Printable Version

Fall 2017
6372 Analytic Methods - CHANDLER- 25885

Professor(s): Seth Chandler (FACULTY)

Credits: 3

Course Areas: Business and Commercial Law 
Law And Society/ Interdisciplinary

Time: 1:00p-2:30p  TTH  Location: 215  TUII

Course Outline: This course satisfies the practice skills requirement. In a recent Harvard survey, lawyers at major firms ranked Analytic Methods as one of the most valuable skills for new attorneys. This course meets that demand by developing students’ skills in analytic methods likely to be used in the practice of law. Although I can not prove it, I suspect that the concepts and skills learned in this class will have great salience as technology and the law evolves over the next decade. The substantive areas emphasized will be (1) database formats and concepts and operations such as grouping, aggregate statistics, and joining; (2) data visualization, both two dimensional, three dimensional and annotated; (3) statistics, including the concept of a distribution, statistical testing, and cross examination of statistical experts; (4) machine learning with an emphasis on critical concepts such as bias, variance, overfitting and adversarial examples. The course will use The Wolfram Language (Mathematica) as a unified framework in which conduct analyses in diverse fields. (Students may have encountered the Wolfram Language either at the WolframAlpha.com website or as a behind-the-scenes component of technologies such as Siri). This is a hands-on course in which the instructor will dedicate the classroom significantly to live experimentation designed to foster the skills of the student in conducting not only basic data analysis by themselves but also to help a new lawyer to work with (or cross-examine) experts in these fields who may appear in transactions involving tax, corporate matters, discrimination lawsuits, family matters and many other arenas.

Course Syllabus: Syllabus

Grading Rubric

Course Notes:   Quota = 12.

PREREQUISITES NOTE: Students who are mathphobic or who have not performed well in math classes should not enroll in this course without discussing the matter with the instructor. Some computer programming experience is also useful background, with knowledge of R, Java, Python, Lisp, XML (not really a language), Maple, Mathematica or Matlab particularly helpful. No knowledge of any of these languages is assumed, however, and sometimes unlearning is required.

Prerequisites:  

First Day Assignments:

Final Exam Schedule: 12/7 1 - 4PM      

This course will have:
Exam:
Paper:

Satisfies Skills Course Requirement: Yes
Satisfies Senior Upper Level Writing Requirement: No

Experiential Course Type:

Bar Course: No

Course Materials (4/13/2017 9:39:04 AM)

No book required for this course