Data science is a career that spans across industries like information technology (IT), business, education, and healthcare. Due to its necessity, there is an escalating demand for data science professionals. One of the ways to land your dream job in data science is by creating a data scientist portfolio.
In this article, you will learn how to make a data scientist portfolio that will impress your potential employer. Keep reading to find out the technical skills, common projects, and important data elements to include, as well as have the chance to see strong data scientist portfolio examples that will help you during your creation process.
What Is a Data Scientist?
A data scientist is an analytical, computer science professional who collects, manages, and transforms valuable sets of data using statistical and algorithmic elements. To become a data scientist you will need to master databases, probability, data analysis, SQL, Python, data visualization, machine learning, and data engineering.
The field of data science is diverse and can lead to plenty of career possibilities such as data engineering, data analytics, and database administration. No matter what your choice of career is, possessing data science skills makes you a sought-after candidate and qualifies you for a rewarding career across many successful industries.
Do You Need a Data Scientist Portfolio?
Yes, you need a data scientist portfolio to impress your future employers. Your portfolio showcases your previous data science projects and skills and conveys your capabilities as a data scientist. It is your opportunity to put forth a personal statement that displays your understanding of in-demand and complex concepts that employers will value.
Your portfolio projects should include exploratory data analysis and visualization, machine learning, statistics, Python, SQL, relational databases, and deep learning, among other personal projects. Be sure to incorporate professional and capstone projects that portray your data science skills and professional competency.
Elements of a Strong Data Scientist Portfolio
The elements of a strong data scientist portfolio include the essential skills and projects relevant to the job listing. The list below details the top five elements that you should include to make a strong data scientist portfolio.
- Contact information. Including your contact information like an email and phone number is crucial for them to contact you. You should also include links to your LinkedIn profile, Kaggle projects, GitHub repository, your data science blog posts, and any other relevant websites.
- A personal statement. A personal statement identifies who you are, your professional background, education, and provides a brief glimpse into the projects you have done. Keep in mind, it may be read by a nontechnical audience. For freelance data scientists, be sure to add your services and rates.
- Data science projects. Types of projects to include are deep learning, machine learning, Python, data and link visualizations, and any other projects you deem relevant. This proves your understanding of advanced concepts in the field.
- Previous professional experiences. Include relevant data science job experiences, freelance projects, or even capstone projects. Showcase any professional certifications, education, or online data science courses relevant to the job posting.
- References. Add several professional or other reputable references to your online portfolio. This can be college professors, colleagues, or project clients who will vouch for you and your abilities.
How to Make a Data Scientist Portfolio
Creating a data science portfolio requires experience. Depending on the experience you have, or the experience you are obtaining, there are five important things you need to include. The steps you take to create your portfolio can be altered based on your personal and professional needs, but below is a guide on how to make a data scientist portfolio.
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1. Obtaining an Education in Data Science
For data science novices or aspiring data scientists, having an educational background in data science is important. Whether it be through formal education at a university or attending popular data science bootcamps, an understanding of the advanced concepts is key.
2. Familiarize Yourself with Common Subject Areas and Skills
Figuring out common subject areas and in-demand skills relevant to your desired job is a crucial step. Data scientists can work across a multitude of industries that have specific skill requirements. Business, IT, artificial intelligence, healthcare, and education sectors are some of the popular industries that hire data scientists, and you should know the skills required for each sector.
3. Come Up with Common Projects Ideas
Once you’ve compiled a list of subject areas and skills, consider what common projects you should tackle that fit within your chosen area. You can use free Kaggle datasets for your portfolio projects, using datasets that highlight the appropriate skills. For those of you who attended data science bootcamps, you can add your course projects to your portfolio.
4. Do the Projects
Put in the effort and build a diverse portfolio with various types of datasets that display your database management, data analysis, deep learning, and data visualization knowledge. Building projects will help you practice your skills as well as improve your portfolio. Look to websites like Github and Kaggle if you need datasets for your independent project.
5. Add Industry Certifications
Professional certifications are a great way to prove your technical abilities and help increase your chances of being hired. There are tons of data science, machine learning, and data analysis certifications offered by reputable institutions including Google, IBM, Oracle, and Microsoft. You can also get certifications from reputable universities.
Data Scientist Portfolio Examples
In the section below are five data scientist portfolio examples you can use as a guide to creating your own strong portfolio. Be sure to incorporate the best elements of each portfolio while selecting project ideas relevant to the job you want, and pay attention to how you want to visually present the information.
Anyone opting out of receiving formal education should use David Venturi’s portfolio as a guide. Venturi dropped out of college to embark on a data science career using online courses as his primary resources. His portfolio includes a link to his blog which appeals to a variety of audiences.
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His portfolio is an amalgamation of project ideas, learning resources, and data science article links. If you are looking to learn Scala, Tableau, and other introductory subjects, then Venturi’s portfolio will lead you to them.
