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6 data science projects to boost your data science portfolio

January 13, 2023

In this blog, we will discuss the latest 6 projects that can escalate your data science career and boost your data science portfolio in a competitive era.

With modern analytics tools becoming easier than ever, one needs to do something more than being able to train a model or plot a graph to stand out from the crowd. While using these tools is easier than ever, building something that is impactful and of good quality is not easy and takes practice.

Building a hands-on project is a great way to start working towards this goal, whether you are simply looking to build your analytics skillset or showcase your talent to potential clients or employers. Here are a few project ideas to help you figure out your next analytics project. These are of varying difficulty and across multiple domains, so everyone from seasoned professionals to absolute beginners will find something that interests them in this list. 

Data science projects to build your portfolio – Data Science Dojo


1. Used car price prediction 

Building a simple predictive model is the best way to start if you are new to the world of analytics. Something as simple as predicting housing prices can start off simple based on the number of bedrooms that the house contains and build up to something more complex, such as using location data and proximity to the local school as features.

Here is a dataset containing used cars and the prices that they were sold at. This is a fairly straightforward project to attempt, but it is a great way for you to test different algorithms and feature engineering approaches and see how they impact your predictions. 


 Build your data science portfolio by following these tips


2. Sentiment analysis on social media content 

Sentiment analysis, sometimes called opinion mining, is a branch of Natural Language Processing (NLP) that deals with determining the sentiment behind text data. This could be as simple as detecting positive or negative sentiments or more complex sentiments such as sarcasm and irony, depending on your data and the depth to which you would like to go. Here is a dataset based on tweets about the FIFA World Cup 2022 to help you get started. You can use this to train a model to predict the sentiment behind each tweet.


3. Detecting emotion from speech 

There has been a growing trend of communicating with machines using human speech. Virtual assistants such as Alexa and Siri rely on the ability to understand human speech to function. Understanding the intent and emotion behind human speech is a critical piece of context that can change the response from the machine significantly, and as such emotion recognition is an important task that you can work on.

Here is a dataset that you can use for building your emotion detection model. You will need to work with audio data which can be tricky so be sure to keep that in mind before attempting this project. 


4. Handwritten character recognition 

Optical Character Recognition (OCR) has grown significantly over the past few years. Machines are now able to identify text from an image or video easily, even text that was written by hand, an example of this is Google Lens. To get started with detecting handwritten digits, the MNIST dataset is a good place to begin. 


5. Web scraping 

Building a web scraper is a skill that is useful when you are trying to automate processes. This could be something as simple as checking if a product you want is in stock, fetching job openings, and automatically filtering ones with specific keywords relevant to your skill set. There are many ways to build a web scraper, you can check out our YouTube video on how to build a simple scraper for eBay and automate it using Azure Functions.  


6. Traffic sign recognition 

Computer vision used to be a task for people with exceptional mathematical skills. You had to be able to work out the math on things such as how to extract a specific feature from your image dataset. Today, computer vision is much more accessible because of libraries such as OpenCV. It is much easier to work with image and video data, and as such Computer Vision projects are now within the reach of beginners as well. One such project you can work on is traffic sign recognition, here is a dataset that can help you get started. 


Any data science projects we missed?

Each project idea comes with a brief explanation and a dataset to help get started. The blog emphasizes that building hands-on projects is a great way to stand out in the field and improve one’s skills. It suggests that the projects listed are suitable for both seasoned professionals and beginners.

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