Easy Guide To Building A Promising Data Science Portfolio

“Two things remain irretrievable: time and first impression.”

           -Cynthia Ozick

No wonder, a Curriculum Vitae was a prerequisite for landing a job role in any industry. But, in today’s competitive times, we’re pushed by the demand for providing more than just the bare minimum. A filthy rich portfolio leveraged with credible data science certification is a critical ingredient in getting closer to your dream data science professional job role.

The best way to learn data science is to do data science.

With humungous data surging with every passing day, it has made itself an integral part of our existence. Neither any business nor personal routine is free from data. If your portfolio looks too generic; or lacks elaboration and interesting projects, it can get hard for you to fulfil your data science dream. 

The days are long gone when recruiters will be satisfied with the basic qualifications. It all comes down to the expertise and pool of skillset that you’ve earned during the courses or certification for the role. Projects and internships (paid or voluntary) add much-needed value to your data science portfolio. Not everything must be looked at from a monetary point of view. Stay proactive and showcase the best of the data science skills that you possess. Let us understand the key insights for developing the best data science portfolio.

What is a data science portfolio?

A data science portfolio is a collection of the best work and demonstrates your skills as a data scientist. A data science portfolio features a combination of your code and documentation and some writing samples showing your ability to communicate effectively about data. 

Role of a data science portfolio?

An aspiring data science professional values and understands the worth of the time invested in building a high-quality data science portfolio. Developing your portfolio based on internal motivation forms the right base, thereby leveraging a genuine feel to it. This also pushes you to get your data science certification and as a certified professional to put in your best for the offered role. It is critical for any data science aspirant to invest their time in building a quality portfolio as;

• It helps you in landing your dream data science role
• Assists you with much-needed hands-on experience
• Helps you in knowing the data community
• Resonates with your passion for the industry

Key constituents of a data science portfolio:

• Concise explanation about yourself and what you do
• Quick load time
• Professional design
• Illustrates your best work and projects
• Social validation
• Gives convenient access to you

Types of Data science portfolio projects:

• Code-based projects: These mimic real-world data science projects by taking a dataset and solving a problem around it. Such as scraping a dataset and analyzing; creating a website or an app, etc.
• Content-based projects: These offer ample opportunities for you to display your communication and writing skills. These include blog posts and coding tutorials, video tutorials, podcasts, etc.

Quick Tips for designing the best data science portfolio:

• Be authentic and provide a snappy introduction
  As data science projects aren’t easy to finish, the ones that capture the most attention are the most sought-after options recruiters look for. Describe your data science skills in a short sentence that’s followed by a short paragraph elaborating on the skillsets. 
• Be Creative with your data science projects choices
  Monotony is never a success! Creativity in the selection of projects is considered a prerequisite to ace an interview. Highlight your most promising projects to entice the recruiter. 
• Showcase a variety of data science skills
  Putting forth the most in-demand data science skills- technical as well as soft skills- data collection, business acumen, pre-processing, programming, designing Machine learning algorithms, etc are at the core of a data science professional‘s role.
• Stay upbeat with GitHub
  Own a GitHub profile and provide the link on your portfolio. It allows you to host a remote version of your project where others can see it and even collaborate to form a better version.
• Write blog posts for your readers and make it easy to navigate your portfolio
• Use a self-hosted solution that offers more control over regulating the look and feel of your portfolio. Investing in an appealing design would help tremendously.

Popular examples of great data science portfolio creators are:

• Natassha Selvaraj- A renowned data consultant working at the amalgam of data science and marketing; enjoys writing blogs and articles for aspiring data science professionals.
• James Le- Ace computer science master’s student at Rochester Institute of Technology; specializes in ML, recommendation systems, data analysis, and data visualization.
• Katie Jolly- A popular geographer, and data scientist currently serving as a data analyst at Ookla; specializes in Cartography and information design, Bayesian spatial modeling, statistical computing, and much more. 
• Hannah Yan- She specializes in data visualization, and has interned with Uber on interaction design and web-app development.  

Recruiters looking for experienced data scientists expect them to be able to manage large amounts of data and analyze and process the same. A good portfolio is definitely the cherry on the cake that grasps employers’ attention. They are keen on deploying professionals who can analyze data using varied techniques and tools. Core ability to communicate findings clearly and concisely is a quality that takes you a long way ahead in your data science career. Whether you’re a newly qualified data analyst or a seasoned data science professional, you’ll need a portfolio that pops. Why wait? Build a strong portfolio today!