QAI Summer Course

QAI Summer Program

The 2020 QAI Summer Program is now full. Thank you for the enthusiastic response to the call for applications!
The QAI Summer Program is a free, non-credit, second-level applied statistics and data science course designed for Wellesley students. The program's goal is to introduce advanced data skills while supporting students in their current and future research projects. Topics include statistics fundamentals, such as study design, statistical inference, and tools for handling common complications like missing data or multiple comparisons; practical data handling skills, including data cleaning and the use of complex data sets; and data communication, such as best practices for visualization. All students will become proficient in the statistical software R, which is free, open-source, and field-neutral, and training in other skills such as SQL, webscraping, and interactive graphics will be available as well. The course is also intended to bring together students from a variety of disciplines whose work involves data. 
The program will begin on Monday, June 8. For some students, the course will end after six weeks on Friday, July 17, though other students may choose to continue two more weeks until Friday, July 31, as explained below.
About the QAI: The QAI was founded in 2013, designed to expand the role of statistics and data science in research, learning, and teaching at Wellesley and beyond. Since its first year, the QAI has run a summer program. The QAI also includes courses during the semester, statistical consulting for research projects, and other resources.
Please read the QAI Summer Program details and FAQ below. Applications will be accepted on a rolling basis, possibly until Thursday, June 4, but the sooner you apply, the higher the chance that we will be able to find enough teaching assistants to accommodate all qualified applicants.
Here are some ways that this year’s program compares to the programs offered in previous years:
In the past:
The QAI Program blended live, on-campus meetings with online resources. The materials were developed as part of the college’s Andrew W. Mellon Blended Learning Initiative.
This year:
The program will be completely remote. Lectures will be delivered by asynchronous, recorded videos. Labs and office hours will be scheduled at a variety of times, including late evening hours, based on the availability of participating students. Students will be able to participate from any time zone.
In the past:
The QAI program has been primarily aimed at those participating in the science or social science summer research programs (including research assistants from anthropology, biological sciences, chemistry, computer science, economics, engineering, environmental studies, Italian studies, neuroscience, political science, psychology, the Botanic Gardens, and Wellesley Centers for Women). Students have been eligible to participate if they have completed any QR Overlay course.
This year:
The QAI program is open to all Wellesley students in the Classes of 2020, 2021, 2022, or 2023. The prerequisite is any introductory statistics course at Wellesley (STAT 160, STAT 218, ECON 103/SOC 190, PSYC 205, BISC 198, POL 299, or STAT 101). QR Overlay courses that do not appear on this list are not sufficient prerequisites. If you took an introductory statistics course at another college, please let us know the college and course number in your application. AP Statistics is not a sufficient prerequisite.
If you have taken STAT/QR 260, you should not apply for this program. However, students who have already taken other post-introductory statistics courses, such as STAT 318 or ECON 203, are encouraged to apply.
In the past:
The QAI Program has been a non-credit version of STAT 260/QR 260, and students who completed the program earned QAI Certificates. Students sent about ten hours each week (range: 8-15 hours) to the course, for eight weeks. Students who earned certificates could move on to courses that require STAT 260 as a prerequisite, meet the modeling requirement for the statistics minor or data science major, and apply for QAI Internships.
This year:
Students accepted into the QAI Program can choose between two tracks:
Summer Session track: The timeline and time commitment for this track matches the shortened format of this year’s summer courses and summer research programs. Students will be expected to dedicate an average of ten hours each week to the course for six weeks. Students will gain new skills in R programming, data management, and statistical concepts. However, the Summer Session track will not cover all of the material in STAT 260, so students on this track cannot move on to courses that require STAT 260 as a prereq, meet the statistical modeling requirement for the statistics minor or data science major, or (in most cases) apply for QAI internships. Students who complete the Summer Session track may still enroll in STAT 260 after the summer.
