Everyone talks about big data. Few know how to deal with it. This course will teach you how to work with data of all sizes. How much money is donated to political campaigns? What characteristics are associated with voting Republican? Has the connection between income and ideology changed over time? We need data, often big data, to answer these questions.

This course, an introduction to data science, will teach you how to think with data, how to gather information from a variety of sources, how to import that information into a project, how to tidy and transform the variables and observations, how to visualize, how to model relationships, and how to communicate your findings. Students will complete a final project, the first entry in their professional portfolio. Our main focus is data associated with political science, but we will also use examples from education, economics, public health, sociology, sports, finance, climate and any other social science topic which students find interesting.

We use the R programming language, RStudio, Git and GitHub.

Prerequisites: None. You must have a laptop with R, RStudio and Git installed.

Logistics: There are two sessions: 7:40 AM to 8:55 AM and 3:00 PM to 4:15 PM, both on T/TH. I will teach both sessions. You must attend one (either morning or afternoon) consistently all semester, unless you have spoken to course staff about an exemption.

Ulysses and the Sirens, 1891, by John William Waterhouse. Homer’s Odyssey recounts the decade-long journey home of Odysseus (known as Ulysses in Latin) after the Trojan War. Although Ulysses’s ultimate goal is his kingdom of Ithaca, he does not shy away from adventure along the way. The Sirens use their enchanting voices to lure unwary sailors to their deaths. Ulysses wanted to hear their songs. He instructed his men to fill their ears with beeswax and to tie him to the mast.


The central metaphor for this class is Ulysses and the Sirens. You are Ulysses. Ithaca is where you hope to arrive after graduation. The Sirens are the many distractions of the modern world. I am the rope.


No course at Harvard does a better job of increasing your odds of getting the future — the internship, the job, the graduate school, the career — that you want. The best way to decide whether or not this class is for you is to look at our final projects. If you want to learn how to build something like that, take the class. See “Kill The Math and Let the Introductory Course Be Born” for more details.


Preceptor David Kane; ; CGIS South 310; 646-644-3626. Office hours via Zoom on Wednesdays (sign up at Calendy) or by appointment. Please address me as “Preceptor,” not “David,” nor “Preceptor Kane,” nor “Brah,” nor “Professor Kane,” nor “Mr. Kane,” nor, worst of all, “Dr. Kane.” I respond to e-mail within 24 hours. If I don’t, e-mail me again.

Teaching Fellows: Beau Meche (, Zoom) and Jessica Edwards (, Zoom).

Course Philosophy

No Lectures: The worst method for transmitting information from my head to yours is for me to lecture you. There are no lectures. We work on problems together during class. You learn soccer with the ball at your feet. You learn about data with your hands on the keyboard.

Bayesian: The philosophy of this class is unapologetically Bayesian.

R Everyday: Learning a new programming language is like learning a new human language: You will practice (almost) every day.

Professionalism: We use professional tools. Your workflow will be very similar to the workflow involved in paid employment. Your problem sets and final project will be public, the better to impress others with your abilities. High quality work will be shared with your classmates. We will learn the “full cycle” of how to draw inferences from data and communicate those inferences to others. We will network.

Cold Calling: I call on students during class. This keeps every student involved, makes for a more lively discussion and helps to prepare students for the real world, in which you can’t hide in the back row. Want to be left alone? Don’t take this course.

Recitations: We do not have normal sections. Instead, you will spend 60 minutes each week meeting with your assigned TF in a small group in the first half of the semester. Recall what I just said about (not) being left alone in this course. In the second half of the semester, you will have one-on-one meetings with your TF for 30 minutes each week, focused on your final project.

Millism: Political disputes are not the focus of this class but, when such topics arise, I will insist that we follow John Stuart Mills’ advice: “He who knows only his own side of the case, knows little of that. His reasons may be good, and no one may have been able to refute them. But if he is equally unable to refute the reasons on the opposite side; if he does not so much as know what they are, he has no ground for preferring either opinion.”

