How to Build an Impressive Data Science Portfolio?
Data Science is rapidly gaining ground and has become one of the most sought-after jobs by graduates and veterans today. This has also resulted in high competition for these jobs. When everyone is passionate, hard-working, and knowledgeable, how do you stand out from the rest of the candidates? Answer- Portfolio. A solid and diverse Data Science portfolio helps in highlighting your abilities and knowledge.
Because of the abundance of talent in the job market, employers might be a little wary of employing a fresh graduate. So how can you persuade them that you are the ideal candidate for the job? You demonstrate your abilities by displaying work from previous projects and internships. Portfolios are made with the primary aim of showcasing one’s abilities to potential employers. Thereby increasing your chances of landing a job at your dream company.
Portfolios also become important if you have a non-technical education background or if you are changing your career. It is also relevant to those who have very little work experience. Now that we have listed out a few reasons as to why a data science portfolio is important, let’s get onto how to build an impressive data science portfolio.
What Projects Look Attractive on Your Portfolio?
The best way to increase your chances of getting hired for a job is by building a portfolio targeted at specific employers, specific industries, and specific jobs. However, if you’re looking for entry-level positions, you’re casting a wide net and seeking opportunities that require a similar set of skills and abilities. You’ll be off to a great start if you put together a portfolio that includes at least one project from each of the following areas.
- Data Cleaning: Data preparation, munging, and cleaning – whatever you want to call it – is required in 60-80% of most data science profiles. Therefore exhibiting your data scrubbing talents with a project will validate your skills. You will first need to pick a messy, chaotic dataset with which you will work. Go through the unfiltered data to come up with interesting analytical questions or angles that you want to investigate. And then clean the data and perform some rudimentary analysis to answer those questions. The cleaning techniques differ from one dataset to another. You might need to fix structural errors, filter out unwanted data, and handle missing data. Some of the best tools to use are TIBCO Refine, Winpure, Data Cleaner, Data Ladder, etc. Tableau is another powerful software that can be used for data cleaning.
- Data Storytelling and Visualization: Storytelling in data science refers to drawing conclusions by analyzing and connecting data. Any data scientist’s job entails providing actual insight and persuading others with data. These conclusions, insights, and their application to the business need to be communicated to various stakeholders. This will require the use of data visualization and communication skills. You can design a project similar to one released by National Geographic visualizing 500,000 deaths from Covid-19 in the US. Or a micro project focusing on the mental health of high school students.
- Group Project: Working in a group reveals that you have effective communication skills and the ability to work in a team, which are vital in data science profiles. Group projects can be a great way to create a challenging and difficult project. You can try contributing to a well-known open-source project, such as a data-science-related open-source library in a language of your choice. One example of such a project is classifying breast cancer. You can use Python for programming this project. Along with Python, the Invasive Ductal Carcinoma (IDC) dataset can be used. You can use Keras, OpenCV, NumPy, and Matplotlib python libraries. It can be tricky, but if you are able to contribute to a well-known library or package, it will really help companies notice your application.
Few More Tips to Make Your Portfolio Attractive
1. Capitalize on Online Presence
Online presence has become very important for having a better shot at getting hired. It helps validate a few things like your identity, education, social connections, interests, etc. There are several platforms that can be used for building an impressive social media presence. Some of them being-
- LinkedIn – A LinkedIn profile works as your social professional profile. It helps to build credibility and interest in your profile. Companies can verify your education, work experience, and projects. Your LinkedIn profile also works as a digital resume since every achievement, qualification, project, and website link can all be mentioned here.
- Build a profound GitHub Profile – GitHub is every developer’s best friend. Especially for a beginner developer. It is software that allows a web-based graphical interface, which offers access controls and collaboration features. You can upload and share your data science project. This way employers or fellow data scientists can refer and go through your project. It is also a great way to collaborate on group projects. Try to have multiple projects and a few collaborations on your profile. Providing the link to your GitHub profile on LinkedIn and resume is a great way for building a portfolio.
- Use Kaggle – Kaggle is another great platform for data science and machine learning enthusiasts. It is a community where one can learn, practice, and test data science, and machine learning. Kaggle is an excellent way to start building data science projects. You can find various open datasets for starting your project, as well as other resources that will come in handy for the project. There are also competitions that you can participate in. You can headline these on your LinkedIn profile and earn badges or medals that will make your profile stand above the rest.
2. Learn & Network As Much As You Can:
It is given that the more languages, techniques, and tools you know about, the better it will be for building a portfolio. This is because having a wide array of skills can help you execute languages and understand problems better. It also helps to sharpen your analytical skills. Since data science is an evolving field, keeping on top of the trends and developments becomes imperative for aspiring data scientists. So, how do you do this?
- Read Blogs – Blogs are a great way to quickly gain new insights and information on any subject. The best thing is that since blogs are published almost every day, they contain relevant information about the new developments. This way you are aware of the current events and latest developments taking place in data science. Mentioning a few of these in an interview will surely impress the panel. One can also help you to master new skills and tools.
- Building a Community – Building or being a part of a community like Kaggle, opens many doors. Communities are a great way to learn, practice, and teach skills. There are various well-known communities like Kaggle, Slack, LinkedIn, and Discord, that you can join. Being active in such communities ensures that you never miss any updates or opportunities. It is a great way to build professional connections. Also, an active profile on communities tells the employers about your serious interest in data science.
- Portfolio Website – This step is often seen as a hassle and hence overlooked. But it is also a step that can help a candidate make a brand for themselves. Similar to how graphic designers use Behance for their portfolios, you can use Wix/Weebly or HTML for creating a website for your data science portfolio.
Creating a portfolio may seem daunting at first, but it will become second nature after you become used to documenting and exhibiting your projects. The best thing to make building a portfolio fun and impressive is by selecting datasets and problems that interest you. This way you might be able to give it a personal twist and differentiate your project from everybody else’s. Start with blogging sites or construct your own website if you want to build your brand. Building a solid data science portfolio will give you enormous advantages. Making the most out of the above-mentioned tips will help you make a substantial and impressive data science portfolio.