Build a console number guessing game that prompts a player to choose a number between a specified range of numbers.
Build a console-based basketball team statistics tool to help you divide up a group of players into teams and then provide a team analysis.
Analyze Olympic data from 1896-2016. Use Google Sheets to compute statistics, filter and sort data, apply conditional formatting, create a customized chart, and create a pivot table. Answer questions based on analyzed data.
Build a console application that loads existing inventory data from CSV files into a SQLite database using Python’s SQLAlchemy. The application will allow a user to interact with the records stored in the database to view existing records, add new items, and backup/export the existing state of the database into a CSV file.
Use Python’s Pandas library to analyze pet shelter data to answer important questions for stakeholders.
Use Python’s graphing libraries, Matplotlib and Seaborn, to analyze video game sales data. Use JupyterLab to create graphs and analyze the data.
Use Python’s Pandas library to combine multiple datasets into one before answering analysis questions.
Gather data on the 10 best picture Oscar-winning movies from a movie API then analyze the data using JupyterLab.
Build a console number guessing game that prompts a player to choose a number between a specified range of numbers.
Build a console-based basketball team statistics tool to help you divide up a group of players into teams and then provide a team analysis.
Analyze Olympic data from 1896-2016. Use Google Sheets to compute statistics, filter and sort data, apply conditional formatting, create a customized chart, and create a pivot table. Answer questions based on analyzed data.
Build a console application that loads existing inventory data from CSV files into a SQLite database using Python’s SQLAlchemy. The application will allow a user to interact with the records stored in the database to view existing records, add new items, and backup/export the existing state of the database into a CSV file.
Use Python’s Pandas library to analyze pet shelter data to answer important questions for stakeholders.
Use Python’s graphing libraries, Matplotlib and Seaborn, to analyze video game sales data. Use JupyterLab to create graphs and analyze the data.
Use Python’s Pandas library to combine multiple datasets into one before answering analysis questions.
Gather data on the 10 best picture Oscar-winning movies from a movie API then analyze the data using JupyterLab.
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