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Andy Ninh – Augmented Reality and On-Demand Legal Services (via ReInventLawChannel.com )

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In the months to come, I will be featuring some of my students who are participating in my law, technology and/or entrepreneurship courses here at MSU Law (access the course list here) — Many of my students are doing interesting and exciting things and so I thought I would take some time to highlight them! For more information about these students or my courses – please feel free to contact me daniel.martin.katz@gmail.com

Andy Ninh is a 3L at Michigan State University College of Law. “He’s a geek, future attorney & entrepreneur, Google Glass explorer, nutrition and exercise junkie, martial artist, and tech enthusiast.”

Check Out His Personal Website Here (See Below).

andy ninh

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Supercharging Patent Lawyers With AI (via IEEE Spectrum)

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In my recent article, Quantitative Legal Prediction – or – How I Learned to Stop Worrying and Start Preparing for the Data Driven Future of the Legal Services Industry 62 Emory Law Journal 909 (2013), I discuss how companies like Lex Machina are creating a more efficient and data driven legal industry.  Next semester at MSU Law, Michael Bommarito and I will co-teach a course called “Legal Analytics.”  This is a follow on the introductory course that I teach called “Quantitative Methods for Lawyers.”  In Legal Analytics, students will be exposed to cutting edge predictive analytics approaches such as machine learning, natural language processing, network science, etc.  Students will apply their skills on real datasets that are available from published papers or from some our industry partners.  Thus, the course will mix theory with practical applications useful for the practice of law as we move forward into the 21st Century.

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R Boot Camp – Part 2 in Quantitative Methods for Lawyers (Professor Daniel Martin Katz)

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Today was Day 2 of our R Boot Camp in Quantitative Methods for Lawyers.

In total, there will be three set of slides in this multi-day bootcamp designed to introduce students to the logic of R, the basic roadblocks such as loading data and cleaning data, loading various R packages, running basic commands, shifting out of default command settings, plotting data, conducting statistical tests, etc. Later in the course we will use R for regression analysis, etc.

For anyone who might be interested, the Full Course Page including all slidedecks is located here. For help on the installation of R and the RStudio IDE please check out my Loading R/RStudio Bonus Module.