9 Platforms for Building a Strong Data Science Portfolio
Getting into the field of data science makes developers and data enthusiasts question about how they can build their career. Becoming an expert in the field requires hours, days, and months of practice and experimentation with various codes, projects, and data already available on the internet. Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To start building projects, developers search for platforms and websites that can help them learn from experts while also collaborating and experimenting with real world applications. To help you get started with building your portfolio, we have listed the top platforms for you to get noticed by recruiters and get hired in data scientist or analyst profiles. These platforms can expand your knowledge about data science while simultaneously helping you put together your portfolio through interactions with experts and building projects. GitHub As a data scientist, building a portfolio on GitHub is one of the several ways to get noticed. Recruiters and hirers are likely to look at your GitHub profile and review your recent projects before asking you questions about the role. As of June 2022, the GitHub community had nearly 83 million developers and at least 200 million repositories. It is probably the largest source for code in the developer community. By pinning your projects on top and building a great profile, you can attract numerous recruiters and desired job roles. Click here to go to GitHub. MachineHack Learn and test your machine learning skills and compete with other developers here. MachineHack hosts hundreds of thousands of data scientists and curates hackathons to learn and compete within the community of developers. It also offers top-rated pocket size courses that can help you perfect your skills in topics of interest such as beginning from basics of reinforcement learning to in-depth technical understanding of mathematics that goes in machine learning. Being a part of this community will definitely help you get noticed, win exciting prizes, and get hired by top tech companies. In addition, taking part in as many hackathons as possible will make your portfolio stand out from the rest. Click here to check out MachineHack. Docker Hub A cloud-hosted Docker repository for open source, scalable server-side applications which is stateless, DockerHub is one of the largest platforms for sharing and storing Docker images. After creating the profile, developers can access official and verified publisher vendored images along with accessing private repositories of container images. Click here to check out Docker Hub. Kaggle Winning Kaggle Prizes is one of the quickest ways to get noticed as a data scientist. There are scientists who hold the Grandmaster position on the charts of Kaggle and receive thousands of dollars for winning each competition. Though it can seem like a big fight to reach to the top, getting to learn from experts and discovering new solutions is one of the largest perks of being a part of this community. Staying active on the platform and participating in discussions and competitions will get you noticed in the data science world. To join the Kaggle community, click here. Fast.ai Founded in 2016, Fast.ai is a non-profit research group that has built a community of developers focused on deep learning and artificial intelligence and has democratised access to the field. The platform includes courses, books, and publishes news along with codes for developers to start practising and building their portfolio from scratch. The most significant attraction of their website is the free MOOC “Practical Deep Learning for Coders” course that teaches developers how to dive into deep learning from the beginning. Click here to see how they are making neural nets uncool again. DeepNote A notebook built for collaboration, DeepNote is where teams collaborate together to build softwares, learn and contribute to projects, and put together their portfolio. The Jupyter compatible notebook is focused on collaborative learning and uses tools like Python, R, SQL, TensorFlow, and PyTorch to easily connect data sources. Getting started with this and learning the platform can help boost your career in the data science industry. Click here to learn more about DeepNote. DataCamp A free to start course, DataCamp offers data skills that a developer and data scientist can learn at their own pace—starting from no-code skills to machine learning. The platform runs straight from the browser and does not require any installation. Opting for tailor-made courses for data scientists, machine learning scientists, data engineers, data analysts, and statisticians can help propel your career in the right direction. Click here to visit the website. Weights & Biases An MLOps platform built with developer-first focus, Weights & Biases offers building models faster, dataset versioning, and model management. It can be accessed on the cloud or installed offline to collaborate in real time. GitHub claims that W&B was fundamental in launching their internal machine learning systems. It has also been praised by Wojciech Zaremba of OpenAI. The community, podcast, and forums allow developers to stay in touch with the AI community and collaborate for projects. Click here to know more about W&B. Dataquest.io This platform with their real-world challenges and projects allow developers and data scientists to master and perfect their skills. Dataquest.io allows you to build a portfolio of their data-driven projects along with enabling collaboration of developers and employees. Learning from this website offers crisp answers to various problems and challenges about machine learning, and thus helps data scientists become job ready and on the lookout of recruiters. Click here to sign up and start learning with Dataquest.io.
The stronger your portfolio, the better chances you have of landing your dream job.
Getting into the field of data science makes developers and data enthusiasts question about how they can build their career. Becoming an expert in the field requires hours, days, and months of practice and experimentation with various codes, projects, and data already available on the internet.
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Mohit writes about AI in simple, explainable, and often funny words. He's especially passionate about chatting with those building AI for Bharat, with the occasional detour into AGI.