Data Science professionals are tasked with managing and manipulating large datasets to find insights or make predictions. They use tools like statistical models, algorithms, and machine learning to make their data more digestible and easier to understand. The role requires an expertise in one or more coding languages that are well suited to data manipulations, storage, and querying, such as Python and/or SQL. Plus, they often use other advanced technologies, like Matplotlib, Internet of Things, Automation, Tableau, ETL, JavaScript, and more.
As you can see, demonstrating the “story” behind the data, can involve quite a bit of complicated knowledge and encompass a wide range of scientific methods and procedures. So there’s no doubt that gaining hands-on experience and honing your skills is absolutely essential when it comes to landing a coveted position within the field.
When it comes to gaining this experience, there’s no better way than completing data science projects. Ranging from beginner to advanced and utilizing a variety of coding languages and techniques, completing the projects listed here will enhance your confidence and technical expertise. We’ve included links to the source material for each one, so you’ll be ready to get started!
Why Complete Data Science Projects?
You might be wondering if it’s really worth your time to put in the hours on projects like these. Regardless of where you are in your data science career, you’ll likely find something to gain with project-based learning. Here are just a few benefits you might find.
- Gain Practical Knowledge – The theoretical can only take you so far. When it comes to skills like picking up a coding language, learning by doing is the absolute best way to gain mastery. Even if you already have the basics, completing projects will help you become more efficient in your work and feel more confident in tackling new problems and projects you haven’t encountered.
- Build a Portfolio – Many employers in the Data Science field will ask to see a portfolio of your previous work and visualizations or a personal website. If you haven’t had the opportunity to gain professional experience, this can be easier said than done. Use projects as a means to fill gaps in your professional background and strengthen your industry knowledge.
- Widen Your Skillset – Looking to enhance that “skills” section of your resume or LinkedIn? Complete projects to deepen your understanding of adjacent concepts and show practical experience in skills and tools that might be outside of your day-to-day core responsibilities.
- Demonstrate Your Experience (and Passion) – A surefire way to level up your career quickly is by showcasing your passion and expertise of the industry through real-world projects like these that leverage Data Science trends and technologies. All of the examples here are a free and time efficient way to become an expert before your job search even begins!
With that being said, let’s get into some of the amazing projects you can choose from!
Beginner Data Science Projects
Detect Fake News with Python
Media literacy is a critical skill. Did you know that determining an information’s legitimacy can actually be detected using data? This project uses Python, one of the most popular data science programming languages, to create a model to distinguish between accurate and fictional news sources. Learn various natural language processing techniques and machine learning algorithms and gain experience with three additional packages, SKlearn, numpy and scipy.
Project Tutorial and Source Code
Build Chatbots
Have you noticed how much easier it is to interact with customer service since the widespread implementation of chatbots? These intelligent pieces of software can handle an unlimited number of customers at a time and are powered by Natural Language Understanding (NLU) and Natural Language Generation (NLG), two key data science concepts. This project is a step-by-step tutorial that takes you through using recurrent neural networks, a dataset from an intents JSON file and coding with Python. In many industries, you’re sure to encounter projects like this one on the job!
Project Tutorial and Source Code
Complete a Sentiment Analysis
One of the most useful (and coolest) applications of data science is the ability to categorize words based on their sentiments in a completely automated fashion. The language used here is R, which is another widely used language especially when it comes to data modeling. This particular project classifies the data into different classes, both binary in nature (positive or negative) as well as by multiple magnitude classes (happy, sad, angry etc.) You’ll also be exposed to three general purpose lexicons in R: AFINN, bing and loughran.
Project Tutorial and Source Code
Dive into Market Segmentation
Here’s another technique you’ll definitely encounter, with all types of real-world application. Data Scientists perform market segmentation to understand demographic, psychographic, behavioral and preference data across their customer base. Doing this at scale requires leveraging data science techniques like supervised learning and using R. The end result of this tutorial is customer clusters that can be used for targeted advertising across email and social media campaigns. To get there, you’ll use need-to-know techniques in R like principal component analysis (PCA) and K-means clustering
Project Tutorial and Source Code
Intermediate Data Science Projects
Recognize Emotion in Speech
The sentiment of written text is one thing, but asking an algorithm to understand the emotion of the human voice is something else entirely. The subjective nature of how we speak makes creating this model a bit more challenging, but certainly not impossible. This project uses the librosa Python package to perform speech emotion recognition and create a model. You’ll use the mfcc, mel and chroma features and learn how to develop an MPLClassifier within this project.
Project Tutorial and Source Code
Detect Driver Drowsiness
Here’s a project that you might’ve not realized data science could potentially solve. If ever implemented on a widespread basis, alerting drivers who are not paying attention to the road, could prevent thousands of accidents and deaths worldwide. This Python project requires the development of a deep learning model. Along the way, you’ll utilize packages, such as OpenCV, TensorFlow, Pygame, and Keras.
Project Tutorial and Source Code
Identify Dog Breeds with Neural Networks
For all the budding data science animal lovers out there, here’s the perfect way to combine both of your interests! Image classification is a popular skill when it comes to building predictive models. This analysis leverages neural networks, with Keras through Jupyter notebooks to see if a model would be better at humans than identifying dog breeds. This is a fun project, with practical applications that includes effectively processing images with network design, transfer learning with neural nets and exploratory data analysis.
Project Tutorial and Source Code
Diabetic Retinopathy Detection
Here’s a project that is already making a material change for the better across the world. Diabetic Retinopathy is a leading cause of blindness and this neural network project can identify whether a patient has the condition or not, before they begin to lose their vision. One of the best aspects of this project is that it leverages data that mirrors real-world conditions. This means, you’ll have to develop robust algorithms that can function in the presence of noise and variation, such as images that contain artifacts, are out of focus or are under or over exposed.
Project Tutorial and Source Code
Advanced Data Science Projects
Recommend Films to a User
Major companies like Netflix and HBO have spent millions on developing proprietary algorithms that increasingly guide user decision making and set apart different streaming platforms. This project uses R to generate a machine learning model, similar to the ones you’ve probably encountered. You’ll use a matrix factorization based model and convert data to a CSR format to understand what powers these increasingly relevant algorithms.
Project Tutorial and Source Code
Detect Credit Card Fraud
This project combines quite a few of the skills listed above to create a practical model in the field of fraud detection. You’ll use R, alongside decision trees, artificial neural networks, logistic regression and gradient boosting classifiers to determine the validity of a given transaction. As you can imagine, models like these are widely used across the retail and banking industries, so gaining experience here is a wise idea for any data scientist.
Project Tutorial and Source Code
Visualize Climate Change
For nearly the past decade, each calendar year has been warmer than the one recorded before. As the scientific community searches for ways to ensure the planet’s long-term sustainability, data scientists have a role to play. This project uses Python to visualize the changes in global mean temperatures due to rising CO2 levels in the atmosphere. You’ll use popular libraries, like Pandas, Matplotlib and Seaborn to splice and visualize the data into line graphs, scatter plots and more.
Project Tutorial and Source Code
Recognize Handwritten Digits
This project takes image recognition to the next level by using convolutional neural networks to create a model that can differentiate between unique handwritten digits. You’ll download the MNIST dataset, which contains over 60,000 images to create your model and utilize Python and the Tkinter and Keras libraries.