QAI Certificate track: Students aiming to earn QAI Certificates will spend an average of 80 hours on the QAI program, rather than the 60 expected in the Summer Session track. Sixty of the 80 hours will be spread over the six weeks between June 8 and July 17. The extra 20 hours of work may be completed during the same six weeks, or they may be completed over the two subsequent weeks, by July 31. These 20 extra hours of learning include both additional topics and additional assignments. To earn a certificate, a student must complete all course requirements and submit work equivalent to at least an overall grade of B. Students earning QAI Certificates will have covered all of the material in STAT 260 and can enroll in courses that require STAT 260 as a prerequisite,
check off the modeling requirement for the statistics minor or data science major,
and apply for QAI Internships.
Depending on how many students accepted into the course are aiming for each track, office hours and labs may be combined or separate the tracks. Different assignments may be posted for the two tracks each week. Running the program with two tracks is an experiment this year, and we will adapt to the needs and goals of the students in the course.
In the past:
Since most past participants were summer research students, their advisors signed off on their participation in the QAI Course, agreeing that the students could spend 10 hours/week of their internships on the QAI program.
This year:
Students who are participating in a Wellesley summer research program or any other internship or job should include their advisor or supervisor’s email address in the application. Please discuss the QAI program with your advisor before applying. This person must agree that the student can dedicate 10 hours each week to the QAI program. In our experience, the summer does not go well if students attempt to complete the QAI program on top of a full-time position, unless they are released from that work for 10 hours each week. Ten hours is more time than it sounds like. If you are working 35 total hours/week, it is 28% of your time!
Students who are supporting themselves with certain in-person jobs this summer (such as delivery or grocery) may not need a supervisor’s sign-off to apply for the QAI program. Please explain your summer situation in your application.
In the past:
About 20-25 students have been accepted to the QAI Program each year.
This year:
We will do our best to accept every qualified student into the program. 
In the past:
The course has been taught by the QAI Director, Casey Pattanayak, along with two teaching assistants.
This year:
The course will still be taught by Casey Pattanayak and a team of teaching assistants. The TAs are Wellesley students who will be supporting the course remotely from all around the world.
In the past:
Students learned the material by completing quizzes and problem sets.
This year:
Students will complete quizzes and problem sets, as usual. Students will also answer questions while watching recorded lectures. Students on track to earn QAI Certificates will be asked to complete additional assignments.
All students will need access to the internet and a computer (an iPad or Chromebook is not sufficient). Please let us know on your application if accessing this technology is an obstacle.
Frequently Asked Questions: 
Will the QAI program appear on my transcript? No. Instead, students usually list it on their resumes, as a way to signal advanced statistical and data programming skills.
Will I earn academic credit for this course? No.
What does the course cost? Nothing.
Will I earn a grade? Because the course is non-credit, you will not earn grades that show up on a transcript. However, we will grade your work and keep track of those scores. For those aiming for QAI Certificates, we will calculate what your overall final grade would have been at the end of the summer, if the course had been for credit. Students earning the equivalent of a B or higher will earn a certificate.
How does the QAI program fit into my statistics minor or data science major?
A QAI Certificate shows that you have completed the equivalent of STAT 260 and meets the modeling requirement for the statistics minor or data science major. However, as a non-credit courses, it cannot be counted as one of the five courses toward the minor or one of the twelve courses toward the major. So, if you earn a QAI Certificate, you should work with your major or minor advisor to choose an additional elective to replace your modeling course toward your major or minor. This elective should usually be at STAT course.
How can the QAI program help me move forward with my statistics sequence?
After you have earned a QAI Certificate, you can move on to courses that require STAT 260 as a prerequisite, such STAT 309 or STAT 318. We usually do not have enough seats to accommodate everyone who wants to take STAT 260 (it is offered annually in the fall), so learning this material in the summer is a way to make sure you do not have to wait to move forward with statistics.

For more information: Please contact Cassandra Pattanayak at


To view the students/alumnae who have received QAI Certificates in the past, see here.