizations where you can opportunities to work on real-life data science projects.

If you’re trying to find more experiences to add to your resume, I highly recommend that you check this out.

6. Refine your LinkedIn profile and resume

Speaking of resumes, make sure that you sharpen your resume and your LinkedIn profile so that it highlights all of your work, achievements, and contributions.

Specifically, I recommend that you consider the following:

  • Add a section in your resume called "Personal Projects" or "Data Science Projects", where you can clearly define the problems that you tackled, how you approached each problem, and what the outcomes were.
  • As well, make sure that you have a section that highlights the skills and tools that you are proficient in, like Python, SQL, Pandas, etc…
  • If you’ve been successful in any data science competitions, make sure to include those as well.
  • Lastly, make sure to include all of the things that I talked about earlier in this article, like a link to your Kaggle profile, GitHub profile, and/or your personal website.

Thanks for Reading!

Not sure what to read next? I’ve picked another article for you:

3 Ways to Get Real-Life Data Science Experience Before Your First Job

Terence Shin


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