General Course Information
1.1 Course details
|Course code:||LLAW6285 / JDOC6285|
|Course name:||Legal Data and Science [previously named: Computer Programming, Data Mining, and the Law An Applied Introduction]|
|Programme offered under:||LLM Programme / JD Programme|
|Prerequisites / Co-requisites:||No|
|Credit point value:||9 credit / 6 credits|
1.2 Course description
This course will introduce students to using data analytics and computational methods in legal studies (or, broadly speaking, empirical studies of law). The course will cover a range of empirical methods that are widely used in sciences and social sciences, including statistical tests, regression analysis, machine learning, and causal inference, and it will use real-world examples to introduce how these methods can be applied into the study and the practice of law. The course will guide students in a hands-on way, focusing on substantive projects that are relevant to legal research and practice.
Designed to serve as an introduction to the field, students can expect to leave the class with an experience of conducting empirical legal study, that is, finding a research question, designing an empirical research, collecting and analyzing data, and presenting the results. Students who aspire to develop a career in law and new technologies, or who plan to pursue a graduate degree (e.g., Ph.D. or JSD), are encouraged to take the course.
Computer programming or statistical analysis experience would help, but is not required. Students without such experience can take LLAW6280 / JDOC6280 Introduction to Artificial Intelligence and Law.
Topics covered will include:
Basic statistical tests
Natural Language Processing
The application of these methods in a range of legal areas, including contract law, property, intellectual property, criminal law, corporate and financial regulation, judicial behavior, and law and development.
1.3 Course teachers
|Course convenor||Ryan Whalenfirstname.lastname@example.org||CCT 815||By email|
2.1 Course Learning Outcomes (CLOs) for this course
CLO 1 Understand the principles and basic methods of data analysis in law.
CLO 2 Achieve a critical appreciation for empirical methods and data analytical thinking.
CLO 3 Gain experience collecting and analyzing data and conducting empirical legal research.
2.2 LLM and JD Programme Learning Outcomes (PLOs)
Please refer to the following link:
2.3 Programme Learning Outcomes to be achieved in this course
|PLO A||PLO B||PLO C||PLO D||PLO E||PLO F|
3.1 Assessment Summary
|Assessment task||Due date||Weighting||Feedback method*||Course learning outcomes|
|To be confirmed||TBA||100%||1, 2, 3|
|*Feedback method (to be determined by course teacher)|
|1||A general course report to be disseminated through Moodle|
|2||Individual feedback to be disseminated by email / through Moodle|
|3||Individual review meeting upon appointment|
|4||Group review meeting|
|5||In-class verbal feedback|
3.2 Assessment Detail
To be advised by course convenor(s).
3.3 Grading Criteria
Please refer to the following link: https://www.law.hku.hk/_files/law_programme_grade_descriptors.pdf
4.1 Learning Activity Plan
|Seminar:||3 hours / week for 12 teaching weeks|
|Private study time:||9.5 hours / week for 12 teaching weeks|
Remarks: the normative student study load per credit unit is 25 ± 5 hours (ie. 150 ± 30 hours for a 6-credit course), which includes all learning activities and experiences within and outside of classroom, and any assessment task and examinations and associated preparations.
4.2 Details of Learning Activities
To be advised by course convenor(s).
|Reading materials:||Reading materials are posted on Moodle|
|Core reading list:||TBA|
|Recommended reading list:||TBA|
Please refer to the following link: http://www.law.hku.hk/course/learning-resources/