Pretty useful summary which is something we try to teach our students in our Legal Analytics Course (which could really be called Machine Learning for Lawyers). BTW – For those of you who emailed us, we promise to fill out the balance of the set of free, online Legal Analytics course materials in the coming months.
Here is an introductory slide deck from “Legal Analytics” which is a course that Mike Bommarito and I are teaching this semester. Relevant legal applications include predictive coding in e-discovery (i.e. classification), early case assessment and overall case prediction, pricing and staff forecasting, prediction of judicial behavior, etc.
As I have written in my recent article in Emory Law Journal – we are moving into an era of data driven law practice. This course is a direct response to demands from relevant industry stakeholders. For a large number of prediction tasks … humans + machines > humans or machines working alone.
We believe this is the first ever Machine Learning Course offered to law students and it our goal to help develop the first wave of human capital trained to thrive as this this new data driven era takes hold. Richard Susskind likes to highlight this famous quote from Wayne Gretzky … “A good hockey player plays where the puck is. A great hockey player plays where the puck is going to be.”
While its performance is sometimes problematic for some extremely large data problems, R (with R studio frontend) is the data science language du jour for many small to medium data problems. Among other things, R is great because it is open source, hyper customizable with thousands of packages available to be loaded for a specific problem.
While Python and SQL are also important parts of the overall data science toolkit, we use R as our preferred language in both Quantitative Methods for Lawyers (3 credits) as well as in our Legal Analytics course (2 credits). We have found that students who are diligent can make amazing strides in a relatively short amount of time. For example, see this final project by Pat Ellis from last year’s course.
Here are some introductory resources that we have developed to get folks started: Loading R and R Studio
R Boot Camp – Part 1 – Loading Datasets and Basic Data Exploration
Data Cleaning and Additional Resources
R Boot Camp – Part 2 – Statistical Tests Using R
Basic Data Visualization in R
Scatter Plots, Covariance, Correlation Using R
Intro to Regression Analysis Using R
Over the balance of the 2014-2015 academic year, Mike and I will be introducing a variety of new things to the quantitative sequence including dplyR, etc. … more to come …