Welcome to STAT250! This seminar is limited enrollment, and will only be offered once; please fill out this survey by Friday, September 6, if you are interested in joining! We will accept late submissions on a case by case basis.


Learn data science by teaching it!

How do you create a new, exciting data science course for the Harvard GenEd program?

This is the question that we, the instructors, faced over a year ago, and we have been working on it ever since. In this seminar we will take you along on our journey to develop the Elements of Data Science course (DS10) that will be offered in spring 2020, intended for the new Quantitative Reasoning with Data requirement that all Harvard College students will have to satisfy. You will learn diverse facets of the data science process, make connections between different disciplines and data science, and be exposed to modern course pedagogy, including the Harvard Business School case method. 

Why should I take this course?

This seminar is your unique opportunity to get deep insights into data science by learning how to teach it. If you ever wondered about the intricacies of data science, how to teach a technical subject to students from widely varying backgrounds, or how to apply modern teaching principles in large college classes, then you should consider taking this seminar. 

Are there any prerequisites?

No. All Harvard students are welcome to apply to take this course, and we would like to have students of very diverse backgrounds. A background in computer science or statistics will help in some ways, but there are many ways in which students can contribute to the course. If you are not familiar with some of the technical aspects of data science, you will be expected to learn them by following recommended free online tutorials and resources.

How can I succeed in this course?

Your success in this class will be based on how well you manage your time, how engaged you are with the class activities, and how well you do on the assignments and final project. More specifically:

  • Show up to class on time. Regular attendance is mandatory. Please contact us if you cannot attend due to a medical emergency and send us the proper documentation.
  • Do the reading. Some classes will involve readings. Since the activities during class will be based on these readings, it is vitally important that you do them before class.
  • Submit all assignments on time. You will complete weekly assignments on your own or in small groups. Because we do not want you to fall behind we expect you to submit them on time.
  • Be a good team member. For the final course project, you will develop a data science case study in a team of 2-3 students. It is vital that you are a good and responsible team member who adheres to the project milestones. We will use peer-evaluation where team members can flag issues that we will address with the team. 
  • Prepare and present a compelling case study. The main focus of the course is the data science case study that you will develop with your team. You will present your case study proposal and the almost-final case in class to get feedback from the instructors, fellow classmates, and any invited guests.  

How will I be evaluated?

This course can be taken for a letter grade only, there is no pass/fail option. The course grade comprises:

  • Citizenship and class attendance: 10%
  • Readings, exemplified by your ability to participate in class discussions: 10%
  • Team participation, based on peer evaluations: 20%
  • Homework, including project milestones (mix of individual and team grades): 20%
  • Final case study project (team grade): 40%

What should I be able to know and do by the end of this course?

  • You will learn about data science in different domains by teaching it.
  • You will learn and practice new pedagogical approaches and the case method.
  • You will learn to communicate and present data science cases to people from different disciplines
  • You will develop and improve data science and software skills, starting at any level, with a focus on acquiring data, performing statistical analysis, and presenting results verbally, numerically, and visually.
  • You will acquire skills using Google Colab Notebooks in Python with statistical modules that include NumPy, Scikit-Learn, and Pandas.

How can I enroll in this course?

To assure that everybody gets a chance to actively participate in class we are limiting enrollment to 20 students. Since the semester start falls on a holiday (Labor Day) the first class will be on Monday, September 9, the same day your study cards are due. It is therefore important that you indicate your interest in enrolling by filling out this survey by September 6! We will accept late submissions on a case by case basis.

Can only graduate students enroll?

Undergraduates are also welcome to apply to join the course. We hope to have a mix of graduate students and undergraduates.

What textbook do I need?

You do not need to purchase any textbook for this seminar. Instead, you will be given PDFs or links to free online materials throughout the semester.

What are the course assignments?

Take a look at the course schedule to see the due dates for homework and project milestones. These will be described in more detail in the descriptions for each assignment that we will upload on Canvas. \

What is Elements of Data Science (DS10)?

Elements of Data Science (DS10) is the new undergraduate data science course that we are developing for spring 2020. Your contributions in STAT250 will have a big influence on the final content of DS10.

What does a typical class look like?

Classes will be a mix of activities, discussions, project presentations, and occasional guest lectures. We will keep the ‘lecturing to you’ to a minimum to keep the class engaging, informative, and fun.

Where can I get extra help?

Your first line of defense for any questions should be the course forum on Piazza. The TF will also be available for office hours.


Course Policies

Citizenship has to do with attendance as well as how you treat others. Most wars, fights, retaliations & insults happen when people feel their dignity has been assaulted. So please, respect each individual’s opinions and beliefs–even if you disagree.

Avoid using cell phones in class, which can prevent you or others from learning. In cases of emergency, please take your phone outside.

Attendance is mandatory. Sign in every class at the beginning. If you come in late, sign in after class–you don’t want to be marked absent by mistake.

The intent of this graduate seminar course is to develop course materials for future courses. If you enroll in STAT 250 you agree that all of the work you produce in this seminar course may be used as material for a future course. Please let us know if you have any issues or concerns with this.

We get there are emergencies or conflicting professional obligations. Do your best to tell us in advance (at least a day before if possible). An authorized note (from a doctor, employer, conference registration, etc.) may excuse an absence or lateness, but only if submitted in a timely fashion. Telling us you will get a note and forgetting to submit one will not excuse you.

Submit all assignments and your final project milestones on Canvas. Late submissions will not be accepted unless you had an emergency or conflicting professional obligations.

We expect you to adhere to the Harvard Honor Code or the GSAS Academic Integrity policy at all times. Failure to adhere to the honor code and the GSAS policies may result in serious penalties, up to and including automatic failure in the course and reference to the ad board.

If you have a documented disability (physical or cognitive) that may impair your ability to complete assignments or otherwise participate in the course and satisfy course criteria, please meet with us at your earliest convenience to identify, discuss, and document any feasible instructional modifications or accommodations. You should also contact the Accessible Education Office to request an official letter outlining authorized accommodations.