Engagement: We require you to be engaged with the outside world. For example, you are required to email alumni and seek to meet with them about their careers. Our final project presentations are public, and you must invite some family and friends to attend.

Teaching to Learn: My main goal is not to teach you how do X. That is easy! More importantly, in a few months, I won’t be around to teach you Y. My goal is to teach you how to teach yourself, how to figure out X and Y and Z on your own. That is harder! Much of the pedagogy of the course — especially my insistence that you work on topics not covered in lecture — is driven by this goal. You will find it frustrating.

Course Policies

Book: The text for the class is Preceptor’s Primer for Bayesian Data Science. We call it The Primer for short. The book is still a draft and contains many mistakes. Please help us by pointing them out!

Workload: The course should take about 8 hours per week, outside of meetings, exams and the final project. This is an expected average across the class as a whole. It is not a maximum. Some students will end up spending much less time. Others will spend much more.

Late Days: Assignments (tutorials, problem sets and final project milestones) are always due at 11:59 PM, unless specified otherwise. An assignment is a day late if it is turned in any time after it was due (even 5 minutes after) but within 24 hours. After that, it is two days late, and so on. You have 5 late days in total. Late days may be used for any assignment, except the four exams and the final project. You should save your late days. If you use them early in the semester for no particularly good reason and then, later in the semester, have an actual emergency, we will not be sympathetic. We will not give you extra late days in such a situation. (That isn’t fair to your classmates, and we are all about fairness.) We will just, mentally, move the late days you wasted so that they cover your actually emergency. You will now be penalized for being late earlier in the semester, when you did not have a good reason for tardiness. You may only use one late day on a given assignment. Hand it in after more than 24 hours and you get a zero on that assignment. But you still must hand it in! Everything must be completed. Late days accrue until you do. Each day late (beyond the five allowed) results in -1 point to your final score. This decrement is a point not percentage penalty. In other words, each additional late day used outside of the allotted five will drop your final class average by one point out of 100.

Submissions: All tutorials, problem sets, milestones and exams are turned in via Canvas. Late days are assigned on the basis of the official Canvas submission time.

Missing Class: You expect me to be present for lecture. I expect the same of you. There is nothing more embarrassing, for both us, than for me to call your name and have you not be there to answer. But, at the same time, conflicts arise. It is never a problem to miss class if, for example, you are out of town or have a health issue. Just email Preceptor and your assigned TF explaining the situation. Please do so on the day you will be missing class. We don’t need advanced warning.

Major Emergencies: We are not monsters. If you are hit with a major emergency — the sort of thing that necessitates the involvement of your Resident Dean — we will be sympathetic. Speak with your TF.

No Cost: Every reading/tool we use is free. You don’t have to spend any money on this class. Some activities, like Shinyapps and GitHub, have paid options which provide more services, but you never have to use them. Don’t give anyone your credit card number.

Role of Teaching Fellows: The TFs run their Recitations, approve all grades, deal with emergencies and so on. Go to them first with any problems. You will be assigned to work closely with a specific TF — your “assigned” TF — but you may ask other TFs for help as well.

Exceptions: There may be a reason why you can’t adhere to class policies. Severe social anxiety may make being cold-called problematic. A learning disability may make take-home tests unfair. Whatever the situation, please talk with me. I am sure we can work out something! I will do whatever it takes to allow every Harvard student to thrive in this class.

Slack: All general questions — those not of a personal nature — should be posted to Slack so that all students can benefit from both the question and the answer(s). Please post your question in a sensible channel.

Plagiarism: If you plagiarize, you will fail the course. See the Harvard College Handbook for Students for details.

Working with Others: Students are free (and encouraged) to discuss problem sets and their final projects with one another. However, you must hand in your own unique code and written work in all cases. Any copy/paste of another’s work is plagiarism. In other words, you can work with your friend, sitting side-by-side and going through the problem set question-by-question, but you must each type your own code. Your answers may be similar (obviously) but they must not be identical, or even identical’ish.

