Course Number 7475
This is the less-intensive, 1.5-credit version of CS50 for MBAs, meeting in Q3 on X days, 8:30am–9:50am, in Aldrich 208. Assignments are largely reading- and writing-based. For a more-intensive, 3.0-credit version of CS50 for MBAs, with additional hands-on programming lessons, see Course Number 7473.
- Educational Objectives
- This course is a variant of Harvard College’s introduction to computer science, CS50, designed especially for MBA 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, this course empowers students to make technological decisions even if not technologists themselves. Topics include artificial intelligence, cloud computing, networking, privacy, scalability, security, and more, with a particular emphasis on web and mobile technologies. 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.
- Career Focus
- This course is designed for students who expect to be analysts, founders, product managers, and problem-solvers more generally.
Course Number 7473
This is the more-intensive, 3.0-credit version of CS50 for MBAs, meeting in Q3 on X days, 8:30am–9:50am, in Aldrich 208, as well for twice-weekly, 2-hour labs most weeks (2:20 – 4:20 PM Mondays and Tuesdays) in Aldrich 209. This version includes additional, hands-on programming lessons in Python and SQL. For a less-intensive, 1.5-credit version of CS50 for MBAs, without the additional hands-on programming lessons, see Course Number 7475.
- Educational Objectives
- This course is a variant of Harvard College’s introduction to computer science, CS50, designed especially for MBA students who’d like to be able to read and write code without becoming software developers themselves. Ultimately, this course empowers students to make technological decisions even if not technologists themselves. Topics include artificial intelligence, cloud computing, networking, privacy, scalability, security, and more, with a particular emphasis on web and mobile technologies. 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. This course also prepares students to be conversant in today’s and tomorrow’s technologies and self-sufficient writing programs of modest size. The course introduces Python, a popular language for general-purpose programming, as well as to SQL, a specialized language for navigating large data sets. Topics include: functions, which are instructions that perform tasks; return values, which are results that functions provide; conditionals, via which programs can decide whether or not to perform some action; loops, via which programs can take action again and again; variables, via which programs can remember information; objects and methods, via which code can be modularized; exceptions, via which errors can be handled; file I/O, via which data can be stored and loaded; libraries, via which code can be re-used; and more.
- Career Focus
- This course is designed for students who expect to be analysts, founders, product managers, and problem-solvers more generally, especially if planning to to work closely with technical teams.
Students in Course Number 7475 are expected to attend all lectures and submit all assignments.
Students in Course Number 7473 are expected to attend all lectures, submit all assignments, and attend and submit all labs.
Absences should be reported via the Student Absence Notification tool.
For students in Course Number 7475, final grades will be based on quality of engagement (10%) and assignments (90%), with a final assignment weighted more heavily than other assignments.
For students in Course Number 7473, final grades will be based on quality of engagement (10%), assignments (70%), and labs (20%), with a final assignment weighted more heavily than other assignments.
A schedule of lectures, subject to change, appears below.
You are expected to arrive at class (with a laptop) having completed all prior assignments, prepared to discuss all prior classes’ material.
Lectures take place in Klarman Hall from 8:30am until 9:50am.
- ASCII, binary
- abstraction, algorithms, pseudocode
- functions, variables, conditions, loops
- correctness, efficiency
- searching, sorting
- arguments, return values
- strings, integers, floats
- overflow, imprecision
- modules, packages
- arrays, linked lists, trees, hash tables
- lists, dictionaries
with Brian Yu of Automattic
- decision-making, decision trees, minimax
- machine learning, reinforcement learning, genetic algorithms
- DHCP, DNS, HTTP, HTTPS, IP, TCP
- hosts, registrars
- CSS, HTML
- primary keys, foreign keys
- constraints, indexes
- caching, load balancing, scaling
- containers, virtual machines
- IAAS, PAAS, SAAS
- encryption, hashing
- blockchain, cryptocurrency
with Patrick Schmid of Sharpen
- frameworks, interfaces, languages
Led by the teaching fellows, labs are opportunities for students in Course Number 7473 to dive into some of the course’s material hands-on. Students in Course Number 7473 are expected to arrive at each lab (with a laptop) having attended the most recent lectures.
Assignments are assigned after each class, all of which must be submitted by 8:30am on the date due. Assignments are opportunities to introduce or reinforce material via exercises, readings, questions, and/or videos.
Many questions on the assignments are out of 3 points. When you receive your feedback on these assignments, these scores translate as follows:
- 3 is reserved for exceptional answers that demonstrate a full understanding of the material in a manner that exceeds the course’s expectations, which may sometimes involve external research. Please note that this does not necessarily mean a long answer; indeed, an overly long answer that introduces irrelevant information is not likely to receive a 3. Rather, a 3 is awarded to a thorough and well thought-out and prepared answer that focuses on the subject of the question being presented. Only a few 3s at most will be awarded per question.
- 2 is awarded for a good to great answer that demonstrates understanding of the material in a manner consistent with the course’s expectations. Most correct answers will be graded a 2, and a score of 2 should be considered your benchmark for success.
- 1 is awarded for a poor to fair answer that either demonstrates partial understanding of the material or otherwise contains an error that suggests a misunderstanding of a key detail.
- 0 is given to answers that are incorrect, exceedingly brief, or blank.
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 violations of the MBA Program Honor Code.
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.
- Communicating with classmates about assignments 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 their code at office hours, elsewhere, or even online, as by viewing or running their 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 assignments.
- Whiteboarding solutions to assignments with others 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.
- Asking a classmate to see their 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 assignments online or elsewhere.
- Splitting an assignment’s workload with another individual and combining your work.
- 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 and basing your own solution on it.