How to Build a Data Science Portfolio
Published: 2022-06-23 16:37:04


Earlier, recruitments were based on the technical knowledge that we mentioned in the CV or resume. Today organizations demand more industry-relevant hands-on experience over theoretical knowledge. Employers look for the work the candidate has done in that particular field. Thus an overall portfolio will help in setting a benchmark for selecting the candidates. Well, this is not required for every IT field, but if we consider the Data Science industry, then having a portfolio is essential as the employers will decide based on your work and the practical knowledge you have. 

Having a Data Science portfolio will help you showcase the skills that will allow you to stand out among other candidates appearing for the same job. If you have done your homework correctly, it will help you get the dream job you desired—all you have to do is showcase your data science on the portfolio. Without much ado, let’s understand the importance of having a data science portfolio to land a top gig as a data analyst, data scientist, or any other data-related job role.

**What is a Data Science portfolio?**
Like any other professional portfolio, a data science portfolio helps candidate showcase their talent and skills through their projects. Having a solid portfolio will highlight your ability to solve research questions, analyze data, gather project insights, how well you can collaborate with other team members, and your ability to communicate your findings to the users. All this is possible only if you have a solid technical background. 

Having a data science portfolio will help establish trust with your hiring manager so he will be sure that you are the desired candidate for the job. Also, you do not have to worry if you are a beginner in the industry and do not possess any experience in any technical field. An attractive data science portfolio will divert the recruiter’s attention more towards your portfolio to judge your skills.

Creating a portfolio for data science is easy for beginners as it offers various public datasets that they can use in their projects. If you have creativity, you can even accommodate every feature using the public dataset rather than spending on the project. 

**Why is there a need to have a data science portfolio?**
Well, most employers look for the degree and technical skills that you possess. They majorly focus on where you got your degree from. If it is a prestigious college or institute, they will be confident that the candidate must have the right skill set that is required for the data science job. But if you do not have a proper degree in data science, you have the lowest chance of getting the job. But if you have a data science portfolio showcasing your creativity, talent, and technical skills, you will be considered for the job. Being a data scientist will require strong technical knowledge and how to use various tools for working with data. 

Thus, it is important that you carry your data science portfolio to get the dream job you thought of. If you are new to data science and do not have any idea how you can start working on your data science portfolio then you can go through the below-mentioned tips for creating one and get noticed during interviews.
**Tips to Create a Data Science Portfolio**

- Create a GitHub Profile
With GitHub, you will be able to host the remote version of your data science project to be visible to everyone. Make sure you have an active GitHub profile so that you can add a link to your CV. an active profile refers that you are working on it regularly to make adequate changes and are visible to your viewers. It is a good practice to have a readme.md for your profile for customizing your homepage. 

- Make proper use of Kaggle
If you want to practice and showcase your skills on a regular basis, then it is recommended to have a Kaggle account. This site is popular among several big companies and used for carrying out competitions for hiring suitable Data science candidates. Kaggle also helps in understanding the techniques and tips that you can use while working with data. You can go through its tests and earn medals that will be beneficial for you and add a plus point during the interview. Also, you can add the link for your Kaggle account on your CV. below is an example of the Kaggle profile.

- Participate in various Competitions and Hackathons
If you want to test your technical knowledge in data science then you can appear for various online competitions and hackathons. It will help you to know where you stand and your position among the peer groups. You can even add your achievements in your CV along with a link to your online certificate for that competition. There are various online platforms where you can appear for the exam and test your skills.

- Prepare yourself using HackerRank
For becoming a data scientist, you need to have various technical skills and strong knowledge of programming languages like Python. You can prepare yourself by engaging yourself in various platforms for enhancing your skills. You can solve and practice questions available on the HackerRank website and add your achievements to your CV for being a better candidate for the profile.

**Some examples for the Data science portfolio **
The main purpose of having a Data Scientist portfolio is to showcase your technical skills and convince the hiring manager that you are the right candidate for the job. So make sure, you add the case studies to your profile. We have mentioned some of the project types that you can create and mention in your CV.

- Data cleaning projects
One of the key roles of a data scientist is to clean and arrange the messy data of any company. So it is a better idea to add a project that involves data cleaning. Make sure to use existing messy data sets, then try to explore data to clean it up and perform analysis to get better insights. This will create a strong base for you to get selected.

- Data Storytelling Project
Due to the expansion in data for any company, there is a huge amount of raw data that is not arranged and does not make any sense. Being a data scientist, it is your job to find the correlation in the existing data and to fit them into a story narration so they make some sense. You can add an example where you have implemented this approach.

- Machine Learning Project
If you include a machine learning project, it will help in specifying your progress in the required technical knowledge. Creating a machine learning project will require knowledge of algorithms that will work on the entered data and create an output. You can showcase your understanding of the algorithms that is a plus point for the hiring manager and put you in the technical role.

**Conclusion**
Data scientists are in great demand and require a unique and strong skill set to become one. Having the right knowledge is not enough to get selected if you do not know how to implement that knowledge in a project. So instead of having theoretical knowledge, you must focus on practical one as recruiters focus on how a candidate is able to apply that knowledge. So having a portfolio will help both the candidate and the recruiter. Candidates will be able to showcase their talents and the recruiter will get a base for selecting the candidate based on the candidate’s talent. So make sure to create a Data scientist portfolio for better job opportunities.