TEST 1

AA 5300: Advanced Analytics

Course Overview

Textbooks

Welcome Video

Syllabus and Calendar

Instructor details

Policies and Resources

Modules of the Course

Instructor Information:

Name: Instructor's name here Email Address: email address We are excited to be your instructors for AA5300! We here to help you master the course material, so please reach out for help if you feel you are not understanding the material. In our experience with this course, the students who do well are those who ask questions when they don't understand something, while the students who do poorly are those who suffer in silence. So, please, ask questions! Brief bio here

SAMPLE

Syllabus and Calendar for the Course

• Syllabus

• Calendar

Modules of the Course

1. Regression and Classification 2. Resampling methods 3. Non-linear regression 4. Tree-based modelings 5. Unsupervised Learning Approaches 6. Support Vector Machine (SVM) 7. Conclusion and reflection

See in detail each module of the course

Required Texts:

There are two versions of the book: a Python one and an R one. The concepts covered are the same; they differ in the code examples, using Python and R, respectively. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2022). An Introduction to Statistical Learning with Applications in R. Second Edition. Springer. James, G., Witten, D., Hastie, T., Tibshirani, R., and Taylor, J. (2023). An Introduction to Statistical Learning with Applications in Python. First Edition. Springer. Information related to books, included free PDF versions for download, is available here:

Course Overview

This course covers several commonly-used advanced analytical methods involving statistical and machine learning. Applications of these methods on datasets drawn from several fields will be emphasized, alongside a coverage of visualizations of data and results. Students will also learn how to automate tasks in various phases of statistical analyses and in creating useful visualizations of data and results. When you enroll in a course, you are expected to participate regularly. At any time in the term, should a problem arise that may affect your ability to participate, for example with financial aid, textbooks, or a personal emergency, you must contact the instructor immediately. Such problems do not excuse you from actively engaging in the course, and the failure to participate in two or more weeks of the course may result in an F as your final grade.

Course overview

This course covers several commonly-used advanced analytical methods involving statistical and machine learning. Applications of these methods on datasets drawn from several fields will be emphasized, alongside a coverage of visualizations of data and results. Students will also learn how to automate tasks in various phases of statistical analyses and in creating useful visualizations of data and results. When you enroll in a course, you are expected to participate regularly. At any time in the term, should a problem arise that may affect your ability to participate, for example with financial aid, textbooks, or a personal emergency, you must contact the instructor immediately. Such problems do not excuse you from actively engaging in the course, and the failure to participate in two or more weeks of the course may result in an F as your final grade.