Building a data science portfolio is necessary? How to Build it?

Businesses that leverage data have a higher chance of making well-informed business decisions. To gather, interpret, and analyze the data, they need someone who has a comprehensive data scientist portfolio. Therefore, candidates who want to work in this field should focus on creating an impressive data science portfolio for themselves.

Harvard Business Review named data science one of the hottest job profiles in 2024. Companies have positions for data scientists, analysts, engineers and managers. However, the lack of awareness about data science in India is one of the primary reasons why these posts remain unfilled. Here are a few advantages that candidates with a data science portfolio have:

Fewer competitors 

Data science is a relatively new field in the market. As a result, few data professionals have a vast portfolio of data science certification courses. These positions are filled mainly by computer science, statistics, and mathematics students who have a fair idea about data analytics and big data. Therefore, the individuals who maintain a data science portfolio will naturally have fewer competitors in the job market. 

The essential requirement to be a data scientist is to possess a decent programming talent with the ability to solve problems. Candidates with enough potential to be in this field can improve their data science portfolio by applying for competent data science certifications. 

Impressive salary 

On average, data scientists in India earn from Rs. 60,000 to Rs. 70,000. Most talented freshers can get this kind of salary within a year. Individuals looking for an exciting job profile with an impressive salary must try their hands at data science certification courses. 

A great scope in the future 

Data science jobs will be relevant for a long time now. As per a report by IBM, a growth of 30% can be seen in data science jobs by 2022. As per the US Bureau of Labor Statistics, around 11 million data science jobs will be created by 2026. These figures indicate an excellent career scope for data professionals in the coming years.

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Different types of portfolio projects

Projects are an essential constituent of the data science portfolio. The candidates can demonstrate their coding skills through the project and also show well they understand the underlying data science concepts. Here are the different types of projects that they can include in their data science portfolio:

Projects based on code

Code-based projects can be used to solve a real-world problem by using a dataset that is obtained from a reliable source. For example, building apps or chatbots to communicate with customers, creating a tool that identifies trends from raw data, etc., are some common examples of code-based projects.

Projects based on content

Including content-based projects are not as commonly used as code-based projects. Nevertheless, they showcase candidates' knowledge, writing, and communication skills.

For instance, candidates can make a video tutorial to explain how an app works. Or else, they can publish an article that describes data science concepts to a non-technical audience.

How to create a data science portfolio?

The online job portals are flooded with applications for roles related to data science portfolios. However, very few candidates try to create an extensive data science portfolio or resume. Here are a few tips that budding data professionals can use to create a comprehensive data scientist portfolio:

Be passionate

The ultimate goal of a data scientist should not be adding as many data science certifications as possible. Experience in using complex data analytical models and tools might help a candidate secure a job. However, the best data science portfolio includes projects stemming from a passion for the subject.

Also, when the candidates are passionate about the concepts related to data science, they will be ready to overcome any hurdle that can restrain them from completing a project. Hiring managers also specialize in analyzing the profiles of such candidates and prefer them over others.

Create a GitHub Profile 

If the students have completed some data science projects, they must upload them on GitHub and share the link on their CVs. GitHub is an internet hosting service that allows programmers to build and upgrade software solutions. Individuals can share their project from anywhere other people can observe it and might want to collaborate to develop the version. 

The best thing about GitHub is that it allows the candidates to narrate their journey in their respective fields. The read me section of the GitHub profile is the perfect place to express why someone should avail of their expertise. A captivating story is also required for non-technical people looking for someone with the necessary skill set and experience to manage data science projects.

Use Kaggle 

Kaggle is a community for data scientists and machine learners. It hosts regular competitions by leading companies like JPM, ZS Analytics, Bain & Co., etc. The students can display their data analysis and programming skills by participating in these competitions. 

Kaggle allows them to earn titles and medals like Expert, Grandmaster and many more that can make their LinkedIn profile impressive. Moreover, it is also possible to include the link in the resume. 

Be updated 

Data analytical and programming skills can be insufficient to build a robust data science portfolio. The students must also keep themselves updated about the latest developments and innovations in the data science field. They can also read the blogs of famous data scientists to learn from their experiences. 

Look for unique projects

Candidates commonly use datasets like MNIST, Iris, and Titanic for their projects. However, these datasets are so overused that they might give a wrong impression to the recruiter. Also, they might create an image that the candidates have just commenced their journey as a data scientist.

Therefore, they must avoid including projects that other students commonly undertake. Instead, they should undertake projects in which they can showcase their technical skills and passion for the subject.

Portfolio Design

The candidates must also focus on the aesthetics of their data science portfolio. They should use neat templates and layouts if they have an online profile, especially where technical terms or code language are defined. The aim should be to enhance the overall experience of those who might read their portfolio. The documentation of the projects should also be used as an opportunity to showcase soft skills and creativity.

Create a website 

Creating a data science portfolio website can significantly impact recruiters. The students can mention their skills and projects and upload their projects on their websites. They can either create a data science portfolio website using HTML or use WordPress, Wix, or other platforms to create a website. 

Create a profile on LinkedIn 

LinkedIn is a massive community of professionals where individuals working in similar fields like to connect and help each other out. While recruiters are actively searching for data scientists, data analytics, and programmers on LinkedIn, it is also an excellent place to upload resume details. The students can post links to their websites, projects, and even blogs on LinkedIn. 

Undertake small projects 

The students should undertake small data science projects to add more relevance to their data science portfolio. They can find the datasets for their projects on HR analytics, Customer Segmentation, Kaggle and other platforms.

They can check the sample projects on the internet to get an idea and inspiration to start their project. Once they have created a project, they can deploy it on AWS, Heroku, or another cloud-based platform.

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Complete data science certifications 

The students can learn R programming, data analysis using Python programming, AI and deep learning, machine learning, and other data science certifications to make a dynamic data science portfolio. 

They can use platforms like Star Agile to find all the data science certifications in one place. StarAgile also provides career and placement services and assists candidates in taking data science projects. Live instructor-led sessions, case studies of large brands, simulated projects, and real website capstone projects are some of the highlights of their data science courses. Visit Star Agile now and explore the opportunities that can add relevance to your data science portfolio! 

Conclusion

These are some tips that can help aspirants to create a comprehensive data science portfolio. While it is essential to include data science certification in the data scientist portfolio, it must be obtained only from reputed institutes. Enrolling in data science training that imparts practical training and covers real-world situations can help them build a rewarding career in this field.