Summary

Answer complicated questions, uncover greater insights and learn high-demand skills with Python and the power of data science. This course provides the foundational knowledge needed to move beyond the limitations of traditional spreadsheets to streamline and automate time-consuming tasks and encourage data-driven decision making.

Associated project work

Students will work with pandas and the College Scorecard data set to determine the top universities using a defined metric. To identify these top schools, students will need to perform common pandas operations like filtering and merging data sets. In additional, students will have to make web requests to reconcile missing data before it can be properly incorporated into the analysis.

Students will work with pandas and the College Scorecard data set to answer questions relating to faculty pay and tuition, requiring the use of grouping and aggregating pandas operations. They will also need to apply statistical analysis to calculate z-scores to quantify atypical values and work across multiple files representing data for different years.

Students solve several simple programming exercises in Python, to demonstrate the proficiency for more advanced courses.

Students will parse a file JSON encoded captions and extract which people appear together. Using this information, they will use basic Python tools to analyze how these people are connected to each other.

This module is currently part of our Data Science Essentials Course.