SGDS2 in Oakland October 28 - 30, 2020

$345.00 USD

SGDS2 continues of 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.  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 substantially experienced in R, RStudio, and R packages, including ggplot2 and tidyverse.  A comparison and integration of Excel® is provided for workflow efficiency.  Methods of transition from canned appraisal software and the spreadsheet— to a real analytics package is reviewed.

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

What you'll get:

  • Instruction on Evidence Based Valuation©
  • Free Reference Book, Handouts, and Practice Data Sets
  • Free Open Source Software
  • Lunch Provided on Day 1!
  • Half-Price Tuition for Returning Students (conditions apply)

Stats, Graphs, and Data Science1 is a workshop applying modern data science methods, predictive analysis, and evidence-based© valuation, emphasizes hands-on activity-based learning using real data.
Students will leave with two basic analytical modeling tools: adjustment calculation using contrasting, and simple regression.  
Objective data selection and its importance is presented by the use of visual plots. Summary statistics in market selection and for comparison support is also introduced.
These tools are particularly useful for sparse-data appraisal problems through support of location adjustments and price indexing of older data.
R, (with RStudio), one of the most widely used data analysis software programs is introduced and compared to traditional accounting spreadsheet add-ons. These open-source software packages are available at no charge on the Internet. Assistance with download and installation are provided after registration if you need it.

Account Information

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 Cancellation, Refund, Final Examination and Re-Examination Policy

If the class is cancelled or postponed for reasons beyond the control of Valuemetrics.Info (e.g.: inclement weather), every attempt will be made to reschedule the class. Refunds will be made to those unable to attend on the new date.

If a student cancels, a $25 cancellation fee will be charged for cancellations less than 7 days prior to the start of the course. No refunds will be given for “no-shows” or cancellations after noon local time the day prior to the start of the course.

Continuing Education seminars do not require examinations. This school offers only continuing education. Grading is strictly pass/fail, based on adequate attendance and professional behavior. In order to promote and preserve the public trust inherent in professional appraisal practice, an appraiser must observe the highest standard of professional behavior and ethics in a formal learning context as well as in providing appraisal services. Learning is an interactive process affecting peers and others in their ability to perform ethically and competently.

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