Photo by Christina Morillo
Data science is a new field that has massive potential and growth. According to IBM, the demand for data scientists has grown at a 5% annual rate. And a report by DevSkiller revealed that the need for data science-related tasks has increased by 295%.
However, there isn’t enough workforce to fulfill the growing demand. This means there is low competition, and employers pay well ($118,972 on average) and offer some juicy benefits to secure talent.
If you plan to enter this field, you must have the chops to deliver high-quality work. So, how can you showcase your data scientist skills and abilities? The answer to this question is online platforms where you can publish your portfolio and seize opportunities.
The data science field has become like a hidden treasure everyone wants to get their hands on; more people are leaving their current occupations and starting new careers to become data scientists.
Building a data science portfolio shows your technical and managerial skills as well as the ability to work under different situations or circumstances. Consistently enhancing your portfolio can also lead to learning opportunities.
When your portfolio is ready, you can upload and publish it on the following platforms. You can share your portfolio on more than one website to maximize visibility.
1. Social Media Platforms
As a data scientist, you can significantly benefit from social media that is not just limited to regular activities. Some platforms have become hubs for businesses and employees to communicate and find employment.
You can use social platforms like Twitter and LinkedIn to engage with meaningful content and conversations. LinkedIn also allows you to add your previous employment history and connect to your desired audience.
Take the approach detailed here to build a strong portfolio and create an unmissable presence on social media:
- Always value people through civilized conversations – people who contribute to the community are more likely to get approached by employers.
- Do not focus on promoting yourself, be natural and work with the flow. You do not want to spam as it can affect your credibility.
- Expand your connections, and reach out to new people across industries, regions, and perspectives to get a complete picture instead of staying in your intellectual bubble.
Share your experience and case studies and provide solutions to highlight your profile and reach more people. LinkedIn can also send you customized job alerts.
2. DataCamp Workspace
This cloud-based notebook has the collaboration feature that lets you work with your team, analyze data, and publish your analysis.
Moreover, you can write code and share the insights from your browser while staying connected to your team via VoIP technology to ensure timely communication and publishing of data.
For beginners who need something to work on, Datacamp workspace offers more than 20 preloaded datasets you can analyze and make part of your portfolio. Apart from these datasets, Datacamp has pre-written codes that help you save time by preventing writing the code again and allowing you to report faster.
Data analysis in Datacamp includes using visual representations as bar and line charts for R and Python programming languages. The best part is that Datacamp notebooks are ISO 27001:2017 certified to ensure security and confidentiality.
GitHub is a popular platform for developers and coders to store their code repositories and change them whenever possible. It allows its community members to work together from anywhere in the world, making it an ideal platform for a data scientist.
A data scientist who wants to share their work can use GitHub’s open-source project sharing feature, enabling them to share code and track problems collectively. Using GitHub is free and simple; just follow the below steps to start sharing your data scientist portfolio on GitHub:
- Visit the GitHub website and create a user account.
- Get the essential information on how to use GitHub and its features. GitHub Docs can provide you with basic information.
- Use GitHub pages and upload your site by creating a new public repository with your desired username.
- Use macOS and desktop versions of GitHub, refresh the GitHub page and finish the installation.
- Create an index.html in your text editor for your project.
- When you are done, hit publish.
Kaggle is a popular platform among data scientists and the machine learning community. It allows you to connect with other data scientists and help them solve problems by publishing and sharing notebooks and datasets.
Kaggle has clean and ready-to-work data sets; beginners who do not want to work directly on complicated data sets can significantly benefit from this platform and the community. Kaggle has different categories based on competitions (tasks created by the platform or other well-known organizations):
- Data scientists on an entry level can participate in long-lasting competitions to polish their skills and gain in-depth knowledge.
- The next stage of the Kaggle competition is a time-limited competition where you can prove yourself capable of solving data science problems.
- Examples of organizations that use Kaggle to find the best data scientists are Google and WHO. Their private competitions offer prize money ranging from $25k to $100k.
Kaggle competitions are an excellent way to polish your skills and create a compelling portfolio. And you can find some fantastic data science opportunities when you reach the rank of grandmaster.
5. Set Up a Personal Website
A website is an effective way to store your projects in one place. You can use content management systems like WordPress or Wix to build a website quickly. Adding a blog can help rank your site and bring visibility to it.
Starting a website does not require a huge budget but ranking a website is a slow process. Unlike Kaggle and Datacamp, you cannot get fast growth and opportunities through your website. However, using specific techniques and methods can boost your site’s visibility.
Data science is your key to a promising career – nearly every industry needs data scientists now or in the future. Beginners should focus on providing quality work by using the above platforms and promoting their services to get new opportunities in the data science industry. Even if you are already working as a data scientist and looking for better employment, take help from the above platforms or work simultaneously on all of them to improve your skills.
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed — among other intriguing things — to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.