The data science market in Singapore is as competitive as it gets. People need to give credit for this to the strong governmental support for digital transformation, the presence of both regional and global data powerhouses, and the growing demand for data-driven solutions. There is also tremendous demand for data professionals in Singapore, evident from the salaries these professionals receive – an average base pay of SGD 7,000 a month. In such a scenario, data science students need to craft a well-structured portfolio that showcases their skills and helps them stand out to prospective employers.
The blog will discuss the best ways for aspiring data scientists in Singapore to build a data science portfolio and also highlight the traits that recruiters in the country look for in such portfolios.
Also Read: Difference Between Big Data and Data Science
Building a Data Science Portfolio: A Step-by-Step Guide
In the competitive data landscape of Singapore, one of the best ways to stand out to an employer is by building a data analyst portfolio. It shows prospective employers that students have taken a different route than their colleagues who studied with them.
Choosing Projects Wisely
Choosing projects wisely is the first step in building a data science portfolio that can help students fortify their prospects of landing good jobs in this sector in Singapore. Students must choose projects on interesting and diverse topics that highlight key interests and skills.
They must consider projects that involve steps such as data cleaning, visualisation, and analysis, as well as machine learning. In this context, they must also not be wary of selecting projects that push them out of their comfort zones. They must also prioritise projects that show their ability to solve real-world issues.
Documenting and Communicating One’s Work
For students who want to know how to make a data science portfolio, properly documenting and communicating one’s work is a crucial step. They need to write concise and clear descriptions of their projects, outlining the problem, their approach, and the results. They must use visuals, such as charts, dashboards, and graphs, to effectively present their findings.
Students should consider using platforms such as R Markdown or Jupyter Notebook to present their work in an interactive and structured manner. They should also explain the rationale behind their selections and the methodologies they followed in their projects.
Building One’s Online Portfolio
Students must always select a platform where they can host their portfolio, such as personal websites like GitHub Pages or platforms like Skillfloor. Over there, they can always check out good data science portfolio examples on the internet to get a better idea of how to go about such work. They also need to ensure that their portfolio is visually appealing and easy to navigate.
Students can always include a section where they introduce themselves and highlight their career goals. They need to showcase their projects and highlight their contributions and accomplishments. They can also write blogs to share their experiences and insights.
Seeking Feedback and Iterating
Seeking feedback and continuously fine-tuning are underrated aspects of building a strong data science portfolio. Students can share their portfolios with peers, employers, and mentors to receive feedback on their work.
They should use the feedback to improve their portfolio and refine their skills. They must also continually update their portfolio with new skills and projects.
Showcasing Key Skills
Showcasing the skills one has acquired through education is one of the most essential parts of building a data analyst portfolio.
| Areas | Skills |
| Programming Skills |
|
| Data Analysis and Manipulation Skills |
|
| Visualization Tools |
|
| Machine Learning Techniques |
|
| Cloud and Deployment Skills |
|
Also Read: Data Scientist Interview Questions in Singapore
What Recruiters in Singapore Look for in a Data Science Portfolio?
While creating a data scientist portfolio, students need to keep in mind that employers are always looking for certain qualities in such projects such as the following:
- Interesting and Diverse Projects
- Clear Communication
- Technical Skills
- Business Acumen
- Problem-Solving Abilities
- Data Interpretation and Management
- Originality
- Communication Skills
- Professionalism
- Continuous Learning
Also Read: How To Become A Data Scientist?
Elevate Your Data Science Career with upGrad
The online data science and analytics courses offered by upGrad are the best options for students seeking to excel in the competitive data science landscape in Singapore. While experts always recommend great data science projects for portfolios to stand out in such environments, they can never trump the need for quality education, and that is what these courses offer. These programs help students elevate their data skills and master the discipline itself.
Explore these online data science courses through upGrad!
- Master of Science in Data Science, Liverpool John Moores University
- Post Graduate Diploma in Data Science (E-Learning), upGrad Institute
- Executive Diploma in Data Science and AI, IIIT Bangalore
- Post Graduate Certificate in Data Science & AI (Executive), IIIT Bangalore
FAQs on How to Build Your First Data Science Portfolio
Q: Do I need both Python and R for a data science portfolio?
Ans: Candidates building a data science portfolio need to master Python at the very least – if they learn R with it, it is even better.
Q: How do I build my data science portfolio?
Ans: Students looking to build a strong data science portfolio must emphasise demonstrating their experience and skills through real-world case studies and projects.
Q: Can I build a data science portfolio without work experience?
Ans: Yes, students can and should always build a data science portfolio when they lack work experience, as it serves as their primary evidence of competence throughout the recruitment process.
Q: How many projects should I include in my data science portfolio?
Ans: A data science portfolio should contain at least 3-4 projects to provide reasonable evidence of the student’s skills.
Q: How do I make my data science portfolio stand out to employers in Singapore?
Ans: Students must showcase their technical skills in their data science portfolios and include a section that showcases their professional and academic background to stand out to employers in Singapore.
