Syllabus

Instructors

Doug Lloyd
lloyd@cs50.harvard.edu

David J. Malan
malan@harvard.edu

Educational Objectives

This course is a variant of Harvard College’s introduction to computer science, CS50, designed especially for law students. Whereas CS50 itself takes a bottom-up approach, emphasizing mastery of low-level concepts and implementation details thereof, this course takes a top-down approach, emphasizing mastery of high-level concepts and design decisions related thereto. Ultimately, it equips students with a deeper understanding of the legal implications of technological decisions made by clients.

Through a mix of technical instruction and discussion of case studies, this course empowers students to be informed contributors to technology-driven conversations. In addition, it prepares students to formulate technology-informed legal arguments and opinions. Along the way, it equips students with hands-on experience with Python and SQL, languages via which they can mine data for answers themselves.

Topics include algorithms, cloud computing, databases, networking, privacy, programming, scalability, security, and more, with a particular emphasis on understanding how the work developers do and the technological solutions they employ may impact clients. Students emerge from this course with first-hand appreciation of how it all works and all the more confident in the factors that should guide their decision-making.

Audience

This course is designed for future attorneys who expect to work closely with and advise decision-makers on legal matters that impact or intersect with technology.

Expectations

You are expected to attend all classes, read all assigned cases and readings, submit all assignments, and submit all projects.

Grades

Final grades will be based on quality of participation (10%), assignments (60%), and projects (30%).

Lectures

A schedule of lectures appears below, subject to change.

You are expected to arrive at lecture (with a laptop) having completed all prior readings, assignments, and projects, prepared to discuss all prior classes’ material.

Lectures meet in Wasserstein Center 1010 from 9am until 12:15pm.

Computational Thinking on Mon 1/7

  • ASCII, binary, Unicode
  • abstraction, algorithms, pseudocode
  • overflow, imprecision
  • Scratch

Programming Languages on Tue 1/8

  • Python
  • functions, variables, conditions, loops
  • arguments, return values
  • types

Algorithms, Data Structures on Wed 1/9

  • correctness, efficiency
  • searching, sorting
  • arrays, linked lists, trees, hash tables

Cryptography on Thu 1/10

  • ciphers and hashes
  • modern cryptographic standards
  • public and private key encryption
  • blockchain and cryptocurrency

Cybersecurity on Fri 1/11

  • memory, hard drives
  • data persistence
  • secure transfer protocols
  • protecting client data
  • compliance protocols

Internet Technologies, Cloud Computing on Mon 1/14

  • IP, DHCP, DNS, TCP, HTTP, HTTPS
  • vertical scaling, horizontal scaling
  • load balancing, caching
  • containers, virtual machines
  • IAAS, PAAS, SAAS

Web Programming on Tue 1/15

  • registrars, hosts
  • HTML, CSS, JavaScript
  • APIs

Database Design on Wed 1/16

  • CRUD
  • indexes, normalization
  • SQL, NoSQL

Cybersecurity, continued on Thu 1/17

  • denial of service attacks
  • cross-site scripting
  • SQL injection
  • man-in-the-middle and phishing
  • database and email hacking

Challenges at the Intersection of Law and Technology on Tue 1/22

  • trust models
  • open-source software and licensing
  • software copyrights and patents; piracy
  • emergent technologies (3-D printing, augmented and virtual reality)

Challenges at the Intersection of Law and Technology, continued on Wed 1/23

  • digital privacy and tracking
  • artificial intelligence and machine learning
  • GDPR and the “right to be forgotten”
  • “net neutrality”

Seminars

Led by the teaching staff, seminars are opportunities to dive into topics beyond the scope of the course’s classes.

Homework

Homework is ordinarily assigned daily in the form of assignments and projects, all of which must be submitted by 9:00am on the day due. Assignments are shorter, opportunities to introduce or reinforce material via exercises, readings, questions, and/or videos. Projects are longer, hands-on opportunities to write code.

Office Hours

Office hours are opportunities for guidance and feedback from the staff on assignments and projects as well as for discussion of the course’s material more generally.

Academic Honesty

This course’s philosophy on academic honesty is best stated as “be reasonable.” The course recognizes that interactions with classmates and others can facilitate mastery of the course’s material. However, there remains a line between enlisting the help of another and submitting the work of another. This policy characterizes both sides of that line.

The essence of all work that you submit to this course must be your own. Collaboration on assignments and projects is not permitted except to the extent that you may ask classmates and others for help so long as that help does not reduce to another doing your work for you. Generally speaking, when asking for help, you may show your work to classmates and others, but you may not view theirs, so long as you and they respect this policy’s other constraints.

Below are rules of thumb that (inexhaustively) characterize acts that the course considers reasonable and not reasonable. If in doubt as to whether some act is reasonable, do not commit it until you solicit and receive approval in writing from the course’s instructor. Acts considered not reasonable by the course are handled harshly as academic dishonesty.

If you commit some act that is not reasonable but bring it to the attention of the course’s instructor within 72 hours, the course may impose local sanctions that may include a failing grade for work submitted, but the course will not escalate the matter further except in cases of repeated acts.

Reasonable

  • Communicating with classmates about assignments and projects in English (or some other spoken language).
  • Discussing the course’s material with others in order to understand it better.
  • Helping a classmate identify a bug in his or her code at Office Hours, elsewhere, or even online, as by viewing or running his or her code, even on your own computer.
  • Incorporating snippets of code that you find online or elsewhere into your own code, provided that those snippets are not themselves solutions to assigned problems and that you cite the snippets’ origins.
  • Sending or showing an answer or code that you’ve written to someone, possibly a classmate, so that he or she might help you identify and fix a mistake.
  • Sharing snippets of your own answers or code online so that others might help you identify and fix a mistake.
  • Turning to the web or elsewhere for instruction beyond the course’s own, for references, and for solutions to technical difficulties, but not for outright solutions to projects.
  • Whiteboarding solutions to projects with others outside of your team using diagrams or pseudocode but not actual code.
  • Working with (and even paying) a tutor to help you with the course, provided the tutor does not do your work for you.

Not Reasonable

  • Asking a classmate to see his or her answer or code before (re-)submitting your own.
  • Failing to cite (as with comments) the origins of code or techniques that you discover outside of the course’s own lessons and integrate into your own work, even while respecting this policy’s other constraints.
  • Giving or showing to a classmate an answer or code when it is he or she, and not you, who is struggling with their own.
  • Paying or offering to pay an individual for work that you may submit as (part of) your own.
  • Providing or making available answers or code to individuals who might take this course in the future.
  • Searching for or soliciting outright solutions to projects online or elsewhere.
  • Splitting a project’s workload with another individual outside of your team and combining your work.
  • Splitting a project’s workload within your team inequitably but claiming an equitable split.
  • Submitting (after possibly modifying) another’s words without attribution for some assignment.
  • Submitting (after possibly modifying) the work of another individual beyond allowed snippets.
  • Submitting the same or similar work to this course that you have submitted or will submit to another.
  • Submitting work to this course that you intend to use outside of the course (e.g., for a job) without prior approval from the course’s instructor.
  • Viewing another’s solution to an assignment or project and basing your own solution on it.