10 Popular Data Science Projects

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10 Popular Data Science Projects

For anyone who wants to be a data scientist, it is important to do more than specialize in the data science field. Coming up with projects and innovative solutions will give anyone who wants to be a data scientist all the prerequisites that will help advance their career. Becoming a data science expert is not easy, but you can be as good as those data science consultants from Active Wizards if you take part in the most common data science challenges, applying the various datasets available and designing projects that solve problems that come up. Below are some popular data science projects for a data scientist.

  1. Titanic dataset

Just like its name implies, this dataset gives all the data of the passengers who boarded the RMS Titanic, which sank on April 15, 1912, after hitting an iceberg in the North Atlantic Ocean. This dataset has been commonly used and referred to for those who are data science beginners. The dataset is made up of 891 rows and 12 columns that cover numerous variables, like age, sex, and the ticket class. The dataset is used to test the classification skills of aspiring data scientists.

2. Boston housing dataset

This dataset has been commonly used in pattern recognition literature. It originates from the real estate industry in Boston. The dataset is made up of 506 rows and 14 columns, and can be used in making predictions on the median value of owner occupied homes using regression. It is a small dataset and as such, you can use the technique without fear of exhausting the memory of your PC.

3. Iris dataset

Just like the Boston housing dataset, this one is also used in pattern recognition literature. However, the Iris dataset is arguably the most versatile, simple, and resourceful dataset one can use. The dataset can help you easily learn classification techniques because it is so simple. If you are a beginner in data science, then this is the best dataset to start with. It is used to make predictions on flower class depending on the available attributes.

4. Loan prediction dataset

Financial service industries, mostly in insurance, are one of the biggest users of both analytics and data science methodologies. This dataset gives you a taste of working on data that originates from insurance companies. The dataset is versatile, too, as it contains 615 rows and 13 columns with variables like the challenges faced, strategies used, and variables that affect an outcome. It is used to determine if a loan will be approved or not. 

5. Time series analysis dataset

Time series is one of the most popular data science techniques. It uses a wide range of applications, starting from weather forecasting and predicting sales to analyzing yearly trends. It is commonly used to predict traffic when a new mode of transportation has been introduced.

6. Wine quality dataset

This dataset has been commonly used by data science beginners. It comes in two datasets on which both regression and classification analysis can be conducted. The dataset contains 4,898 rows and 12 columns, and thus is quite important when testing your understanding in various fields. It is usually used to predict wine quality.

7. Heights and weights dataset

This dataset is much more straightforward, so it is a good option if you have just begun your data science lessons. It is a regression problem that comes with 25,000 rows and 3 columns with the variables of index height and weight. This dataset is used to predict both the height and weight of an individual.

8. Black Friday dataset

This dataset is made up of the sales transactions that have been captured at the point of retail. It can hone your feature engineering skills and at the same time help you explore daily multiple shopping experiences. With 550,069 rows and 12 columns, you can easily predict the purchase amount.

9. Text mining dataset

This dataset originated from the Siam text mining competition held in 2007. The data contains the aviation safety reports that describe problems encountered in certain flights. It is a high-dimensional problem comprising 21,519 rows and 30,438 columns.

10. Trip history dataset

This dataset originates from a bike-sharing service in the US. The dataset requires the user to exercise their pro data munging skills. The data is provided quarterly starting from year 2010, and each file contains 7 columns. It is a classification problem used to predict the class of the user.

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