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If you are enrolled in courses delivered in traditional or hybrid modalities, you will be expected to attend face-to-face instruction as scheduled.
| Semester: | Fall 2020 |
| Number: | 0144-225-001 |
| Instructor: | TBA |
| Days: | Tuesday Thursday 1:40 pm - 2:55 pm |
| Note: | Online, Both synchronous and asynchronous |
| Location: | Online |
| Credits: | 3 |
| Notes: |
Classes Will Meet Synchronously On September 24, October 22, And |
| Course Materials: | View Text Books |
| Description: |
Students will be introduced to statistics and data analytics using modern computing systems, with attention to fundamentals and practical aspects. Students will explore sources of data, data formats and transformation, the use of spreadsheets and databases, statistical analysis, pattern recognition, data mining, big data, and methods for data presentation. |
| Learning Goals: |
• Students will understand how to describe distributions of data and how to explain relationships between variables. This objective will be measured by Quizzes 1 and 2, and Examination 1.• Students will understand how to use probability distributions in data analysis. This objective will be measured by Quiz 3 and Examination 1.• Students will understand how to apply inferential statistics to various data sets. This objective will be measured by Quizzes 4, 5, and 6, and Examination 1.• Students will apply the principles of regression theory to various sets of data. This objective will be measured by Quizzes 7 and 8, Examination 2, and the Final Examination. • Students will understand the principles behind data mining and its relationship to structural equation modeling (SEM). This objective will be measured by Quizzes 9 and 10, and the Final Examination. *The learning goals displayed here are those for one section of this course as offered in a recent semester, and are provided for the purpose of information only. The exact learning goals for each course section in a specific semester will be stated on the syllabus distributed at the start of the semester, and may differ in wording and emphasis from those shown here. |
| Prerequisites: |
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