Best Parts of David Venturi’s Data Scientist Portfolio
- Venturi’s data scientist portfolio has content diversity. You will find his courses, articles, personal and professional projects, and career recommendations.
- Another impressive aspect is the various data science topics covered on his page. He provides links to data analysis, machine learning, SQL, and data visualization projects.
- Venturi’s portfolio provides a seamless user experience and is easy to navigate. The contents are divided into specific subsections and the page isn’t crowded.
Hannah Yan Han is a professional data scientist with university degrees from reputable institutions. Han’s portfolio covers data visualization, data science, and creative coding topic areas and includes her personal projects made in Tableau, R, D3.js, Flourish, and p5.js. Her portfolio serves as a great guide for freelance data scientists looking to expand their portfolio projects.
Best Parts of Hannah Yan Han’s Data Scientist Portfolio
- Han’s personal statement and professional achievements are presented in a short and concise manner. Han includes her top professional clients like Twitter and LinkedIn, and all essential components like education and professional background.
- Han’s portfolio presents a diverse collection of projects made with several important data science tools. The projects are made with essential tools that show her expertise.
- Han has appropriately categorized her projects that highlight her UX design skills along with specific data skills.
Donne Martin is a tech lead software engineer at Facebook who has a compilation of system design, coding, Python for data science, AWS, and data visualization projects on his portfolio. Martin’s portfolio is a great source for professionals looking to advance into software and data science integrated careers.
Best Parts of Donne Martin’s Data Scientist Portfolio
- The various technical projects relevant to both software engineering and data science fields highlight his competencies. The project links lead to GitHub repositories that contain in-depth assignments.
- Martin’s personal statement section contains vital information regarding Martin’s work history, designation, technical abilities, and hobbies.
- There is a blog section highlighting his interpersonal communication skills and writing abilities. The posts are informative, covering Hadoop, data visualization, and Python topics essential to a data scientist job.
For those interested in pursuing a career focused on data analytics, Claudia Ten Hoope’s portfolio is for you. Hoope includes professional services, project examples, rates, and an about section apt for a freelance data professional.
Best Parts of Claudia Ten Hoope’s Data Scientist Portfolio
- Hoope’s data scientist portfolio is a great example for all freelance data professionals. The portfolio includes a clear prices and services breakdown essential for future clients.
- She showcases data analytics, front end development, and data visualization projects relevant to a data scientist portfolio, linking to examples.
- It also includes coaching projects that are great for data science newbies looking to get guidance from an established professional.
If a career in machine learning or deep learning is your goal, then Julia Nikulski’s portfolio is a great example. The portfolio is also apt for current master’s students looking to merge their thesis into their professional portfolio. It includes Python, NLP, Bootstrap, HTML, PostgreSQL, supervised machine learning, and statistical hypothesis testing projects.
Best Parts of Julia Nikulski’s Data Scientist Portfolio
- Nikulski’s code, data science, and machine learning projects put forth a capable data science candidate. The projects cover data science, feature engineering, data visualization, PyTorch, deep learning, and web crawling topics.
- Her portfolio leads to several Kaggle assignments and GitHub repositories with projects from HarvardX, and other competitions.
- Nikulski’s portfolio links to a blog page that discusses sustainability aspects across corporate machine learning and statistical sectors. The blogs contain explanatory posts on how to become a successful and empathetic data scientist.
Best Data Scientist Portfolio Project Ideas
The best data scientist portfolio project ideas should display your understanding of programming languages, statistics, data analysis, and database management. Read below to find five of the best data science portfolio project ideas.
- Sentiment Analysis with Python. Qualitative analysis plays a crucial role across customer data in the business sector. This project idea allows you to practice your sentiment analysis with Python skills using Kaggle datasets. You will use Twitter sentiments and posts for analysis through a natural language toolkit.
- Exploratory Data Analysis and Visualization. Data analysis and data visualization tasks are essential in a data scientist’s career. In this project, you will work with crime datasets categorized into regions and types of crime.
- Train Chatbot with Python and TensorFlow. This chatbot project is for you if you want a machine learning, Python, and TensorFlow project idea. You will build a chatbot and train it, using Python to manage the chatbot app and deploy machine learning techniques for training.
- Forest Fire Prediction. This project uses data visualization, NumPy, Pandas, and Matplotlib. This is a suitable project for those who want to work with statistical analytics and prediction.
- Uber Pickup Analysis. This is a data analysis assignment where you will conduct data processing, importing, and plotting. This project uses Python, NumPy, and Pandas and offers date, time, and location as its variables.
Data Scientist Portfolio FAQ
You should include projects showcasing your understanding of statistical modeling, data analysis, Python, SQL, database management, and data visualization skills in your data scientist portfolio.
Yes, a portfolio is necessary to get a data science job as it highlights your professional and technical capabilities as a candidate, helping you stand out. You need to add data science projects relevant to the job listing and the industry.
You can find a multitude of free datasets on Kaggle and GitHub repositories to use for your projects.
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