Projects are an excellent way to gain experience with the end-to-end data analysis process, especially if you’re new to the field of data analysis. Here are some great project ideas for beginners:
Web Scraping
Web scraping is the extraction of data—such as images, user reviews, or product descriptions—from web pages. This information is first collected, then formatted. Web scraping can be done by writing custom scripts in Python, or by using an API or web scraping tool such as ParseHub. Here are two popular ways to practice web scraping:
Reddit is a popular repository for web scraping because of the sheer amount of data available— from qualitative data in posts and comments to user metadata and engagement with each post.
Subreddits on Twitter enable you to extract posts on specific topics. PRAW is a Python package you can use to access Reddit’s API to scrape the subreddits you’re interested in (a Reddit account is required to get an API key). You can then extract data from one or more subreddits at a time. If you’d rather not scrape your own data, you can find Reddit datasets on data.world.
Real Estate
If you’re interested in real estate, you can use Python to scrape data on real-estate properties, then create a dashboard to analyze the “best” properties based on data points like property taxes, population, schools, and public transportation. There are two main Python libraries for data scraping: Scrapy and BeautifulSoup. You can also use the Zillow API to obtain real estate and mortgage data.
Exploratory Data Analysis
Another great project for beginners is to do an exploratory data analysis (EDA), which is the probing of a dataset to summarize its main characteristics. EDA helps determine which statistical techniques are appropriate for a given dataset. Here are some projects where you can work on your EDA chops:
World Happiness Report
The World Happiness Report surveys happiness levels around the globe. This project, from a student at Pennsylvania State University, uses SQLite, a popular database engine, to analyze the difference in happiness levels between the North and South hemispheres.
Global Suicide Rates
While there are countless datasets concerning suicide rates, this dataset created by Siddarth Sudhakar contains data from the United Nations Development Program, the World Bank, Kaggle, and the World Health Organization. Import the data into Python and use the Pandas library to explore the data. From there, you can summarize the data features. For example, you can uncover the relationship between suicide rates and GDP per capita.
Data Visualization
Visualizations are powerful tools for communicating trends, outliers, and patterns in data. If you're new to data analysis and seeking a project, creating visualizations is a great starting point. Choose appropriate graphs that effectively convey the story you want to tell. Bar charts and line charts are useful for showing changes over time, while pie charts are suitable for illustrating part-to-whole comparisons. Bar charts and histograms are effective for displaying the distribution of data. By creating meaningful visualizations, you can enhance your data analysis skills and effectively communicate insights to others.Here are some great data visualization projects for beginners:
Instagram Visualization
This project on KDNuggets makes use of Jupyter notebooks and IPython to analyze Instagram data. Regular Python works fine, but you may not be able to display the images in your notebook. You can use Instagram data to compare the popularity of two political candidates, like this project, or perform a time series analysis on a public figure’s popularity before and after a major event.
Astronomical Visualization
Modern telescopes and satellites produce digital images that are perfect for data visualization. This dataset from data.world shows future asteroids poised to pass near Earth within the next 12 months, as well as those that have made a close approach within the last 12 months. You can view live visualizations based on the dataset here to inspire your own analysis. You can also use this resource to find the asteroid orbital classes for each data point (eg: asteroid, apollo, centaur).
Conclusion
In summary, engaging in data analysis projects is an excellent way for beginners to gain practical experience and enhance their skills. Web scraping, exploratory data analysis, and data visualization projects offer valuable opportunities to collect, analyze, and communicate insights from various datasets. These projects provide hands-on learning and contribute to real-world applications of data analysis.