A Step-by-Step Guide For Building A Data Science Portfolio
A few weeks ago, we wrote an article about the skills needed to be a data scientist. Besides the benefit of skills, a portfolio is a must to land you an interview. For this article, let’s define a portfolio as public evidence of your data science skills.
But Why A Portfolio?
If you have a degree in artificial intelligence or a related field from a top-tier school, it is comparatively easy to get a data science job. Employers trust that you can add value to their organization because of the prestige of the institution that issued you the degree because it is in a subject that is relevant to their work. However, if the degree is not from a top-tier school, building trust is a must, and you can exhibit your passion and specific knowledge to the employers.
In the following section, we will dive straight into the core steps that will use to build a data science portfolio concisely. At the end of this article, we hope to give you a better understanding of how to build your data science portfolio with direction to landing your dream job in data science.
The Three Steps:
1. Get an Internship or Work Experience While Studying
Internships are always a great approach to get your foot in the door at a company you wanted to land as a data scientist. Employers often look for students or fresh graduates with some masters in data science work. Most importantly, they want to hire someone who can start working on real stuff immediately with minimum training time.
You will get the opportunity to work as a data scientist intern, data analyst intern, data intern, and other similar positions. These skills are highly transferable to any data science jobs in the market. Regardless of your academic background, the internship is a boost to your data science portfolio. Stay tuned for our next article on the best practices to land one of these elusive opportunities while at university!
2. Work on Personal Projects (Through Kaggle or Hackathons)
Your data science portfolio should consist of a number of projects that showcase your job-relevant skillset and knowledge. Again, the purpose here is to demonstrate you can do the task, so the more your portfolio looks like the day-to-day work of the jobs you’re applying for, the more impressive it’s going to be.
We recommend you not to add some random task to your portfolio or resume. “Solve a difficulty that relates to the organizations in which you’re interested.” Co-founder Refeal ‘rafi’ Zikavashli.
Participate in Kaggle competitions and Hackathons. Both are arguably the most popular platform for various data science projects and competitions. Not only will you learn a lot from such virtual competition, but it can also showcase your knowledge in data analysis and model development and optimization with different machine learning and artificial intelligence techniques.
Add experience to your portfolio, enroll yourself at the next exciting hackathon event at Hackmakers!
3. Social Media Presence
Share your work and projects with others via social media. After participating in hackathons and other competitions, never forget to share your journey. Be active on social media platforms so that various recruiters can notice you and your work. Linkedin, Github and Medium are the best places to post your successes, your work, and to showcase your other achievements.
While creating this presence, don’t forget to focus on your branding- your beliefs, your story, and experiences that demonstrate expertise and authority in your niche. Position yourself by leveraging social media and let it speak for yourself. As always, remember to value others while sharing your experiences.
CONCLUSION
We hope this would be beneficial to you and to those around you, who are trying to build their data science portfolio. As long as you are passionate about the work you do, you will surely land a job in this fast moving field. Good luck in your data science career, and happy learning!
