College Performance, Debt and Earnings

Analyzing Student Completion and Loan Repayment Rates

By Education [source]

About this dataset

> This College Scorecard dataset offers a comprehensive look into the performance, cost, and outcomes of U.S. colleges and universities. It contains an extensive amount of data detailing information related to cost of attendance and various outcomes such as average salary after graduation, loan repayment rates, and gainful employment rates for graduates. The datasets also provides information on program level demographics such as gender breakdowns among enrolled students and faculty diversity in programs attended by students. This is an invaluable source of information for anyone who wants to make informed choices about their college education experience in terms of both costs and expected returns after graduation. Going beyond financial metrics, this dataset give insight into the cultural climate at each college or university so that users can analyze whether their unique backgrounds or experiences will fit into the campus ethos at those institutions. With this data set available to everyone interested in higher education options, individuals have a powerful tool to compare options from many perspectives including financial investment returns economics , educational quality measures , graduate success rate indices , faculty diversity break-ups etc

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How to use the dataset

> > This guide will provide guidance on how to utilize this powerful dataset to explore various stats related to college outcomes. > > > Data Preparation: > Start by looking into the SQL query which is provided in order to get a better idea of what columns are available in this set. This will allow you to start thinking about what kind of information you want from your analysis and create an appropriate query for it (if desired). Additionally take a look at the column descriptions file which may help explain some variable names and their meanings for further data manipulations that may be required prior to analysis or plotting. > > > Columns Selection/Data Cleaning: > Once you have a good idea of what variables are available in each column it's time to select what specific variables you want in your analysis using tools such as SELECT, WHERE etc... Furthermore, use a combination of cleaning methods like reordering columns, removing outliers or observations based on criteria like missing values or extreme values etc.. This process should allow us us refine our raw data depending upon our requirements before beginning exploration. > > > Exploratory Analysis & Visualization: > Now we can start our exploratory analysis process by examining relationships between different independent & dependent variables- with charts & tables being used heavily when coming up with insights/inferences from our plots & tables. Tools such as pivot-tables can also be used here depending upon our requirements (generally these come handy when dealing with large datasets). Alongside exploratory analytics many statistical tests may also come useful if we're required identify statistically significant relationships like ANOVA Test for two or more groups , regression models etc.. Finally after generating interesting findings visualizing them using tools like ggplot2 is strongly advised- enabling easy presentation & understanding amongst readers even outside data science world . > > > By following these simple steps we should now have a better understanding off how unlock valuable information using Unlock College Performance Debt & Earnings Outcomes dataset provided by Kaggle

Research Ideas

> - Analyzing the correlation between geographic region and performance outcomes of college students. > - Investigating the cost-benefit analysis of tuition rates for colleges across different income brackets in order to make college more accessible for students from poorer economic backgrounds. > - Studying the historical trends in student loan debt, with a focus on origination amounts and repayment periods, to gain insights into how debt impacts graduates' financial management strategies in life after college

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source >

License

> > See the dataset description for more information.

Columns

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit Education.