12 Data Science Projects for Beginners and Experts

 The best way to gain more exposure to data science apart from going through the literature is to take on some helpful projects that will upskill you and make your resume more impressive. In this section, we’ll share a handful of fun and interesting projects designed for all skill levels.

If you fancy data science and are eager to get a solid grip on the technology, now is as good a time as ever to hone your skills. The purpose of this article is to share some practicable ideas for your next project, which will not only boost your confidence in data science but also play a critical part in enhancing your skills.

Data science is a profession that requires a variety of scientific tools, processes, algorithms and knowledge extraction systems that are used to identify meaningful patterns in structured and unstructured data alike.






Credit card fraud is more common than you think. In fact, it’s an issue that has now impacted around 60 percent of credit card holders in the United States. But thanks to the innovations in technologies like artificial intelligence, machine learning and data science, credit card companies have been able to successfully identify and intercept these frauds with sufficient accuracy.

The idea behind this is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones. For this project, you can use either R or Python with the customer’s transaction history as the data set and ingest it into decision trees, artificial neural networks and logistic regression. As you feed more data to your system, you should be able to increase its overall accuracy. 

In today’s connected world, it’s become ridiculously easy to share fake news over the internet. Every once in a while, you’ll see false information being spread online from unauthorized sources that not only cause problems to the people targeted but also has the potential to cause widespread panic and even violence.

To curb the spread of fake news, it’s crucial to identify the authenticity of information, which can be done using this data science project. You can use Python and build a model with TfidfVectorizer and PassiveAggressiveClassifier to separate the real news from the fake one. Some Python libraries best suited for this project are pandas, NumPy and scikit-learn. For the data set, you can use News.csv. 

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