By Katherine Olowookere

Dec 18, 2022

Anyone can claim to be a good data scientist on their résumé, but hiring managers want to see tangible proof to support that claim. Otherwise, get ready to get dropped like a lousy AOL connection.

Early career professionals require more than simply a solid theoretical basis to succeed in the field of data science. Today's hiring managers are looking for data scientists with practical experience completing projects that address real-world issues. You need to have "experience" proving your capacity to deliver them even before landing on your first job.

Data science projects in your portfolio serve as evidence to employers that you possess the skills necessary to be successful in a data science position rather than simply telling them. It is therefore very important you pay great attention to working on Data Science projects and the kind of project you choose to work on.

Data Science projects on your portfolio also help the hiring managers to be able to ask technical questions based on your experience. If you only list a bunch of degrees and courses on your resume without any specific Data Science project, it would be very difficult for them to find a specific topic that they can put their finger on and ask you deep technical questions. Sometimes this wouldn’t be fair to you too.

This article will show you how to choose and build a Data Science project wisely in 2023 plus the top beginner Data Science projects you start with.

When you set out to do a project think about the followings:

  1. Business Acumen: What the problem is. Why is it important for the business?
  2. Probability of Success: How likely will this project be a success? Can we solve it? How should we solve it?
  3. The Impact of the Project: How impactful is this project going to be? Can the results cause a real change?
  4. Your Learning: What will you learn by doing this project?

How to Choose a Data Science Project

Make your projects unique and authentic 

Data science projects are very important for a successful Data Science career. A typical project allows you to use skills in data collection, cleaning, analysis, visualisation, programming, machine learning, and so on. It helps you take your skills to solve real-world problems.

The best portfolio projects aren’t those that use the latest or most complex tools and models. Instead, portfolio projects which capture the most attention are those that come from a place of authentic passion. If you have painstakingly scraped a dataset for a specific task, written a compelling story, or created something that tells the world about your passion, people will notice this.

Any project you choose to work on has to be unique and important to you. Your personal interests and professional passions must reflect in whatever it is that you're working on. This is what makes you attractive to recruiters and hiring managers –the passion, uniqueness, and creativity that you show in your work.

Do it end-to-end 

End-to-end simply describes the process a system takes from the beginning to the end of a project to deliver a complete functional solution.

An end-to-end Data Science project includes the following steps in order:

  1. Data Collection
  2. Data Cleaning
  3. Exploratory Data Analysis
  4. Modeling
  5. Evaluation
  6. Deployment

Doing end-to-end projects shapes you into becoming an independent data scientist that can single-handedly identify a problem, design a solution, ship it, and measure outcomes.

Being more end-to-end improves your ability to create meaningful impact and solve problems independently.

Do it well and enjoy it  

Anything worth doing at all is worth doing well. If you are going to spend time doing a project you should do it as well as possible. For every project, strive for excellence.

Avoid cookie-cutter projects that have already been analyzed and cleaned by hundreds of people in the past. Avoid using datasets like the Titanic, MNIST, or Iris at least in the exact same way that many have done before you.

These datasets have been recognized worldwide as datasets used by pure beginners at the start of their careers to learn how to test models and clean data. Displaying such projects in your portfolio might send the wrong message to recruiters about your position and level in the Data Science journey, causing them to lose interest in you.

It is unsafe to display a widely used project in your portfolio. Many of these recruiters/ Data scientists looking at your portfolio to hire might have also worked on these popular projects themselves in the past or they might have seen it in the portfolio of several other applicants.

Therefore it is important for you to deliberately go all out and seek out new and creative datasets/data science projects that you are genuinely interested in and work on them. This will make you interesting to recruiters, it will show your passion for Data Science and will also help in getting the attention of hiring managers.

The whole point of doing projects is for you to stand out from the competition and to showcase your creative data skills. So if possible avoid working on these popular datasets, particularly because there are a lot of publicly accessible tutorials centered around these datasets.

Don't try to be perfect 

Get comfortable with your work and the results that you get. It doesn't have to be perfect but you have to present it and answer people's questions about every possible detail. You should own your projects.

An essential skill for any job is the ability to succinctly and simply explain a complex subject; this skill should be highlighted in your portfolio projects.

Make sure your code is understandable and properly documented. Ensure that notebooks have titles and descriptions. Add comments to the functions in your code as you go along. If someone takes the time to read through a notebook, they will keep track of remarks and variable names that are easy to understand.

Top Data Science Projects for Beginners in 2023

Here are two examples of Data Science projects you can build in 2023 to get the attention of prospective employers. Remember to only work on projects that truly interest you.

  1. Leaf Disease Detection: Data Science Project Idea: Disease detection in plants plays a very important role in the field of agriculture. This Data Science project aims to provide an image-based automatic inspection interface. It involves the use of self-designed image processing and deep learning techniques. It will categorize plant leaves as healthy or infected.

    Dataset: Leaf Dataset

  2. Brain Tumour Detection : Data Science Project Idea: There are many famous deep learning projects on MRI scan datasets. One of them is Brain Tumour detection. You can use transfer learning on these MRI scans to get the required features for classification. Or you can train your own convolution neural network from scratch to detect brain tumours.

    Dataset: Brain MRI Image Dataset


It's undoubtedly a terrific approach to learn data science by reading books and watching tutorials, but nothing beats actually developing end-to-end projects for difficult data science problems that you can own the work. The best method to develop your data science abilities and move closer to mastery is to work on a variety of engaging data science projects.

Do you want to become a Data Scientist? Do you need guidance in developing your Data Science portfolio projects?

O'Fallon Labs can help you get started. Our qualified Data Science mentor can work with you in uncovering your passions in Data Science, finding interesting projects, and building them from start to finish. Begin today by scheduling a FREE session. 

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About O'Fallon Labs

In O'Fallon Labs we help recent graduates and professionals to get started and thrive in their Data Science careers via 1:1 mentoring and more.

Katherine Olowookere

Katherine is a content manager at O'Fallon Labs. She is interested in writing about a varioty of topics including careers in technology. Katherine holds a B.Sc. in E. Physics. She is passionate about personal growth and making young people become better versions of themselves through personal self development

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