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Data Scienceand Analytics
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9/18/24, 12:28 Data ScienceandPM Analytics
4/20/2019 Spark Page
DATA SCIENCE
& ANALYTICS
Teaching Innovation and Learning Technologies
(TILTed Tech Event @ Fort Hays State University),
March 26, 2019
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,9/18/24, 12:28 Data Scienceand
PM Analytics
4/20/2019 Spark Page
Dr. Shalin Hai-Jew, ITS, Kansas State University
Part 01: A Crowd-Sourced
Definition of "Data Science"
“Data science,” both as a term-of-art and as many
types of practices, is still being defined. In one crowd-
written definition, it is “a multi-disciplinary field that
uses scientific methods, processes, algorithms and
systems to extract knowledge and insights from data
in various forms, both structured and unstructured,
similar to data mining” (“Data science,” Feb. 20, 2019).
Classic basic data structure...
labeled
transpose-able
stackable
substitute-able
reconfigurable
union-able
interpret-able
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,9/18/24, 12:28 Data Scienceand
PM Analytics
4/20/2019 Spark Page
A Basic Data Table Structure for Structured (Labeled) Data ->
Data Arrays -> Matrices -> Cross Tabulations...
The definition of "data" has expanded--from structured
data (data arrays, data tables, spreadsheets) to semi-
structured/unstructured data (imagery, text sets,
multimodal data sets), and others. This draws from the
ideas in qualitative research that everything has some
data value and can be informative in one way or
another. Data also come from sensors and IoT (Internet
of Things) devices and any number of other sources. An
assumption is that some degree of "ground truth" is
accessible.
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, 9/18/24, 12:28 Data Scienceand
PM Analytics
4/20/2019 Spark Page
What is knowable has expanded...in ways that may be
invisible to most people.
Past research is put to the test of validity/invalidity
based on current practices and knowledge.
Research is evaluated based on precision of available
measures. It is evaluated based on explanatory
depth, value to decision making, and insights.
What may be analyzed covers "all" time--past, current,
and future. Past thinking may be studied and
validated/invalidated. Learning from the past may be
extended into the present and future.
"Computational thinking" is about...
efficient problem solving
based on a knowledge of "priors" and baselines
with clear and logic-based hypothesizing
recordable steps
generalizable steps (to other
contexts) available acquire-able data
statistical methods
computational efficiency, and
parsimonious (as simple and pared-down as
possible) modeling.
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