Data Science Academy
The Wharton Data Science Academy will bring state-of-the-art machine learning and data science tools to high school students. We aim to stimulate curiosity in the fast-moving field of machine learning through this rigorous yet approachable program. Building up statistical foundations together with empirical and critical thinking skills will be the main theme throughout.
Overview
We believe data science is not just a collection of techniques; it is foremost motivated by real-world problems. The data scientist of the 21st century must be able to identify relevant problems, provide sensible analyses, and ultimately communicate their findings in meaningful ways. By the end of the Wharton Data Science Academy, students will not only be equipped with essential data science techniques such as data visualization and data wrangling but will also be exposed to modern machine learning methodologies, which are all building blocks for today’s AI field. Along the way, students will develop a working proficiency with the R language, which is among the most widely used by professional data scientists in both academia and industry.
What students can expect:
- Wharton instructors who are data science experts will lead the lectures and will also be available to students outside of class.
- Students will advance their skills with data from real-world cases and will be challenged to articulate their findings with a final project.
- Wharton undergraduate and graduate TAs will engage with students and share their experiences in studying data science.
- Guest speakers who will share their wisdom of data science as a career.
- Students will work in teams to complete a final project and present to their peers at the end of the program.
All participants who complete the program will earn a Wharton Global Youth Certificate of Completion.
Instructional Team
Program Leader: Linda Zhao
Linda Zhao is a full professor of statistics in the Wharton School. As an expert in machine learning, she has been teaching a modern data mining course to undergraduate, MBA, Master, and Ph.D. students throughout the entire Penn campus. Students comment that her data mining course is one of the most fun and useful courses offered at Penn. In addition to teaching regular Wharton students, Linda served as a co-director of the Wharton–SAC (Securities Association of China) executive program, which she successfully ran and taught. By teaching various levels of students, Linda is able to design and deliver state-of-the art machine learning skills to students from all different backgrounds. A fellow of the IMS, a leading international statistics organization, Linda has been actively engaged in her academic career. Her specialty falls in modern machine learning methods, replicability crisis in science, high dimensional data, housing price prediction, and Bayesian methods. Her work has won the NSF support for over 20 years. She is also an avid ballroom dancer and she loves to travel around the world.
Teaching Assistants
Teaching Assistants consist of both undergraduate and graduate students from the University of Pennsylvania. TAs facilitate small-group discussions, lead small-group lab work, ensure student understanding, assist with final project development, and hold office hours to answer student questions.
“My favorite part of the Data Science Academy was the final group project where my team and I were able to put our statistical learning skills to the test with a completely new set of data!” - Ramya S., California, USA