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How to build a Data Science Portfolio

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Updated on
May 10, 2023
How to build a Data Science Portfolio
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One of the most important steps to take when planning how to become a data scientist is deciding how you will showcase your skills, accomplishments, and knowledge.

A professional portfolio is an important means for building connections for data scientists. To get started, assess the skill sets that you have mastered (or that you are learning). Based on those skills, build a portfolio showcasing your contributions, internship opportunities, and job experiences.

The requirements in data science job postings make it challenging for every job applicant to stand out. However, your portfolio can strengthen your efforts in data science job hunting. Consider your public portfolio as the skeleton of your skills. You can apply for a wide variety of entry-level data science jobs by showcasing your data skills to impress employers through your portfolio.

In this guide, we explain the importance of building a portfolio through projects and provide factual tips that will open doors for you.

Why You Need a Data Science Portfolio

Looking for an entry-level data science job can be a discouraging experience because you need experience to land a job, but you also need a job to gain experience. It can be confusing for beginners. Most data science job roles require years of prior experience, making it difficult to break into the field.

How can you get your foot in the door without data science experience? Will a Data Science Certification help you if you don't have the required experience? NO.

There are many ways to get an entry-level data science job, such as internships, advanced bootcamps, and master's degrees, but the one thing that has helped many people is creating a portfolio. To get a job in Data Science, you need to demonstrate expertise through real-world projects.

When building data science skills, go the extra mile and work on data science projects and public datasets to stand out. You can deploy your project for public use through GitHub and write articles that explain your findings.

Data scientists are always curious to see what other data scientists have done.

In simple terms, the easier it is for people to find these projects, the easier it becomes for hiring managers to evaluate your skills.

Data Science Projects

One of the best ways to get started in data science is by working on projects, and there are many free resources online.

While many data science projects may seem difficult, as you learn the basic concepts of statistics for data science, you can perform a lot of tasks to improve your data science skills.

Data science projects push you to spend more time learning about programming, performing statistical analysis, deploying solutions, and creating data visualizations to communicate results effectively.

Here are some key reasons why working on data science projects is worth your time and why creating a portfolio will boost your career prospects.

  • Hands-on experience: Working through a data science project will cement your knowledge and bootstrap your confidence to talk about it.
  • Data Community: You can connect with people dedicated to data science and machine learning on platforms like Kaggle, Reddit, and Stack Overflow to receive free and expert guidance.
  • Contributions: Data scientists that find your projects interesting will also look through your portfolio in order to gauge your skills, experience, and interests, and may even recommend you for open-source contributions.
  • Internships: Showcasing projects on your portfolio is often a key tool in the finding internships opportunities.
  • Jobs: Finding opportunities is the main reason for building a portfolio and you increase the chances by showing the work you have done on the projects.

We've thoroughly discussed why the basics should never be discounted when building the right foundation in our data scientist skills article. Now, in this guide, we focus on specialization through projects.

Working on data science projects and public datasets will help you build intellectual curiosity and focus on specializing in one specific field.

It's like wanting to become a specialist in a particular profession; you should first learn everything essential to becoming a generalist in that profession.

It takes time, diligence, hours of research, and working with data.

There are many ways to showcase your work while you are still learning, making it easy to build a strong data science portfolio.

Let's take a closer look at what they are and how you can employ them.

Creating Projects

As a beginner, you can start with easy projects and observe how your peers create well-documented projects and communicate the quality of analyses.

It matters what projects you create and how you make the best use of resources, such as scientific libraries, packages, and tools at your disposal. You are essentially learning concepts and growing logical reasoning skills while making the optimum use of your time by identifying purpose.

Without purpose, your efforts are in vain, but the debt of purpose can be realized by answering questions such as:

  • What problem am I solving here?
  • How would I benefit from my analysis?
  • What skills will I gain?

Projects are not substitutes for your work experience, but if you dedicate time to improving your skills, you can show the expertise that most people gain through work experience.

As you learn through projects, cultivate the habit of documenting your work on platforms like GitHub and Deepnote.

Projects Portfolio and Documentations

Portfolio projects that capture the most attention are those that are well-documented. Documentation can make or break the success of your projects and your portfolio overall.

Code quality is of paramount importance for relevance and clarity. If your work is not simple, it is not exceptional.

Here is an example of elegant Python code.

import pandas as pd

# Load a sample dataset from seaborn library
from seaborn import load_dataset

# Load the 'titanic' dataset
data = load_dataset('titanic')

# Display the first 5 rows of the dataset
print(data.head())

# Calculate the mean age of passengers
mean_age = data['age'].mean()
print(f"Mean Age: {mean_age}")

# Group the data by 'class' and calculate the average fare for each class
average_fare_by_class = data.groupby('class')['fare'].mean()
print(average_fare_by_class)

# Create a new column 'age_group' to categorize passengers into age groups
data['age_group'] = pd.cut(data['age'], bins=[0, 18, 60, 100], labels=['Child', 'Adult', 'Elderly'])

# Count the number of passengers in each age group
age_group_counts = data['age_group'].value_counts()
print(age_group_counts)


This code snippet is human-readable, with comments explaining the use of each function and variable in fewer words.

