--With a Research Focus
Statistics plays a central role in both data science and machine learning. The Data Science and Machine Learning (DSML) program introduces students to some of the most popular statistical methods in data science and machine learning for various tasks, such as classification and regression. These methods will include very modern techniques such as ensemble tree-based methods (including bagging, random forests, boosting such as gbm and xgboost, etc.) and neural networks, as well as some ever-popular classical techniques. Students will learn not only what the methods are, how they work, why they work, but also how to code and use them in real data. Course projects will provide students the opportunity to apply these methods in real datasets and identify/tune-up/develop the best model. Working on these challenging real datasets will be a great research experience for students.
- An academically rigorous program that incorporates 28 classroom contact hours with faculty, 6 hours of workshops, and 6 hours with teaching assistants.
- Live and synchronous classes with a University of Notre Dame professor in the Department of Applied and Computational Mathematics and Statistics.
- Live workshops on Data Storytelling, Data Ethics, Applying to Grad School in the U.S. & at Notre Dame, Academic English Presentation Skills, etc.
- Panel discussion with Notre Dame’s current international PhD students.
- Have access to University of Notre Dame’s student learning platform and all classroom materials.
The schedule linked below is for reference purposes only. A finalized day-to-day schedule will be provided to all participants at least two weeks before class begins.
100% online and synchronous (live)
14 days of classes
Dates & Times
2023 dates are TBD
The core class, Statistical Methods in Data Science and Machine Learning, meets on weekday evenings, US Eastern Standard time. Workshops are often conducted in the evenings and mornings, US Eastern Standard Time.
- International students enrolled in any accredited institutions internationally or in the United States.
- Minimum GPA: 2.75
- Preferred English proficiency:
- TOEFL iBT "My Best Score" 80
- IELTS 6
- Duolingo English Test 105
- Chinese English Test 4 (CET4) 500
- Chinese English Test 6 (CET6) 450
Preparation for This Course
- Basic knowledge of applied probability and applied statistics, especially, linear regression.
- R programming. Suggested read - click here (Chinese version).
Submit your application using the link below.
PLEASE NOTE: You will be required to upload a copy of your English proficiency test report and a copy of your university transcript. Both reports can be unofficial, but must be legible and with your name on the report.
2023 dates are TBD
Admitted students will receive detailed instructions on making the program fee payment (via credit card) once their application is submitted and reviewed.
- Withdraw on or before July 15 (US Eastern Time) - full refund.
- Withdraw between July 15 and July 31 (US Eastern Time) - refund minus a $100 withdrawal fee.
- Withdraw on or after August 1 (US Eastern Time) - no refund.
Enrolled participants are expected to attend all classes and mandatory workshops. Participation requirements are detailed on the schedule. Personal emergencies such as illness or technological issues can be excused and program staff must be notified.
Upon successful completion of the program, each participant will receive an official program completion certificate.
Some students might wish to apply for scholarships at their home universities to cover their program fee. For this purpose, an official program invoice can be provided upon request. Please email firstname.lastname@example.org to request your invoice.
Please email email@example.com for any questions.