Stats, Graphs and Data Science II

Stats, Graphs, and Data Science2 broadens and deepens understanding of the five primary data science analytic tools.  Critical spreadsheet, statistical, and math concepts are related to appraisal problems. 

  1. Review of mathematics, computation tools, reasoning, and visual interfaces;

  2. The measurement of reliability (a central feature of valuation data science);

  3. Expanding data sets through indirect and analogous information;

  4. Data enhancement:  controls, coding, transformations;

  5. Three variable regression;

  6. Work-scope for predictor estimation (takings, etc.) is demonstrated;

  7. Forecasting and real-time valuation practice;

The importance of market identification and delineation (data set selection), is related to good practices in documenting data. The handling of exceptions and outliers, and the relation to verification, validation, and confirmation of market data will be explored. Modeling concepts will be examined and the decisions involved in choosing the right model for each application will be taught.

Additional spreadsheet and analytics software practice involves sorting, coding, and calculated fields; including non-linear and kinky trend lines.  Three-variable regression concepts and problems are introduced, with indicator (binomial) variable use compared to matched-pair concepts.  Case studies apply methods to deepen and widen the analyst’s ability to address any type of problem, including partial interests, sparse-data situations, and unique properties. Employ indirectly competitive and analogous transactions to reliably widen the data set as needed. 

Learn the basics of reliability scoring of the valuation product, to integrate with user needs for risk analytics, revaluation, and regulatory compliance.  Reliability is contrasted with “credibility.”  Credibility is subjectively judged (e.g., USPAP Standard 3). Reliability is an objective, quantified measure of accuracy.

Stats, Graphs, and, Data Science I is a prerequisite – no exceptions in respect for those who are able to proceed to the next level of competence.       

Personal integrity, competence, and professional satisfaction are emphasized throughout.  The positive interplay of valuation standards, regulatory requirements, legal applications, and investment consulting needs are addressed throughout the learning time.

The use of R related community-building, with sharing, reusability, and collaborative work is presented.  Full user access to the professional networking media center is granted for those able to participate.