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How to Build a Data Science Portfolio? (with Examples)
Published
08th Sep, 2023
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You have done your pieces of training and completed your learnings, and you are ready with your data science arsenal to battle business problems. You are ready to put your data science skills to work. But you don’t have the experience that can vouch for your skills, you have never made a Machine Learning model for a business before. Then how would you convince the hiring team that you can solve all their data problems with your newly learned skills?
Well, you are in luck because there’s a way around not having the relevant experience in Data Science which can help you convince your hiring manager that you are the right person for the job. Yes, you can land a job by building a strong data science portfolio. Let's start with understanding what a data science portfolio is.
What is Data Science Portfolio?
A portfolio is a collection of data science projects that a professional has worked on. Like any other professional portfolio, a data science portfolio is also the collection of data science projects that you have worked on. A data science portfolio can help you showcase your skills and credibility, and also helps you highlight your strengths and abilities.
A Data Science portfolio is an amazing way to bridge the gap between your learnings and your practical knowledge. You can make up for the experience that you do not have, by simply creating a strong data science portfolio. For those who are already experienced, the portfolio gives them the chance to showcase the skills they could not apply during their work experience. If you are looking for a place to start learning Data Science, you can check for online Data Science Bootcamps.
The best part about building data science portfolios is that there’s no shortage of datasets that you can use to get started. You can easily choose from hundreds of publicly available datasets and start exploring them.
How to Create an Effective Data Science Portfolio?
1. Find the Best Platforms to Host Your Portfolio
Pick the right platform. There are a number of different platforms that you can use to host your portfolio, but not all of them are created equal. Do some research to find the platform that best suits your needs. Checkout for online Data Science Bootcamps to know more about data science opportunities and the learn the skills required for these roles.
2. Write a Concise Explanation About Yourself and What You Do
When it comes to writing about yourself in a portfolio, it is important to be clear and concise. Include information such as your educational background and any relevant work experience. As for your work, be sure to include only your best and most impressive projects on your data science portfolio website.
3. Illustrates Your Best Work and Projects
When creating portfolio, focus on including only your most impressive data science projects. This will ensure that potential employers or clients see your best work and get a good sense of your skills and abilities. In addition to featuring your best data science portfolio projects, make sure to include a brief description of each one.
4. Add Social Proof
An effective portfolio will not only highlight your technical expertise, but also demonstrate your ability to solve real-world problems. One way to add social proof to your portfolio is to include testimonials from clients or employers. Another way to add social proof in your data science portfolio is to include links to articles, blog posts, or other online content that you have created.
5. Add a Straightforward Way to Reach You
Make it easy for potential employers to reach you. Include your contact information on every page of your site, and make sure it's up-to-date. You should also include links to any professional social media profiles (e.g., LinkedIn) that you have.
6. Provide a Catchy Introduction
Start with a strong foundation. Make sure that your portfolio includes a clear and well-organized overview of your skills and experience. Include links to relevant projects, publications, and presentations. Be sure to also highlight any awards or recognition you have received.
7. Add a More “About” Section
Make sure to add a more thorough “About” section. This is your chance to introduce yourself, briefly describe your career journey, and highlight your strongest skills and qualities. Additionally, be sure to include links to your professional social media accounts, as well as your resume or CV.
8. Mention an Easy Way to Contact You
For building a data science portfolio, ensure your contact information is prominent and easy to find. Hiring managers should be able to reach you with just a few clicks.
9. Showcase Your Most Promising Projects (Less is More)
It is essential to put your best foot forward and only include your most promising projects for portfolio. This doesn't mean that you should only include projects that were perfectly executed - after all, data science is all about experimentation and learning from mistakes.
10. Make It Easy to Navigate (Simple and Concise)
To make it easy to navigate, keep your portfolio simple and concise. Include clear titles and descriptions for each project and use consistent formatting throughout. It will increase your chances of impressing a potential employer and landing your dream job.
11. What to Avoid (Points)
First, avoid including too much information. Data science is a broad field, and your portfolio should highlight your specific areas of expertise. Second, don’t include irrelevant information. Third, make sure the information you do include is accurate. Finally, avoid plagiarism in any part of your portfolio.
Data Science Portfolio Examples
There are a few different things that you can include in your portfolio for data science, but some of the most common examples include:
- Projects that you have completed during your studies or while working as a data scientist. These projects can demonstrate your analytical skills and showcase how you have used data to solve real-world problems. Avail Data Science Course online to learn in detail about data science and acquire the required skills to stand out from other job seekers.
- A list of publications that you have written, either alone or as part of a team. These publications demonstrate your ability to communicate complex information in a clear and concise manner.
- Presentations that you have given at conferences or other events. These presentations show that you are comfortable speaking about data science topics in front of an audience and can help to engage potential employers.
- Any awards or recognition that you have received for your work must be included in your Github data science portfolio. These awards can show that you are respected by your peers and are seen as an expert in your field.
Now that we have understood the ways to make a data science portfolio, let’s now have a look at some of the data science portfolio examples that you can use to create your own set of projects.
1. Data Cleaning
Cleaning data is the first and the most important task in any Data Science project. The skill to clean data and make it usable for analysis and modelling is one of the most sought skills by hiring managers. Adding a Data Cleaning project to your portfolio will definitely prove your foundational skills in Data Science.
2. Storytelling
Another interesting data science project you can work on is that of Storytelling using data. This basically involves finding hidden insights and patterns from the clean data and making sense of it. It also involves finding relations and connections among various features of the data.
You can even use data visualization libraries in Python to visually narrate the story that your data is trying to tell. This helps you show trends, correlations, seasonalities, etc lying within the data. You can also report effects on the response variable at various cuts in the data which could possibly drive a business decision associated with that data.