Git and GitHub: Analyzing data without using source control is like writing an essay without using a word processor — possible but not professional. We will do all our work using Git/GitHub.

Readings: Assignments in a given week cover (approximately) the material that we will use that week. I will not hesitate to cold-call students with questions about the readings. Do them. Note that the readings must be done before Tuesday class or before your Recitation, whichever is earlier.

Optional Activities: The syllabus includes background readings, videos and materials which students may find interesting. You do not have to do them.

Computer Emergencies: We are not sympathetic about computer emergencies. You should keep all your work on GitHub, so it won’t matter if your computer explodes. If it does explode, you will lose only the work after your last push. You can then restart your work on a public computer (the basement of CGIS Knafel has machines with R/RStudio installed) or on your roommate’s computer.

Github Classroom: We use Github Classroom to distribute problem sets and exams. You will receive an e-mail with a link. Click on that link and a repo, with instructions, will be created. Do this as soon as you receive the e-mail. We don’t want GitHub problems to arise the night before the assignment is due.

Tardiness: We begin on time and end on time.

Announcements: You are responsible for any assignment/exam/deadline updates/changes which are either announced in class or promulgated via the course Canvas e-mail list. The official Preceptor’s Notes, posted to the Slack channel #preceptors-notes, are important, but we will e-mail them to you. You are not responsible for every random post on Slack.

Holidays and Wellness Days: No required class events occur on University holidays nor on this semester’s Wellness Days: Friday, February 5; Monday, February 15; Monday, March 1; Tuesday, March 16; Wednesday, March 31; Thursday, April 15.


Solo Participation: 5 points. This category relates to things you do alone in class. Missing class (without notifying us) or missing too many classes will cost you points, as will a failure to participate in class activities. Note that I do not care if you know the answer when I cold-call you. This plays no part in your grade. The most common example in this category is required e-mails, which we do in class. Whenever you send such an e-mail, you must bcc both your assigned TF and Preceptor.

Group Participation: 5 points. This category relates to activities you do with other students. Helping your fellow students, especially on Slack, is the best form of group participation, as is volunteering for a class role like scribe. Be a good class citizen. Help your classmates during Study Halls. Speak up during Recitations. Do not shirk on group projects.

Tutorials: 5 points. Tutorials are distributed in the primer.tutorials package. Make sure to install the latest version before you start a new tutorial. Grades are pass/fail only. Given the level of the questions and the hints provided, it is essentially impossible not to get full credit as long as you make an honest effort. If a question is too hard, just provide your best guess. If something seems broken, just skip it and go on. There are hundreds of questions. We don’t care if you miss a few, or even a bunch. But, if you just give up or provide nonsense answers, then you and your TF are going to have an unpleasant conversation . . .

Problem Sets: 22 points. Follow these instructions. The first problem set is worth 1 point. The remaining 7 are worth 3 points each. Problem sets after the first are distributed on Thursday and then due the following Wednesday at 11:59 PM. You are welcome to work on them with your friends but you must personally type in every character in the work you submit.

Exams: 35 points total. The four exams are take-home and unhackable. The first is worth 5 points and the others are each worth 10 points. They are open-book and open-web. Because students have different schedules, you can complete the exam any time within a four-day window starting after exam distribution. Late exams earn zero points. You may not seek or receive help on the exam from a person, e.g., asking a roommate or posting at RStudio Community. You may use any written materials from the class, including problem set answers. If you have a question, ask on Slack. Teaching staff (not other students) will answer it.

Final Project: 28 points. Students will present their projects publicly at the end of the semester. They will then have the opportunity to incorporate feedback before submitting the final version. There are eight milestones for the projects, each worth one point and each requiring the submission of a URL via Canvas. Demo Day (which includes a review of your code) is worth 10 points. The final project submission is worth 10 points. Follow the course style guide.

