CS 230 Data Analysis

This course considers how to take data sets and describe them using descriptive statistics which will primarily be generated with software tools. Emphasis will be placed on students learning to analyze the problem setting and reaching and communicating statistically justifiable conclusions about those data sets. Focus is on the use of software analysis tools to explore data distributions and graphing; univariate and bivariate data; measures of central tendency, relative standing, and variability; probability distribution; the Central Limit Theorem; and hypothesis testing. 

Credits

3

Prerequisite

CS 160 or permission of instructor

Offered

Spring

Student Learning Outcomes

1. Explain the difference between categorical, discrete numerical, and continuous numerical data
2. Create appropriate graphs to assist in the visualization of data
3. Determine appropriate methods of gather sample data in order to complete a study of experiment
4. Generate descriptive statistics to analyze data, including the mean, median, correlation coefficients, regression lines and slopes, standard deviation, the coefficient of determination, confidence intervals, etc.
5. Explain the variability of sample statistics and their relationship to population statistic
6. Translate a research question into null and alternative hypotheses, carry out appropriate testing measures and interpret the results in context
7. Use appropriate computer tools to produce numerical and visual analysis of data
8. Discuss ethical considerations for data privacy and possible misuse of statistics
9. Interpret and apply statistics in a business and managerial context