ONLINE COURSE | Applying Data Science & Machine Learning to Real Estate
In response to demand from global participants across institutional real estate and investment, PropertyQuants is launching additional runs of the Applying Data Science and Machine Learning to Real Estate Masters-level hands-on courses.
What you will learn:
- Data science fundamentals applicable to all industries including Python, Pandas, and Scikit-Learn
- Data science methods for real estate, including index construction, automated valuation, cluster analysis, and time series forecasting (ARIMA, VAR, and VECM)
- The ability to utilise large datasets to determine fair transaction prices and forecast future returns
- Geographic Information Systems – software for spatial location analysis and visualization
You will complete an individual capstone project where participants can work on a dataset that is relevant to them. Optional 1-on-1 sessions and graded assignments are included. Participants to date have come from across real estate industry, investing, appraisals, proptech and data science.
This in-depth course is relevant for:
- Anyone involved in real estate research aiming to use data and machine learning to produce game-changing insights and unlock the value of large datasets
- Those looking to get into the rapidly growing proptech industry
- Investors who want to use data-driven approaches to find exceptional opportunities and beat the market
To learn more about the opportunities for data science in real estate, watch this recent presentation made by Nelson Lau, PhD and PropertyQuants CEO, lead instructor for the course.
Find the upcoming course schedules below – there are course runs starting on 19 January, 5 March and 27 April.
|Jan 2021 course||March 2021 course||Apr 2021 course|
|Topic||Date & Time||Date & Time||Date & Time|
|[B1] Python Bootcamp – Part I||Jan 19 8am-11a m GMT||Mar 4 8pm-11pm EST||Apr 27 9am-12pm BST|
|[B1] Python Bootcamp – Part II||Jan 20 8am-11am GMT||Mar 5 8pm-11pm EST||Apr 28 9am-12pm BST|
|[B2] Pandas Bootcamp – Part I||Jan 26 8am-11am GMT||Mar 11 8pm-11pm EST||May 4 9am-12pm BST|
|[B2] Pandas Bootcamp – Part II||Jan 27 8am-10.30am GMT||Mar 12 8pm-10.30pm EST||May 5 9am-11.30am BST|
|[B3] Scikit-Learn Bootcamp – Part I||Feb 2 8am-11am GMT||Mar 18 8pm-11pm EDT||May 11 9am-12pm BST|
|[B3] Scikit-Learn Bootcamp – Part II||Feb 3 8am-10am GMT||Mar 19 8pm-10pm EDT||May 12 9am-11am BST|
| Course Introduction & Web Harvesting||Feb 9 8am-11.30am GMT||Mar 25 8pm-11.30pm EDT||May 18 9am-12.30pm BST|
| Property Price Indices||Feb 16 8am-11am GMT||Apr 8 8pm-11pm EDT||May 25 9am-12pm BST|
| Automated Valuation Models||Feb 23 8am-11am GMT||Apr 15 8pm-11pm EDT||Jun 1 9am-12pm BST|
|[G1] Geographic Information Systems I||Mar 2 8am-10.30am GMT||Apr 22 8pm-10.30pm EDT||Jun 8 9am-11.30am BST|
|[G2] Geographic Information Systems II||Mar 9 8am-10.30am GMT||Apr 29 8pm-10.30pm EDT||Jun 15 9am-11.30am BST|
| Time Series Forecasting I||Mar 16 8am-11am GMT||May 6 8pm-11pm EDT||Jun 22 9am-12pm BST|
| Time Series Forecasting II||Mar 23 8am-10am GMT||May 13 8pm-10pm EDT||Jun 29 9am-11am BST|
| Cluster Analysis||Mar 30 9am-11am BST||May 20 8pm-10pm EDT||Jul 6 9am-11am BST|