Data science is the study of comprehending, analysing, and applying current technologies and approaches to select meaningful data and build procedures for making major business choices. A data scientist’s job has become one of the most sought-after jobs in the world.
Gone are those days when employers would look at your CV and judge whether you were a good fit for an internship or employment. People are now using the entire projects and profile (Portfolio) to shortlist applicants in the technology sector, particularly for Data Science. However, as corporations have begun to provide paid online projects, practically every student is now involved in internships and projects (unpaid/paid/voluntary).
A portfolio is crucial since it can assist you in getting work and the benefit of learning from it. Let’s define a portfolio for this purpose as public evidence of your data science abilities. Data science is a vast field, so it isn’t easy to know what types of projects hiring managers are looking for.
As a result, candidates need to take the initiative in completing projects and demonstrate their abilities to be considered for a position. By displaying, I mean that you should create your own brand. When someone looks at your data science portfolio, one should obtain a clear picture of your previous work, interests, and successes and be eager to speak with you.
Tips for Creating a Fantastic Data Science Portfolio
1. Have a Github profile that is active.
GitHub allows you to share a far-flung version of your project with others, allowing them to see it and perhaps collaborate on making it better. Maintain a dynamic GitHub profile and provide a link on your resume. By “active profile,” I mean that you must work on it regularly because your daily contributions are recorded and visible to readers. Also, to customise your homepage, prepare a readme.md for your profile.
2. Start Using Kaggle
It is important to have a Kaggle account. Not only to show off your abilities but also to put them into practice regularly. Many firms, such as ZS Analytics, Bain & Co., KPMG, JPMorgan Chase, and others, host Data Science Competitions similar to Kaggle.
Apart from that, the Kaggle learning competitions assist in learning more about the approaches and ideas to use while dealing with various types of data online. Kaggle is also a fantastic place to show off your abilities. You can earn medals and titles like Kaggle Grandmaster that will have a big impact on your LinkedIn profile if you show them on the headline. On your CV, you can also include a link to Kaggle.
3. Take part in hackathons and competitions
Competitions and hackathons allow us to hone our skills and understand our place among our peers. You can participate in various online platforms and use the achievements in contests and hackathons to boost the credibility of your work. You can also learn new and better methods by viewing the Top Approaches of any tournament.
The hackathon is a highly sought-after chance for Great Learning platform students to put their knowledge to the test in a time-constrained setting.
Several firms hold recruitment hackathons to weed out top performers. Thus hackathons are a surefire way to improve your chances of making a career change.
4. Use HackerRank to practise questions.
HackerRank is an excellent resource for honing your Python skills. HackerRank has questions that will assist you in improving your programming abilities. It also awards stars based on the number of points you get for accurately answering the questions. To demonstrate their proficiency in Python or Data Structures or Algorithms, include ( HackerRank 5 star) in their LinkedIn title.
5. Read Blogs
Reading blogs keeps you up to date on current events in your field. They might also come in handy during the interview’s talks. In addition, you can utilise blogs to master new skills. Reading blogs about personal experiences might help you understand more about the sector and what you should do in the future to obtain a suitable job.
6. Make your portfolio website
Create a basic portfolio website. You can make one using Wix/Weebly or by coding in HTML. Once your website is up and running, make sure to include it in your resume. Your website will greatly influence an employer who sees your profile. It will improve your skills and provide them with the opportunity to see your projects and fieldwork.
7. Make a LinkedIn profile
Everyone should have a LinkedIn profile. This allows you to engage with people worldwide, working in fields similar to yours. LinkedIn can also assist you in publicising your work. Many recruiters increasingly contact candidates for open positions in their companies using LinkedIn’s recommendation system. Follow hashtags in the DS and ML-like fields as well.
8. Do small projects
Start with well-known datasets such as Boston Pricing, XOR, Iris, MNIST, and others. After that, move on to larger tasks such as a recommendation engine, a comprehensive analysis of some data, and so on. On Kaggle, you can find datasets. Some project ideas include HR analytics, customer segmentation, image analysis, etc. Feel free to create your own dataset and analyse it.
9. Deploy code
After you’ve created a project, try deploying it on Heroku, AWS, or another cloud platform. This enables you to create a fully functional data science application. For example, if you’re building a movie suggestion engine, you can use Heroku or AWS to create a website where individuals can come and choose movies they want to see. Your algorithm will predict which movies they should watch based on their preferences. This code deployment impresses HR a lot and will almost certainly help you get an interview.
10. Emphasize community development.
The tactics listed above can undoubtedly assist you in creating a fantastic profile, but knowing about opportunities is equally crucial. Join a community and form strong bonds to achieve this. LinkedIn, Slack, Discord, and Telegram are some platforms where you may connect to a set of data scientists that publish messages about possibilities that you can take advantage of daily.
Conclusion
Employers may consider you based on your portfolio if they believe you can contribute to their continued growth. Getting a data science job becomes straightforward if you have completed or trained in the data science programs or a related topic from an online or offline institution. The beautiful thing about data science is that the work you perform on your own projects typically resembles the work you’ll do once you’ve been employed.
Now that you know how to create a portfolio, you must decide what to include in it. You should have a few projects on Github or your blog, where the code is visible and well-documented, at the very least. The more easily a hiring manager can locate these projects, the easier it will be for them to assess your abilities. Each project should be as well-documented as possible, with a README file that explains how to set it up as well as any data peculiarities.
