Advanced Topics in Real Estate Data Analytics
Real estate has become a sophisticated industry that now relies on advanced data analysis to drive investment and other critical decisions. You gain exposure to advanced topics in data analytics used throughout real estate and be exposed to hands-on projects using data from major providers in the industry. Applied statistics will be presented using the open-source R statistical computing environment, together with Jupyter notebooks, an increasingly popular presentation environment. You will gain hands-on experience creating statistical models to drive informed decision-making in real estate investment.
Note: Registering at least two weeks prior to the start of the course date is highly recommended. Popular classes fill up quickly and more specialized classes need sufficient enrollment.
For general information about this course, please call 212-992-3336 or email sps.realestate@nyu.edu.
If you are registered for an online course and are not able to access/view your course in Brightspace, please note the following:
- It may take at least 24 hours from the time you registered for your information to be transferred into Brightspace.
- New students registering two days or LESS before the start date of the course may experience delayed access.
For additional technical support, contact the IT Service Desk (available 24/7/365) at 212-998-3333 or AskITS@nyu.edu.
You'll Walk Away with
- An understanding of the available economic and real estate data sources and the range of potential analyses that can be performed using such data
- The skills to develop advanced statistical models to inform real estate decisions, including real estate investment decisions
- An exposure to the application of cutting-edge techniques, including Deep Learning and Bayesian inference
Ideal for
- Real estate professionals looking to broaden their skill set
- Those interested in gaining sought-after skills to enter or advance in the real estate industry
NO open sections available for this course at the moment. Please check back next semester.
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Section
1 -
Semester
Spring 2024 -
Date
Jan 22 - Mar 4 -
Day
Self-Paced -
Time
Self-Paced -
Sessions
N/A -
Faculty
TBA -
Location
Self-Paced
Tuition $895 -