Calculation: Each problem set, milestone and exam is graded out of a maximum of score of 20, regardless of its weight in the final grade calculation. For example, both Exam 2 and Milestone 2 are graded out of 20, but the former is worth ten times as much to your final grade.

Final Project

Do you love soccer or wine or NYC politics? The final project provides you with an opportunity to study that topic in depth. Your final project will be, for most of you, the first item in your professional portfolio, something so impressive that you will be eager to show it to graduate schools or potential employers. You must show this work publicly, both on the web (viewable by all) and in person at our Demo Day. You will host your final project using Shiny Apps. Make use of free statistical consulting from the Harvard Statistics Department and from IQSS. Read this advice if you are working with data larger than 100 megabytes. Consider scheduling an interview with Hugh Truslow (), Head, Social Sciences and Visualization, Harvard University. No one at Harvard knows more about potential data sources. Visualization Specialist Jessica Cohen-Tanugi () is a great person to talk to about your graphics. Explore the final projects from past semesters.

There are four key components to every final project. First, you must have made a meaningful effort to collect and clean your data. Typing library(fivethirtyeight) is not enough. Second, your Shiny app must, on at least one panel, be interactive. It must provide the viewer with a choice of some sort and then report results which depend on that choice. Third, there must a statistical model, presumably on a “Model” panel, along with an associated discussion of its creation and interpretation. Fourth, there must be an “About” panel which provides an overview of the project, including a discussion of data sources. It must also provide a link to your Github repo for the project.

Other instructions: Follow the Style Guide. Use an informative name for both your repo and the app itself. (Do not name your repo final-project or gov-1005.) When opened, the Shiny app should default to an “interesting” panel, presumably something with graphics. You want to grab the reader’s attention.

Possible Approaches

Most students will gather some data, estimate some models, and create a Shiny App. Good stuff! But there are other possible approaches:

Paper Replication

You can replicate and extend a published paper from the academic literature. See examples from Gov 1006: Models from spring 2020. I recommend this approach for students who already know how to create a Shiny App.

Original Data Collection

Students interested in a topic about which there is no publicly available data are welcome to collect their own data. This must be something much more substantive than just asking 100 students outside Annenberg about their favorite salad. Two categories of data work best. First, pick a topic which you truly care about. Second, pick something Harvard-specific. This Crimson article and these class projects — spring 2019 and fall 2019 — are great examples of the latter.

Work with Other Classes

You are welcome to use data from your other classes in the creation of your final project. This includes thesis work. You automatically have permission from us to do this, but you must also obtain permission from the instructor of the other class.


Interested in doing a project which seems different from what we describe above? Come talk to me! The best projects involve topics which students are passionate about. If you really care about X, then we are eager to help you create a final project about X. Examples: participation in the NFL Big Data Bowl, submitting Numerai forecasts or entering a Kaggle competion.


If you had tried to complete a data analysis project before taking this class, you would have done X well. Now that you have taken the class – now that you know how to describe, predict and infer – you will do Y well. The success (or failure) of the class can be measured by comparing Y with X.


Everything — tutorials (Mondays), problem sets (Wednesdays), milestones (Fridays) and exams (Sundays) — is due at 11:59 PM, unless otherwise specified.

Rhythm of the Class

The class follows a steady weekly rhythm:

Monday 11:59 PM. Tutorials are due.
Tuesday 7:45 – 9:00 AM or 3:00 PM – 4:15 PM. Class.
Tuesday 4:00 – 7:00 PM, Study Hall.
Wednesday 9:00 AM – 12:00 PM, Study Hall.
Wednesday 11:59 PM. Problem sets are due.
Thursday 7:45 – 9:00 AM or 3:00 PM – 4:15 PM. Class.
Thursday evening. Problem set due next week will be distributed.
Friday 11:59 PM. Final project milestones are due.
Sunday 11:59 PM. Exams, if distributed, are due.

