A data science portfolio demonstrates your ability to handle, comprehend, and analyze data. A range of initiatives can be included, depending on your role. A data scientist can show by using prediction models and machine learning as examples. A data analyst can exhibit statistics and data visualization. A data engineer can demonstrate ETL, database administration, and data warehousing. A solid portfolio in data science might help you project a more professional image. These are the five methods for building a portfolio in data science:
- Kaggle: Kaggle is a great resource for anyone interested in data science and machine learning. You can work with others, broaden your horizons, and learn new things. You can also showcase your skills and attract companies’ notice by engaging in debates, posting notes or projects, and competing.
- DagsHub: Those who are interested in machine learning and data science should visit DagsHub. For ML practitioners and students, all ML model-building tasks can be finished in one place. You can host your projects with code, data, models, experiments, guides, and visualizations. It’s also easy to implement your models.
- LinkedIn: Data scientists can use LinkedIn to highlight their accomplishments, skills, and body of work. Because it has a large user base and focuses on careers, it can help you connect with companies or clients. You can highlight your data science credentials, papers, and projects on LinkedIn. You may network with other data professionals, follow companies, and join groups.
- Medium: For data scientists looking to display their skills and work, Medium is a great blogging platform. It boasts an easy-to-read format and a large readership. A data science portfolio can be constructed by summarizing your projects and scholarly work. You can publish articles describing your data science methodology, processes, and conclusions. You can also post entries that demonstrate your skill with certain techniques and tools.
- DataSciencePorfol.io: With this site, data scientists can easily construct an online portfolio. It is intended for the data science community. It only takes a few minutes to establish a data science portfolio. You can showcase your education, work history, skills, projects, and more. You may articulate the goals, methodology, and results of your projects. You can also add live demos or GitHub links to showcase your skills.