A good portfolio project showcases both your technical and soft skills. Expanding through writing and showcasing contributions will enhance your chances of getting noticed by potential employers, as the intended use of your portfolio is to provide a quick tour of your skills.

If you've spent hours scraping a public dataset for a specific task, you could also create a project repository to make your scraping tool accessible and document the entire process by writing an article about it that demonstrates your technical skillset.

Here's an example of a great portfolio:

All the relevant information is on the homepage. "I am Chris Tran, a Machine Learning Engineer in Deep Learning, NLP, and Computer Vision. What else is there to know?" It is punchy and direct to the point. Chris has an educational background in Statistical programming and Machine learning.

The main thing to take from Chris Tran's approach is simplicity and organization. The portfolio section clearly shows that Chris has put careful thought into showcasing his skills by writing in-depth tutorials explaining every important detail for each project.

He is driving visitors from his project repositories. He creates a clear and intuitive README file for each repository with links to a topic-specific article for learning the concepts involved in building the project. This is a brilliant approach to maintain a healthy portfolio.

It's worth noting from this brief clip how Chris gets into detailed case studies on his website, where we also get to learn about his personality and communication skills.

Tip: Learn project documentation from READMEs guide by GitHub.

Publishing

Again, the most important aspect of deployment is code quality. Learn the best practices to write your programs more effectively. This will help you learn what to include and what to avoid, how to strike the right balance, and why it's the best choice.

You might benefit from the book Effective Python to learn specific ways to write better Python code. It's highly recommended in the developer community.

Your work will not go unnoticed. Honing your coding skills and learning from others will help you become a better researcher.

You could configure a local Jupyter environment with GitHub or Deepnote to publish your projects. The single document approach with Jupyter Notebook makes it easy to develop, visualize, and add information and formulas that make work more understandable, repeatable, and shareable.

This is what data scientists are doing. It is a common practice to demonstrate that you have the technical skills and ability to explain complex topics in a way that is understandable.

3 Tips for Building a Strong Presence

With building a professional portfolio, your goal should be to stand out and be one of a kind, not one of many.

These tips will help you persuade potential employers that you are uniquely qualified for a position.

Join Kaggle

Kaggle is the largest and most trusted online community for data scientists and machine learning enthusiasts. You can collaborate with other users, find and publish datasets, use GPU-integrated notebooks, and participate in competitions to solve data science challenges.

Employers pay a lot of attention to your Kaggle profile. A strong profile will surely result in a lot of exposure, which can help you get an entry-level job.

It is great for learning machine learning. It's completely free, including all datasets, participation in competitions, and discussions. You can also connect with recruiters through the Jobs Board.

Datasets

It's a great platform to learn how to think and solve real-world problems. You can generate project ideas from real-world datasets, with over 160k of them to keep your motivation high throughout the learning process.

Competitions

Companies such as Google and American Express host Kaggle competitions. Your performance is a powerful way to stand out from the crowd and show your abilities in solving complex problems.

The competitions usually last for three months, offering anywhere between $10,000-150,000 in prize money. There are only 94 grandmasters in the world, and most of them have been using Kaggle for over two years.

Be open to sharp criticism; Kaggle offers aspiring data scientists the best chance to learn from qualified people for free.

The expertise you gain on Kaggle will be invaluable.

Always use GitHub

GitHub keeps track of your daily contributions. Your work is publicly visible, and people can see your working knowledge and commitment to data science.

You should make the most out of GitHub. Data scientists universally use GitHub because it hosts nearly all data science repositories, powerful libraries/packages, and tons of other programming resources.

One of the best ways to highlight your skills is to have an active presence on GitHub. Having an active GitHub profile could open up tremendous collaboration or internship opportunities that you can also showcase in your portfolio.

You can host both code-based and content-based projects on GitHub.

Project Example 👇🏾👇

It’s clear at a glance where Chris Tran's skills lie: Python, machine learning, and building AI systems.

It's always a good practice to put the code you’ve written up on your GitHub profile regularly. You can create a static website like Chris Tran's with GitHub Pages to host your blog and portfolio for free.

You can easily customize your GitHub profile page, add links to your articles, and showcase your projects. It's best to link your GitHub, LinkedIn, and Kaggle profiles.

It's easy to gain familiarity with Git and GitHub terminology, such as repository, branch, commit, pull request, etc. You can learn from the official guide or the recommended resources below.

Write as you learn

Data science blogs can be a fantastic way to improve your communication skills, present your analysis, uncover unique insights, and publish data visualizations.

While it's true that you can show your expertise through projects, you should also start writing tutorials as you grow. You'll build readership if you write high-quality tutorials.

Marketing Tip: We recommend publishing articles on your blog and then republishing them with a canonical link on platforms like Medium, Dev.to, and Kdnuggets.

There is no one portfolio format that works best. The common denominator, though, is that you should focus on your specialty, skills, and notable accomplishments.

Your portfolio should have an intriguing description that drives people to check out your projects, tutorials, articles, etc.

Thanks for making it to the end...

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