Key Dates

Part 1: Tools and Framework

Tutorial #1 due Monday, February 1.
Day off Friday, February 5.
Tutorial #2 due Monday, February 8.
Problem Set #1 due Wednesday, February 10.
Milestone #1 due Friday, February 12.
Tutorial #3 due Sunday, February 14.
Day off Monday, February 15.
Problem Set #2 due Wednesday, February 17.
Milestone #2 due Friday, February 19.
Tutorial #4 due Monday, February 22.
Problem Set #3 due Wednesday, February 24.
Milestone #3 due Friday, February 26.

Part 2: Sampling and Inference

Tutorial #5 due Sunday, February, 28.
Day off Monday, March 1.
Problem Set #4 due Wednesday, March 3.
Exam #1 distributed on Thursday, March 4 and due Sunday, March 7.
Tutorial #6 due Monday, March 8.
Milestone #4 due Friday, March 12.
Tutorial #7 due Monday, March 15.
Day off Tuesday, March 16.
Problem Set #5 due Wednesday, March 17.
Milestone #5 due Friday, March 19.
Tutorial #8 due Monday, March 22.
Problem Set #6 due Wednesday, March 24.
Exam #2 distributed Thursday, March 25 and due Sunday, March 28.

Part 3: Models

Tutorial #9 due Monday, March 29.
Day off Wednesday, March 31.
Milestone #6 due Friday, April 2.
Tutorial #10 due Monday, April 5.
Problem Set #7 due Wednesday, April 7.
Milestone #7 due Friday, April 9.
Tutorial #11 due Monday, April 12.
Problem Set #8 due Wednesday, April 14.
Day off Thursday, April 15.
Exam #3 distributed Friday, April 16 and due Sunday, April 18.

Part 4: Conclusion

Milestone #8 due Friday, April 23.
Last Day of class is Tuesday, April 27.
Demo Days
Final project due Wednesday, May 5.
Exam #4 distributed Thursday, May 6 and due Sunday, May 9.

Study Halls

Tuesday, 5:00 PM to 8:00, with Beau present from 5:00 to 6:00.
Wednesday, 10:00 AM to 1:00, with Jessica present from 10:00 to 11:00.


Week 0, January 25: Shopping Week

Purpose of this week is to help you decide whether or not you want to take the class. No one is required to be here. Only you can decide whether the goals of the class, and the workload associated with meeting those goals, make sense for you. We will spend most of our time in breakout rooms. One breakout session will be devoted to Tutorial Shopping Week, your answers to which you must submit via Canvas. Another breakout room session will begin Visualization-A, which is due on Monday.

Also, you must sign up for a 15 minute meeting with a member of the course staff, the purpose of which is to ensure that your computer is set up correctly. At that meeting, you will submit Milestone #0.

Readings: Shopping Week

Reminder: Initial tutorials are due Monday, February 1 at 11:59 PM.

Week 1, February 1: Visualization

You are Ulysses. I am the rope.

You will have your first Recitation with your Teaching Fellow this week. In class, we learn how to create and knit an R markdown file. We also connect to Github and discuss the importance of source control. We send a thank-you e-mail.

Day Off: February 5.

Readings: Chapter 1 Visualization and the first three parts of the Appendix on Tools: “Working with the terminal,” “Git, GitHub, and RStudio,” and “PDF.”

Assignment: Five tutorials (Visualization-A, Visualization-B, Visualization-C and Visualization-D, Tools) due Monday at 11:59.

Optional: Read and watch the videos from Getting Used to R, RStudio, and R Markdown by Chester Ismay and Patrick C. Kennedy.

Week 2, February 8: Wrangling

You can never look at your data too much. – Mark Engerman

The first problem set will be distributed on Tuesday, via Github Classroom, and completed during class. We will submit that problem set via Canvas. We send an e-mail to an alum.

Readings: Chapter 2 Wrangling and Maps.

