Data Science in Real Estate Online Short Course
Cambridge, USA
DURATION
6 Weeks
LANGUAGES
English
PACE
Part time
APPLICATION DEADLINE
Request application deadline
EARLIEST START DATE
Request the earliest start date
TUITION FEES
USD 1,900
STUDY FORMAT
Distance Learning
Introduction
Harness analytics to make more informed investment decisions.
Thanks to machine learning and data analytics, real estate professionals and investors are now able to make more accurate property assessments than ever before. This Data Science in Real Estate online short course from the MIT School of Architecture and Planning (MIT SA+P) focuses on growing your data science skills within the context of the built environment. During the program, esteemed MIT faculty and industry experts will teach you how to harness statistical techniques to reveal key insights into the factors that impact property investment and development opportunities.
Over the course of six weeks, you’ll learn how to evaluate and tidy data, expand data sets, and generate a selection of models that can be used to explain industry trends and forecast real estate values.
An online education that sets you apart
This MIT SA+P online program is delivered in collaboration with online education provider GetSmarter. Join a growing community of global professionals who have already benefited from the opportunity to:
- Gain verifiable and relevant competencies and earn invaluable recognition from an international selection of universities, entirely online and in your own time.
- Enjoy a personalized, people-mediated online learning experience created to make you feel supported at every step.
- Experience a flexible but structured approach to online education as you plan your learning around your life to meet weekly milestones.
Why choose GetSmarter?
GetSmarter, powered by 2U, is an online learning expert with over 10 years of experience in developing premium online short courses from the world’s leading universities and institutions. We are powered by 2U to support you in unlocking your potential through life-changing learning with an immersive and high-touch experience.
We deliver market-led courses that equip working professionals with the expertise required to upskill, reskill or kickstart a completely new career. Through a data-driven approach, we analyze future skills requirements and ensure all courses address this need. We ensure that leading universities and institutions are your expert guides and our people, technology, and resources are your engine – together we power more than just education, we power your potential.
Ideal Students
Is this course for you?
- Real estate professionals and independent investors looking to grow their property portfolios.
- Those with existing knowledge of data science who would like to harness it in a real estate context.
- Professionals who want to develop relevant skills in analysis and valuation in order to gain a competitive edge in the built environment.
- Anyone with an interest in real estate development, finance and investment, or data science.
While this course has no formal prerequisites, it’s recommended that you have a basic understanding of programming. You won't be expected to write code, but will need to understand and manipulate code written in the programming language R.
Program Outcome
You’ll walk away with:
- The ability to better inform your investment decisions and discover new opportunities in the real estate sector using insights taken from data analytics.
- A data science toolkit that will enable you to tidy and join data, and perform statistical analyses and modeling using interactive software applications like R and Jupyter notebook.
- The skills to analyze and interpret data surrounding the factors that impact real estate valuation such as trends, location, and amenities.
- Key industry insights from esteemed MIT faculty and industry experts from the real estate, finance, and IT sectors.
Curriculum
Over the duration of this online course, you’ll work your way through the following modules:
- Module 1: Data Science and Machine Learning Principles
Understand data science and machine learning concepts used in the built environment. - Module 2: Using Real Estate Data
Learn how to prepare real-world data for analysis. - Module 3: Initial Insights Using Statistics
Understand the value of quantifying and detecting patterns in data. - Module 4: Using Data Science to Ask Questions
Discover the stories that live in real estate data that can be used to explain and predict outcomes. - Module 5: Relationships in Real Estate Data
Understand relationships between outcomes and features using regression analysis. - Module 6: Forecasting in Real Estate
Discover how to use machine learning to forecast outcomes and support decision-making in real estate.
Manage your time
GetSmarter's learning model is designed to help you, as a working professional, improve your skills without compromising on work and family responsibilities. The course work is broken up into weekly, manageable bite-sized modules, with incremental deadlines, designed to help you pace yourself over the duration of the course and allow you the legroom to work when it suits you best.
At the beginning of each module, you'll be presented with the course content and assignments necessary for completion. You also have access to your Success Adviser who will help you manage your time, and support you with any administrative or technical queries you might have.
Admissions
Program Tuition Fee
Career Opportunities
Seek employer assistance
By improving your skills and industry knowledge, you'll have an influence on the success of your organization. Why wouldn't you ask your boss to help you fund your studies if it's going to have an impact on the way you do business?
Of our past students, 37 percent have received financial assistance from their employers. You can ask for help, too.
If you are a Learning & Development (L&D) manager, or involved in training and upskilling for an organization, you can request information regarding our corporate offering on our GetSmarter for business page.
Facilities
English Language Requirements
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