Given the large number of movies and series available on the platform, it is a perfect opportunity to data manipulation skills and dive into the entertainment industry.

Using python to understand U.S. bike-share data and calculate statistics and build an interactive environment where a user chooses the data and filters for a dataset to analyze.

Analyzing Police Activity with pandas, cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data.

Investigating a dataset on wine samples by going through the entire data analysis process and building more skills with Python for data analysis.

Investigating a dataset on fuel economy and learning more about problems and strategies in data analysis.

Investigating a dataset on movies data and trying to analyze the data by asking questions and testing theories by getting answers from the data.

Doing a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories and looking for insights into the data to devise strategies to drive growth and retention.

Reading in, cleaning up, and visualizing the real-world project repository of Scala that spans data from a version control system (Git) as well as a project hosting site (GitHub).

The Nobel Foundation has made a dataset available of all prize winners from the start of the prize, in 1901, to 2016. Let's load it in and take a look.

Using REST API to visualize to display the movements of the price of a currency using candlesticks.

Using yfinance to extract finance information to determine any suspicious stock activity.

Using yfinance & Web scraping to extract finance information to determine any suspicious stock activity.

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data.

Analyzing and predicting housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on.

Using historical data from different stores in the USA, Germany, and Egypt, applying exploratory data analysis for this data, calculating GMV & MOM for each country, visualizing the results, and building a dashboard using Tableau.