Assignment: Four tutorials (Wrangling A through D) due Monday at 11:59.

Problem Set #1 due Wednesday.

Final Project Milestone #1 due Friday. Speak with your Teaching Fellow about your final project during your Recitation session or outside of it. Google Dataset Search is a good way to find data. See also these resources. Create a Github repo for this Milestone. Connect the repo to your computer. Write one paragraph in an Rmd file about your current thoughts/ideas/plans. Knit the Rmd file into an html. Push to Github. Submit to Canvas the url to the repo.

Optional: RStudio Essentials Videos. Most relevant for us are “Writing code in RStudio”, “Projects in RStudio” and “Github and RStudio”. Again, these are optional! But they are very useful for students who find traditional lectures to be a helpful supplement to classroom practice.

Week 3, February 15: Functions

The best data science superpower is knowing how to ask a question. – Mara Averick

We will learn about functions. We will learn how to produce a reproducible example — a “reprex” — in order to help strangers to help us. We send another e-mail to an alum. We will make a Shiny App.

Day Off: Monday, February 15.

Readings: Functions, Shiny and How to Get Help.

Assignment: Two tutorials (Functions A and B) due Sunday, February 14 at 11:59.

Problem Set #2 due Wednesday.

Final Project Milestone #2 due Friday. Github repo with Rmd (and knitted html) which discusses pros and cons two projects from past years. At least one project should be one which did extensive data gathering/cleaning. You should not select the same projects for commentary as your friends have. Students generally write about a paragraph for each project. The Rmd/html file should include the url for your repo. The only thing you are submitting is the url to your repo.

Optional: RStudio Webinar on Reprex and RStudio Webinar on List-columns. Again, these are optional! But they are very useful for students who find find traditional lectures to be a helpful supplement to classroom practice.

Week 4, February 22: Rubin Causal Model

No causation without manipulation. — Don Rubin

We will introduce the “potential outcomes” framework and review the fundamental problem of causal inference. We will discuss the slogan “no causation without manipulation.” We will explore Big Data on FAS OnDemand. In Recitations, you will make another Shiny App.

Readings: Rubin Causal Model and Animation.

Tutorials Rubin Causal Model A and B due Monday.

Problem Set #3 due Wednesday.

Milestone #3 due Friday. Create a Shiny App. It will be mostly empty. But it must have at least two tabs, one of which will be the “About” tab. The About tab should include the url to your repo, should we want to examine it. Discuss all your proposed data sources. (If you are gathering Harvard data, you should have a draft of your survey questions.) Remember: You must gather data from two or more different sources. Learning how to source, clean and combine data is one of the goals of the project. On almost any topic, there are useful tables of information on Wikipedia. See here and here for advice. Submit the url for your Shiny App to Canvas.

Optional: Chapters 1 to 6 of The Unix Workbench.

Week 5, March 1: Probability


Day Off: Monday, March 1.

Readings: Probability.

Tutorial Probability A and B due Sunday.

Problem Set #4 due Wednesday.

Exam #1 distributed on Thursday and due Sunday.

Optional: Statistical Rethinking: A Bayesian Course with Examples in R and Stan (pdf) by Richard McElreath. Chapter 1.

Week 6, March 8: One Parameter

Lot of points were taken off for small errors that I did not see as pedagogically important. – Gov 1005 student

Readings: One Parameter.

Tutorial #6 due Monday.

Final Project Milestone #4 due Friday. Improve your Shiny App by adding a tab which does something with you data. With luck, you will have gathered all your data and placed it in the repo. (This will generally be done with a different Rmd, like gather.Rmd, in your repo which contains the code which actually downloads your data.) You should have processed your data. (It is OK if you have not gotten quite this far as long as you discuss your progress and your plan in the About page.) This can be as simple as running summary(). You should still have an About tab. You may change your project completely, all the way until Demo Day. But you are still responsible for meeting these milestones, even if you know you are going to pivot. Your data can not be from a single source. Typing library(fivethirtyeight) is not enough! Submit the url for your Shiny App to Canvas. (This might be the same url as last week (with new material) or a new url at which you have started over.)

