Data science is considered one of the hottest careers in the technological era.
It has captured the imagination of businesses worldwide due to its ability to predict business trends and even the stock market.
However, it appears to be a challenging field to break into. Finding a job is difficult, but making a portfolio is more challenging and requires effort.
Nevertheless, if you follow the tips and strategies, you can easily make your relevant public profile. These public profiles can be your Github, website, or LinkedIn. Thus, creating a portfolio to land a high-paying job or develop freelancing business opportunities is important.
What is a data science portfolio?
A data science portfolio is simply a collection of projects and relevant works.
It reflects your brand, includes your GitHub, Kaggle, website, LinkedIn, etc. It adds context to your resume and gives employers a better idea of what you can bring to their organization.
It works on two goals:
- Highlight your expertise/ Knowledge
- Show that you’d be a good fit to work /Teamwork
While a resume matters, having an impressive portfolio/ public evidence of your data science skills can do wonders for your prospects. Even if you have a referral, the ability to show potential employers what you can do rather than simply telling them is important.
This guide tries to cover everything you need to create an impressive and killer portfolio to land your dream job.
Why should you create a portfolio?
You might be wondering why you need a portfolio or public profile. After all, isn’t a resume itself enough? The simple answer is no. People no longer look at your CV and decide whether you’re a good fit for an internship or a job. Besides, many fresh graduates or beginners are unable to find relevant jobs. This is because they do not have a data science portfolio.
Creating a successful portfolio can be challenging for beginners. A data science resume is not the same as a software engineer resume or any other resume. Before writing a data science resume, you should prioritize the role since it should be customized according to the job description. In addition, the role should be crafted so that it allows you to showcase your data science skills and the projects you’ve worked on.
Tips for creating a data science portfolio:
Candidates should actively participate in projects and be able to demonstrate their skills to stay ahead of the competition.
A well-curated highlight of your best work has the potential to catch an employer’s attention. So if your data science skills are polished, it’s high time to put together a portfolio to show a hiring manager that you’ve got what it takes.
Keeping this in mind, here are some fantastic tips for developing an impressive data science portfolio.
- Showing expertise in projects:
The first thing on your agenda should be project works. As you know, projects are substitutes for experience. So start doing as many small projects to nurture your desire to learn. Begin with working on popular datasets like Boston pricing, Boston, etc. After that, move on to larger projects such as a recommendation engine, HR analysis, etc. These datasets can be found on Kaggle. You can later share these projects on platforms like Github and Kaggle.
- Create an active Github profile:
Github is a popular platform for programmers and many data scientists who use it to showcase their works and collaborate with other like-minded people. A GitHub profile is an excellent resource for filtering resumes.
- Maintain an active GitHub profile and include the link to it on your resume.
- Regularly participate in this platform by contributing, reporting bugs, and pushing your current projects.
- Every project should be documented with links, cover images, and detailed descriptions.
- Pin the best projects
- Create a Kaggle account:
Creating a Kaggle account will help you get noticed by recruiters faster. In addition, most data science competitions are hosted by Kaggle. These competitions help me learn more about tips and techniques while dealing with different data.
- Participate regularly in discussions, learn from experts, and seek assistance.
- Focus on releasing your best work, complete with detailed descriptions and high-quality code.
- Be active by using new datasets and creating data analysis and ML models
- Use web scraping tools to publish your datasets
- Include your Kaggle URL in your resume or CV
- Showcase your communication skills:
Employers will certainly want to know your coding and analytical skills for data science jobs, but they will also look at your communication and storytelling skills.
Communication is an essential aspect of data science in the workplace.
- You can show your communication skills by combining portfolio samples with an attached story on the piece of work you’ve done.
- Alter your resume according to JD:
A resume is the first and main part of job applications as it decides whether or not you’ll get shortlisted. Usually, recruiters spend no more than a min skimming through your resume. Hence, it is critical to make it eyecatching and on a single page. Besides this, It is important to go through the job description first and alter your resume accordingly. When writing a data science resume, Keep the following things to consider: Summary, Skills according to JD, Projects, Relevant Experience (if any), portfolio, and social media links.
- Make your portfolio website.
Creating a personal website can be highly beneficial for your data science portfolio. If you’re not a developer, you can make a website using Wix/Weebly. Your website will greatly influence people who come across your profile. Additionally, It will improve your skills while allowing them to see your projects and work in the field.
- Be active on Social media accounts:
- LinkedIn: LinkedIn has become one of the popular and go-to places for building professional profiles. This allows you to connect with like-minded people from all over the world. However, keep in mind that, It is the first place people look for your public profile. Hence, it is essential to prioritize making and optimizing your LinkedIn profile.
- Twitter: Most data science experts are quite regular on Twitter space. You learn about the projects these people are working on and their opinions on social issues. In addition, it is a great place to promote your blogs, videos, and other discoveries.
The majority of data science certifications or Bootcamp you enroll in require you to complete several projects. While watching tutorials can be interesting, you should also be able to work on different datasets to put your newly acquired knowledge and skills to the test. There are several resources available where you can download data and create projects for your portfolio.
Data science courses in Delhi are one of the best learning platforms to build varieties of projects from multiple domains like banking, healthcare, marketing, etc.
I hope you found this article helpful in developing a data science portfolio.
A data science portfolio is a quick way to establish your trust with a hiring manager and demonstrate that you’re qualified for the position.
If you are interested in learning more about data science, Learnbay offers data science certification courses in Delhi. They are comprehensive yet concise, assisting you in establishing a solid foundation of work to showcase to land a good job. You will learn various data science concepts and tools such as data wrangling, machine learning, and so on through live sessions. You will also have the opportunity to work on portfolio-worthy industry projects.