Exciting Data Science Project Ideas To Brush Up Your Skills
7 contemporary data science project ideas to inspire you
Projects have always been thought of as measurable improvements resulting from a result produced, which serve as the icing on the cake for achieving personal or corporate goals.
Talking about individual projects, have you found it challenging to learn at home? Many of us are in the same boat — there are far too many things to handle during these trying times, and learning has taken a back seat, contrary to our expectations.
So, what are our options for getting back on track? How can we apply what we have learned about data science in the real world?
Picking an open-source data science project and sticking with it is extremely beneficial. This not only clarifies the major areas in which you need to develop but also points you in the right direction.
These are not your quotidian data science projects, either. These are initiatives that focus on a specific area of data science, such as computer vision or web analytics. The project could be a dataset, a cutting-edge library that has advanced the data science industry, or even an accessible analytics tool.
Let us now read through some new-fangled data science project ideas that zealous data scientists can pick from.
1. Image conversion to 3D
This is a fantastic example of how computer vision may be applied. At one point in time, transforming an image into a three-dimensional photograph necessitated an extensive and in-depth understanding of technologies like Photoshop. Data science professionals can now accomplish this conversion in just a few lines of code, thanks to advancements in deep learning and computer vision.
That is exactly what this GitHub-based project does. It creates a 3D photo from a single RGB-D input image. This is “a multi-layer representation for new vision synthesis that comprises hallucinated color and depth features in regions obscured in the original image,” if you prefer deep learning terminology.
2. Object detection frameworks
Object detection frameworks data science projects have come a long way in the last several years. From producing simple anchor boxes on static photos to detecting dynamic objects in films, we have come a long way. That is how powerful computer vision can be.
The integration of the ideas of object recognition and re-identification, on the other hand, has been slow (to say the least!). The researchers provide a simple baseline to solve this gap by utilizing one-shot multi-object tracking in this fascinating work.
3. Driver drowsiness detection
Driving nighttime is not only difficult but also dangerous. Many accidents have occurred as a result of the motorist falling asleep behind the wheel.
As a result, our research can aid in the prevention of numerous traffic accidents that occur as a result of such incidents. The major goal of this project is to detect when a driver becomes fatigued and falls asleep while driving. This project makes use of the Python programming language to create a model that can recognize the tired driver’s behavior in real-time and raise an alert alarm via a loud beeping siren.
As a data scientist, you can use this project to construct a “deep learning model” and use it to classify photographs where a human eye is not present. Not only that but another formula line in this model is used to calculate the score.
This score is determined by the length of time the eyelids remain closed. Throughout the driving session, the score is kept. If that score rises above a certain point and reaches a certain criterion, this model will activate workflow automation, causing the alarm to begin beeping loudly.
4. Create a cartoon illustration from an image
If data scientists do not have a lot of free time, this is a happy side thing to focus on. It does exactly what it says on the box: you provide an input image to the model, and it will turn it into a cartoon version. Can you guess what computer vision idea this project is based on?
Yes, Generative Adversarial Networks (GANs) are a type of adversarial network (GANs). Since GANs was open-sourced to the community in 2014, I’ve been blown away by the tremendous progress we’ve seen. There are plenty of different frameworks to choose from, ranging from CycleGANs to StarGANs.
The creators of this data science project have made a pre-trained model available for you to load and run on your PC.
5. Analysis of Uber’s pickup
Is Uber contributing to the worsening of rush-hour traffic in New York City?
FiveThirtyEight, a data-driven news website now owned by ABC, answered this question as one of four. This is a wonderful data science project if you want to improve your data analysis and strong analytical skills.
FiveThirtyEight collected Uber’s rideshare data and studied it to better understand passenger trends, how it connects with public transportation, and how it impacts taxis. They then went on to write thorough news reports based on the data analysis.
6. Recognizing human behavior
The human action recognition model is the subject of this data science research. It will investigate brief films of people performing specified tasks. This model seeks to classify things based on the actions they take. You will need to employ a complicated neural network in this data science project. After that, the neural network is trained on a dataset including these short videos. Then there is the data from the accelerometer that is linked to the dataset. First, the accelerometer data is converted, followed by a ‘time-sliced’ representation. Following that, you must use the ‘Keras’ (an easy-to-use and powerful open-source Python library) to train, validate, and test the network using these datasets using the ‘Keras’ library.
7. Segmentation of customers
In the discipline of data science, this is one of the most prominent projects. Nowadays, digital marketing is an enhanced approach for businesses to reach an audience using their online marketing operations for marketing goals. So, before launching a marketing effort, various customer segments are identified.
Customer segmentation is one of the most widely used applications of unsupervised learning. Companies can now readily identify the distinct categories of clients using clustering methodologies to target the possible user base. Customers are segmented and groups are generated based on common factors such as gender, interest areas, age, and behaviors. They can effectively promote each customer segment based on this information. The project makes use of ‘K-means clustering,’ and you will learn how to visualize gender and age distributions. Annual incomes and average score values of customers can also be examined.
What are some ways to present your data science projects?
Present and potential data science professionals can do the following:
- It should be included in your CV
- Connect these to your Linkedin account
- Keep your Github account active
- Make a website for your portfolio
- Case studies of your initiatives should be written and published on a blog/medium.
Endnotes
It is critical to understand the theory behind data science. However, project-based learning is essential for properly grasping the data science process.
After you have completed some or all of these data science projects, it is a good idea to go back and study a few more topics before moving on to more difficult ones.
After you have gained confidence, you can go on to more advanced projects. You should get your hands on the aforementioned suggestions if you want to boost your data science skills.
