We are excited to offer our online course in data science! Developed and taught by NYU instructors, this 12-week course is a great opportunity for those looking to expand their STEM interests beyond the high school curriculum. This class is designed for all levels.
- 2023 Course Dates: January 31 – April 25
- Instructor: Hui Guan, NYU
- Tuition Rate: $2,000
- Graded course
- Students receive an official NYU academic transcript
- Lecture Days/Times: Tuesdays 7pm – 8:30pm (ET)
- Lab Days/Times: Students are required to attend a weekly online lab and may choose between a weekday evening or a weekend morning
- Open to current juniors and seniors
- Application deadline: December 15th
Why GSTEM Data Science?
Because data science is evolving quickly and being a data scientist is a very marketable skill set. The ability to gather, process, analyze, and visualize data will give you an incredible advantage as you move forward with your STEM studies and STEM careers.
Because data is everywhere! We often don’t realize the extent that we use it and how it influences our everyday lives.
Because data science has always been an important, underlying part of the program. The Winston Data Scholarship for our summer program has been around since 2015.
What You’ll Learn
This course introduces ideas and techniques in modern data analysis, including interpreting and visualizing data, using data to build mathematical models, and assessing the validity of models and their predictions. The course is hands-on and data-centric, and will give students the opportunity to analyze data and create models.
By the end of the course, students of all levels will:
- Understand how to interpret data and data visualizations, and use python and jupyter notebooks to work with data tables and to produce data visualizations
- Understand what mathematical models are, how data are used to build, assess, and validate models, and how models are used to make predictions
- Gain some basic programming skills, particularly those that are relevant for data analysis and modeling
- Enhance their STEM skillset and marketability by applying data science to future research opportunities
Application & Eligibility
Our Data Science Course application is now closed.
Applicants must include a current transcript through Spring 2022 and include a list of their Fall 2022 classes. Two short essays are also required.
The course is open to current juniors and seniors.
Math prerequisites: Must be taking or have taken Pre-calculus (or its equivalent)
Computer Science prerequisites: Must have taken or be taking AP Computer Science Principles (or an introductory computing course)
Unfortunately we are unable to admit international students at this time. (Only those already in the U.S. on an F-1 student visa are eligible.)
Questions?
We’re happy to help. Contact us at gstem@courant.nyu.edu.
Testimonials
This course has made me more appreciative of data science and the countless opportunities it provides to researchers. After taking this course, I am more aware of the data around me and the countless data files that appear in my everyday life. Now, with the skills from the course, I can explore any data file that I am curious about and draw my conclusions about problems that I may deal with.
Prior to GSTEM, I had little to no background in computer science. The program helped me delve into the realm that I was previously afraid to enter, and made it so that I could incorporate my love for research and scientific inquiry.
With the knowledge and skills gained from this course, I can now implement data science in my future STEM studies. As an ardent scientific researcher, I plan to further my inquisitiveness in my undergraduate education; I will bring my data science prowess to new laboratories and institutions in order to examine experimental results and harness new interdisciplinary studies.
I emerged from this class with thorough knowledge of a new programming language and an understanding of a modern utility/subject which will only become more significant as the world continues to digitize.
Working with like-minded peers in a supportive environment encouraged me to ask questions and go beyond the syllabus to maximize my learning from this experience.
I especially enjoyed the recurring topic of considering the human contexts of data and data science; we should simultaneously consider the technical consequences as well as the safety, ethical, and moral impacts of all our discoveries and practices.
