![\"Star](\"https:\/\/pages.charlotte.edu\/brian-magi\/wp-content\/uploads\/sites\/68\/2021\/01\/TLJ-stats-1024x575.png\")
You can ask Kylo Ren and Rey about the best way to tackle data analysis. Yes, I made my own meme using a scene from Star Wars: The Last Jedi.<\/p><\/div>Course Catalog Description for 4122<\/strong>: Develop techniques in a programming environment to quantitatively summarize data related to Earth sciences using univariate and bivariate statistical calculations; defend interpretations of Earth science data from both statistical and physical perspectives.<\/p>\n Course Catalog Description for ESCI 5122<\/strong>: Develop techniques in a programming environment to quantitatively summarize spatiotemporal data related to Earth sciences using univariate and bivariate statistical calculations; defend interpretations of spatiotemporal Earth science data from both statistical and physical perspectives.<\/p>\n Prerequisite<\/strong>: For ESCI 4122, the prerequisite is MATH 3122, MATH 4122, STAT 1220, STAT 1221, STAT 1222, STAT 2122, or STAT 3122. For ESCI 5122, it is graduate standing in Earth Science or Geography or a PhD program.<\/p>\n Goal<\/strong> (same for both 4122 and 5122): The goal of this course is for you to learn how to use a progamming language to interrogate an Earth and\/or Environmental Science data set using statistical methods, and then describe the results of your statistical analysis.<\/p>\n Objectives for ESCI 4122<\/strong> Objectives for ESCI 5122<\/strong>
\n1. Understand how statistics are calculated and evaluated in a programming environment using real data
\n2. Be able to effectively perform exploratory data analysis and linear regression testing to learn more about a dataset than what was measured
\n3. Accurately and concisely summarize analysis of a large, multidimensional dataset to other people
\n4. Defend conclusions about a large, multidimensional dataset using quantitative arguments related to both statistical calculations and physical constraints<\/p>\n
\n1. Develop a working knowledge or improvement in programming skills using statistical calculations centered on a spatiotemporal dataset
\n2. Be able to effectively perform exploratory data analysis and linear regression testing to learn more about a spatiotemporal dataset than what was measured
\n3. Accurately and concisely summarize analysis of a large, multidimensional dataset to other people
\n4. Defend conclusions about a large, multidimensional dataset using quantitative arguments related to both statistical calculations and physical constraints, and to peer-reviewed literature<\/p>\n
![\"XKCD\"](\"https:\/\/imgs.xkcd.com\/comics\/correlation.png\")
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