Building a Data Science Portfolio

[Participant @simileoluwa]

Regarded as the sexiest 21st-century job by Harvard Business Review, the fast-growing field of Data Science is in huge demand. The exponential growth in demand for Data Science skills has been substantially fueled by Big Data and Artificial Intelligence. The world is inundated with data existing either in structured, semi-structured, or unstructured forms. Without individuals to facilitate the collection and implementation of analytical processes for insights, the vast data available is not useful.

Data Science deals with the process of drawing insights from data and its concept is to unify Statistics, Data Analysis, Machine Learning, and their related methods. It combines the use of programming languages such as Python, R, SQL, etc. with Statistics, Linear Algebra, Calculus, Probability to find patterns in data and thus helps to drive the decision-making processes of an organization.

The increasing relevance of Data Science can be associated with the fact that more companies are coming to realize the importance of Data Science, AI, and Machine Learning. Regardless of industry or size, organizations that wish to remain competitive in the age of Big Data need to efficiently develop and implement Data Science capabilities or risk being left behind. The field of Data Science can be subdivided based on tasks to be accomplished and tools to be used into different professional titles, however, all their responsibilities revolve around data. These subdivisions include:

1. Data Scientists: The term “Data Scientist” was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data. Data Scientists primarily are tasked with building models using Machine Learning. These models are expected to engender an organization’s software with product features that predict and explain; making applications adaptive. They have strong business acumen and visualization skills.
2. Data Analysts: They are responsible for the identification and creation of Key Performance Indicators (KPIs), skilled in the use of Business Intelligence tools and they possess very strong business acumen and domain knowledge.
3. Data Engineers: They are the data professionals who prepare the “big data” infrastructure to be analyzed by Data Scientists or Analysts. They are software engineers who design, build, integrate data from various resources, and manage big data.

A Data Science Portfolio

Having understood the various subdivisions of the Data Science world, it is important to know that although this field is in high demand, the supply of skilled applicants, however, is growing at a slower pace. Landing a Data Science role in any of the above-mentioned divisions requires a delicate balance. While a candidate for a role may be qualified, equipped with all the necessary degrees, coding skills, etc, he/she might not get the role due to the absence of a Portfolio. Employers want to see a strong aptitude for Data Science in their new hires, but this skill isn’t always easy to demonstrate in an interview. Employers generally look for:

• Ability to communicate
• Ability to collaborate with others
• Technical competence
• Ability to reason about data
• Motivation and ability to take initiative
• Effective project execution

All these cannot be easily demonstrated in an interview and this is exactly what a Data Science portfolio helps you achieve. A Data Science portfolio is a great way to showcase your skill set in place of work experience. It also demonstrates your passion for Data Science and assuming that passion is genuine, you will also have a lot of fun completing your projects and learning new Data Science skills through them. Various things make up this Portfolio:

GitHub Page:
This is a very crucial part that makes up your portfolio. A crucial part of Data Science is the ability to code and Gitbub helps potential recruiters see what past projects you have accomplished. While having a GitHub page is not the only deciding factor to land up a good job, a genuine GitHub portfolio lets potential employers have a glance into a candidate’s abilities.

Competency Projects:
This involves picking up certain areas of Data Science and improving on them by working with datasets. Kaggle is a great way to start this. Say for example you want to improve on your data wrangling or visualization skills, pick up datasets that you can apply your coding skills on and promptly practice. When you have completed each task you can upload these projects on your GitHub page and give descriptions of the processes you took.

Open-source contribution:
An open-source project is where the code to a certain project is completely open-source. That means that anybody can readily see the code that went into a project. Open-source projects also are usually community-based and accept help from other programmers. Contributing to open-source helps to showcase your interests, problem-solving skills. This could include answering questions on Stack overflow, reviewing projects on GitHub and making corrections as small as renaming or correcting typographical errors.

Writing:
Another way to showcase your skill is by writing about the various processes you’ve learned. A lot of Data Science is about communication and presenting data. Blogging is a way of practicing this and showing you can do this. Writing about a project or a Data Science topic allows you to share with the community as well as encourages you to write out your work process and thoughts.

Publicity:
This can be achieved by having social media accounts such as Twitter, LinkedIn, etc. where you can actively share what you do and what you’re doing. A platform such as LinkedIn provides you an avenue to connect with professionals and possible employers. Your consistency in displaying what you’re involved in can help you gain an employer’s interest.

Conclusion

We have discussed the fact that a portfolio helps you amplify what you can do, however, it is not to say that certifications, resumes, and degrees will not be recognized. These are great, however, anyone can claim to have them all but a great portfolio backs up efficiently the claims. They should, therefore, not be ignored but a balance should be maintained and you’re on your way to landing that dream job.

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