Stats, Graphs, and Data Science^{1 and 2} are a series of workshops (each 14-hours in length) that apply modern data science principles, predictive analysis, and types of artificial Intelligence.  The foundation is Evidence-Based-Valuation (EBV)^© methods to real estate analysis. These courses emphasize hands-on, activity-based learning using real data. A laptop is required for classroom courses. Your desktop computer is required for classes taught on Zoom. Also, two screens for the Zoom courses is strongly recommended.
 
In Stats, Graphs, and Data Science 1, students practice two basic analytical modeling tools: adjustment calculation using contrasting, and simple regression.  Using real data sets, objective data selection presents through use of visual plots. Students apply summary statistics and visualization in market selection and for adjustments. Methods of transition from canned appraisal software and the spreadsheet— students apply results using a real open-source analytics package, as well as spreadsheet.
 
These tools are particularly useful for sparse-data appraisal problems through estimation of location adjustments and price indexing of older data.

R, with the RStudio interface are used and compared to traditional accounting spreadsheet add-ons. These programs, plus graph and mapping packages are provided at no charge. Assistance with download and installation are provided after registration if you need it.  All example data sets are provided.

Instructed by George Dell, SRA, MAI, ASA, CRE,  other Instructors for this class include Bruce Hahn, CCIM, MAI, SRA,  John Fariss, MNAA.

Completing Stats, Graphs, and Data Science 1 is the entry level requirement for joining the Community of Asset Analysts.

Stats, Graphs, and Data Science 2, continues on the foundations of SGDS¹.  Building on the SGDS¹ learning in depth, breadth, and detail.  We continue examination of the theory, concepts, and practice of data science as applied to asset analytics.  Students are prompted to download a local data set, to learn and perform relevant  custom outputs.

The learning follows the development sequence of valuation and risk analytics.  Starting from the problem identification (scope of work) in measurable terms, we proceed to:

• Identify the overall project data frame;
• Define the CMS^© (Competitive Market Segment), the ideal data set;
• Resolve the bias-variance tradeoff to optimize accuracy and precision of results;
• Perform necessary data wrangling (analogical to traditional confirmation/verification)
  • Portray data visually
  • Reduce to the CMS^©
  • Detect quantify missing/needed predictor variables
  • Create new calculated fields
  • Transform functional relationships (e.g., linear to logarithmic)
  • Create Transformed data levels
  • Identify and handle outliers
  • Recognize needed predictive confirmation/verification

This class is experienced in R, RStudio, and R packages, including ggplot2 and tidyverse.  A comparison and integration of spreadsheet tables is provided for workflow efficiency.  

Specific analytics include polynomial price indexing, seam regression, introduction to GIS in R, data selection algorithms (particularly hierarchical cluster analysis), and sources of help for R.  Also we resolve conflicts in USPAP, AI-SVP, GSE guidelines, and EBV^© (Evidence Based Valuation) practice, and reporting in a data-stream delivery through dashboard views.

In addition to George Dell, SRA, MAI, ASA, CRE, as your instructor, other Instructors/contributors for this class include Bruce Hahn, CCIM, MAI, SRA,  John Fariss, MNAA, and Charles Abromaitis AACI, P. App.

Bruce Hahn is an independent fee appraiser, applying R analytics to residential and other assignments from Reno, NV.

John Fariss is an independent fee appraiser, applying R analytics to residential assignments in Bakersfield, CA 

Charlie Abromaitis  is the pioneer in the practical application of statistical/analytics software for valuation, from London, Ontario, Canada

All instructors are leading members of the Community of Asset Analysts^©

Included:

• Hands-on Instruction
• Open-source data analysis software
• State CE Certification available in most states
• Student attendance certificate provided at the end of class

Bonuses: 

Reference Book, Handouts, R script, and Practice Data Sets.