Week 7, March 15: Two Parameters

Comment as a service to the most forgetful possible version of your future self. – Alex Albright

Day Off: Tuesday, March 16.

Readings: Two Parameters and “Causal effect of intergroup contact on exclusionary attitudes” by Ryan Enos, PNAS March 11, 2014 111 (10) 3699-3704.

Tutorial #7 due Monday.

Problem Set #5 due Wednesday.

Final Project Milestone #5 due Friday. Add a beautiful graphic to your Shiny App, using ggplot2 or another package of your choice, which uses some of your data. This can still be very rough. We just want some evidence that you have some data and that you are doing something with it. If your data plans are behind schedule, just let your TF know. But you still turn in something. Submit the url for your Shiny App to Canvas.

Week 8, March 22: Three Parameters

Amateurs test. Professionals summarize.

Readings: Three Parameters.

Tutorial #8 due Monday.

Problem Set #6 due Wednesday.

Exam #2 distributed Thursday and due Sunday.

Optional: How to Start Shiny video tutorial.

Week 9, March 29: Four Parameters

Fitting is easy. Prediction is hard. – Richard McElreath

Day Off: Wednesday, March 31.

Readings: Four Parameters.

Tutorial #9 due Monday.

Final Project Milestone #6 due Friday. You must have a working Shiny App. It can be a mess, but it must have at least one attractive graphic with your data. You must have a Model panel with a fitted model and description thereof. You must have an About panel. Submit the url for your Shiny App via Canvas.

Optional: Shiny tutorials.

Week 10, April 5: Five Parameters

I stopped teaching frequentist methods when I decided they could not be learned. – Donald Berry

Readings: Five Parameters

Tutorial #10 due Monday.

Problem Set #7 due Wednesday.

Final Project Milestone #7 due Friday. Cleaned up Github account. Submit the url for your Github account to Canvas.

Optional:The Bayesian New Statistics” by John K. Kruschke and Torrin M. Liddel.

Week 11, April 12: N Parameters

Day Off: Thursday, April 15.

Readings: N Parameters

Problem Set #8 due Wednesday.

Exam #3 distributed Thursday and due Sunday.

Week 12, April 19: Even More Parameters

Put your work on the web. – David Sparks

Final Project Milestone #8 due Friday. Working rough draft of your Shiny App. You must have a fairly complete version of your current project: a Shiny app with your About page, your data and your model. Write a four sentence elevator pitch for your project and e-mail it to your TF. This pitch is how you will begin each presentation during Demo Day. Submit the url for your Shiny App via Canvas.

Week 13, April 26: Wrap Up

A public portfolio of high quality work will do more for your future than a Harvard degree.

Last day of classes. Make memes, provide course feedback, discuss final projects and have fun!

Optional: Mastering Shiny by Hadley Wickham.

Important: Check your grades on Canvas, including your calculated late days. Any questions/complaints must be made before the last day of classes. After that, no changes will be made.

Demo Day Sessions

Follow our detailed instructions. Although your presentation is not, itself, graded, we will take off points for showing up late or otherwise messing up the process. Demo Days are public events. We welcome all who are interested in your work. You, also, must send out two e-mail invitations (bcc’ing your assigned TF), one to your non-Harvard family/friends and one to your Harvard friend(s). We strongly urge you to invite your parents and/or extended family, but this is not required.



This course is inspired by STAT 545, created by the legendary Jenny Bryan. The pedagogical goals follow Don Rubin’s vision. Some of the classroom exercises come from Statistical Inference via Data Science: A moderndive into R and the tidyverse by Chester Ismay and Albert Y. Kim. Many thanks to all the folks responsible for R, RStudio, Git and GitHub. This course would not be possible without their amazing contributions.