Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
According to a Deloitte report, 34% of commercial real estate firms in North America want to expand their expenditure in data analytics in the coming year. Real estate investors and professionals can now conduct more accurate property appraisals than it has ever been, just because of data analytics and machine learning. The Data Science in Real Estate online course is designed to help students develop data science abilities in the constructed environment's context.
According to a PwC report, 55% of commercial property CEOs think that leveraging data to improve customer satisfaction and generate a competitive edge is a top priority. Throughout the Data Science in Real Estate training, renowned MIT academics and industry professionals teach how to use statistical approaches to uncover crucial insight into factors that influence development prospects and real estate investment.
The Data Science in Real Estate syllabus teaches students how to assess and organize data, extend data sets, and build a variety of models that could be used to evaluate industry patterns and anticipate real estate values.
The Highlights
- 6 weeks duration
- Shareable certificate
- Online learning
- Course provider Getsmarter
- Projects and assessments
- Downloadable resources
- Split option of payment
- 7-10 hours per week
- Self-paced learning
- MIT offering
Programme Offerings
- Case Studies
- video lectures
- Infographics
- quizzes
- Offline resources
- Live polls
- online learning
- Self-paced learning.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | MIT School of Architecture and Planning, Cambridge |
Fee type | Fee amount in INR |
Data Science in Real Estate fees | Rs. 89,108 |
Installments pattern -
| 1st installment | Required before: 2025-04-01 | Amount Due: ₹44,554.00 INR |
| 2nd installment | Required before: 2025-05-01 | Amount Due: ₹44,554.00 INR |
Eligibility Criteria
Certification Qualifying Details
To qualify for the Data Science in Real Estate classes program, Applicants must finish all of the online course's learning modules and submit all projects and assignments. Applicants are required to engage in interactive class activities such as quizzes, live polls, surveys, case studies, and more. Applicants are evaluated based on a sequence of submitted projects, assignments, and class activities. To qualify for accreditation, candidates must complete all of the requirements outlined in the coursebook.
What you will learn
After completing the Data Science in Real Estate online course from MIT, Students will learn about the essential tools for making educated real estate investments decisions using big data insights. Students will gain an understanding of the numerous elements that influence the value of a real estate investment and the ability to use dynamic software tools such as R and Jupyter notebooks to do statistical modelling and analysis. Candidates will also gain an understanding of the foundations of machine learning ideas as they apply to the built world.
Who it is for
- Anybody interested in finance, real estate development and investing, or data science, i.e, data scientist, financial analyst, financial advisor, a financial planner who wants to expand their property holdings.
- Individuals with a prior understanding of data science who want to learn how to apply it in the real estate industry.
- Individuals fascinated by the built environment seek to gain a competitive advantage by learning the skills required for analysis, appraisal, and informed decision-making.
Admission Details
To enrol in the Data Science in Real Estate classes course by MIT, follow the steps mentioned below:
Step 1. Open the course website by following the link below
(https://www.getsmarter.com/products/mit-sa-p-data-science-in-real-estate-online-short-course)
Step 2. Click on the ‘Register Now’ button to start the registration
Step 3. Agree with the provider terms and conditions and continue
Step 4. Create a profile on Getsmarter by filling in personal details
Step 5. Provide the billing address and sponsor details
Step 6. Pay the fee amount and start learning at the scheduled date and time
The Syllabus
- Demonstrate a conceptual understanding of data science and machine learning in the built environment
- Recall the fundamentals of data science and machine learning
- Discuss practical applications of data science and machine learning in the built environment
- Assess the ethical issues related to data analysis and its application in the built environment
- Investigate new data science tools
- Discuss the need for good data management
- Identify data management strategies and best practices
- Practice tidying data and detecting anomalies
- Reflect on the challenges of joining data sets and evaluating data
- Implement the steps to join real estate data sets
- Apply the frequency toolkit in R
- Discuss the use of frequency distributions and sample statistics to assess data
- Identify the principles and attributes of correlation
- Execute a time series analysis using the toolkit in R
- Practice doing a correlation analysis using R
- Evaluate the outcomes from a time series analysis
- Debate the outcomes of a geospatial analysis on real estate data
- Execute a geospatial analysis using the toolkit in R
- Articulate the use of outcomes to answer questions that support decision making
- Identify features and outcomes in the context of real estate
- Formulate a question that can be supported by an outcome
- Describe the importance of identifying the type of relationship between features before doing analyses
- Justify the features that could be used to answer your question
- Discuss regressive value proposition outcomes in real estate
- Discuss the challenges and opportunities related to econometrics and forecasting
- Outline how machine learning can be used to predict outcomes
- Practice regression analysis using real estate data
- Describe the drivers affecting value in real estate
- Interpret the results from a regression analysis
- Investigate a strategy to communicate relationship information to non-technical stakeholders
- Analyze the accuracy of a regression analysis
- Assess how information can be presented in an ethical manner
- Review machine learning methods
- Recognize the value of machine learning for forecasting
- Use machine learning methods to forecast the value of an asset
- Evaluate the predictive performance of models
- Evaluate guided forecasts
- Reflect on the ethical impact of using machine learning
- Discuss a strategy to include relevant data science applications in your business
- Demonstrate an understanding of the future of data science within the real estate industry