3. Machine Learning
Another idea is to take up an end-to-end project where you get the opportunity to show off all your data science skills under one Machine Learning project. Here you can prove your forte right from data cleaning to building a machine learning model equipped with the right algorithms and explanatory results.
A machine learning project is very important for your portfolio, as this can show the recruiters that you have the knowledge of how a complete data science pipeline is handled using the right methodology.
4. Blogging
Data Science blogs can also be a great way to build credibility in your portfolio. You can write informative blogs about various topics in Data Science, or you can write a case study using data and code examples. You can also write your personal experiences in the form of challenges that you might have faced, and how you went about solving those problems.
Blogging also shows that you can communicate your ideas and transfer knowledge in an effective way whenever required. It tells the recruiters that you know how to break down a problem and articulate it in simple words.
Some examples of portfolio for data science:
https://www.claudiatenhoope.com/
https://harrisonjansma.com/archive
Including these examples in your data science portfolio will give employers a well-rounded view of your skills and experience. Checkout the KnowledgeHut Online Data Science bootcamps that will enable you to advance your skills and help employers identify your unique strengths.
Tips to Make an Amazing Data Science Portfolio
Let's look at some of the tips that you can use to build an amazing data science portfolio.
1. GitHub Profile
GitHub is a great platform to work on, store and showcase your projects. Anyone can see your work (if you allow it) and even collaborate with you to make a better version of your project. It's almost important to have an active GitHub profile and share its link in your resume.
You can get started with GitHub by creating a profile page. Then start building projects and document every project on GitHub with links, explanatory images, and descriptions. Try to contribute to the work done by others.
2. Start Small
If you are too overwhelmed about where to start, or which project to take up as your first, don’t worry. You can always take baby steps before taking big ones. You can start with small datasets like Iris, MNIST, Boston Pricing etc. and from there level up yourself. These datasets are also used for training in almost any data science course online.
Once you are confident with these simple and small datasets, you can move on to a more challenging project. You can find a plethora of such datasets on Kaggle. Feel free to check them out and start building your first project for your data science portfolio. You can also check KnowledgeHut Online Data Science Bootcamps to learn how to analyse these datasets.
3. Kaggle
A Kaggle is yet another great platform for Data Scientists. Kaggle has been a part of almost every budding Data Scientist in the past few years. It’s not only important for showcasing your skills but also for practicing them. On Kaggle you will find lots of competitions to take part in, lots of interesting datasets to work on, and an amazing community to learn from.
You can participate in the ongoing challenges and even win rewards if you stand out. You can learn medals and titles like Kaggle 1X/2X/3X/4X Expert, Kaggle Grandmaster etc. These medals and titles can add a lot of value to your profile.
4. Competitions and Hackathons
Participating in data science competitions, hackathons and challenges is a great way to test your skills and learn from the best in the process. Most of the time, these hackathons are conducted by major companies, looking to hire for data roles. And because these companies are involved, the business problem that you get to solve is often times very close to a real-life business problem.
Some of the online platforms that conduct data science hackathons on a regular basis are Kaggle, Analytics Vidhya, HackerEarth, TechGig etc.
5. Portfolio Website
Create a Portfolio website using either HTML or no-code tools like Wix. Apart from showcasing your projects on this website, you can also tell the world about yourself, your interests and other skills that you might have. You can also use this website as a medium to connect with your potential recruiters.
Many hiring companies are impressed if you have your own portfolio website with all your works gathered in one place. It's a great way to stand out and display your skills without distractions or options to browse other profiles, unlike public platforms.
6. LinkedIn
This one goes without saying. I’m sure most of you would already have a LinkedIn profile. The important thing about LinkedIn is that you already have the footfall, you just have to sell your skills.
LinkedIn also gives you the option to add your projects and contributions to your profile. You can connect with a wide number of professionals and leverage the power of professional networking to put your portfolio in front of the right people. Don't forget to add the link to your portfolio. After all, it's not just about building a data science portfolio, it's also about showing it to the world.
7. Blogs
Reading blogs can be a great way to keep yourself updated on the latest trends and technologies. This helps you make a portfolio that is aligned with the current market standards. It’s too important to stay informed to beat the competition and reading blogs can prove to be one of the best and free ways to do so.
I also recommend that you read blogs which tell you about the personal experiences of Data Science experts. This helps you stay exposed to not only the practical problems that one faces in the industry but also to get an intuition on how to solve those problems.
8. Deploy
Deploying the project that you built is often times neglected by data science learners. But it can prove to be a very impressive way to enhance your portfolio. This helps you to build a live working prototype of the model that you have built.
You can easily deploy your code on cloud platforms like Heroku and AWS. This deployed model can even help you impress the recruiters and land a job, as this is working proof of what you can do.
Conclusion
In the end, I hope you all will create your data science portfolio using the given platforms. So, create your GitHub profile, pick your dataset, start participating in Kaggle and other hackathons, build your own website, and update your LinkedIn profile. I know it can all sound very overwhelming to start, but once you get into it, it will get easier.
Frequently Asked Questions (FAQs)
1. How do you make an awesome data science portfolio?
You can make an awesome data science portfolio by adding a variety of projects to GitHub and taking part in Data Science Hackathons. You can also build your own portfolio website.
2. What are the 3 main concepts of Data Science?
The three most important concepts of Data Science are Data Cleaning, Storytelling, and Machine Learning.
3. How do I start a Data Science portfolio?
You can start a Data Science portfolio by working on a small dataset like Iris, MNIST etc. You can then create a GitHub profile and start adding the projects there.
4. What should be in a Data Science portfolio?
The Data Science portfolio should showcase your end-to-end data science skills through a variety of projects, with a focus on your strongest skill.
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