IDENTIFYING THE DATA SCIENTIST AMONGST STEM EDUCATORS [Autosaved]
1. IDENTIFYING THE DATA SCIENTIST
AMONGST STEM EDUCATORS:
AN INTROSPECTIVE SURVEY OF WORK
SKILLS
TANYA THAMES PORTER
CAPELLA UNIVERSITY
SCHOOL OF BUSINESS AND TECHNOLOGY
BUSINESS INTELLIGENCE SPECIALIZATION
MENTOR: DR. WILLIAM MCKIBBIN
COMMITTEE MEMBERS: DR. JAVAD SEYED & DR. GREGORY
MCLAUGHLIN
2. BUSINESS PROBLEM
MATCHING ANALYTICAL TALENT WITH PROJECTS AND
REQUIREMENTS IS A MAJOR TASK AND PROBLEM FOR BUSINESSES
(HARRIS, MURPHY, & VAISMAN, 2013).
RESEARCH PURPOSE
IS TO SURVEY HOW STEM EDUCATORS SELF-IDENTIFY WITH THE
OPERATIONAL QUALIFICATIONS, CHARACTERISTICS, AND VALUES
(SKILLS) OF DATA SCIENTISTS.
Introducti
3. RESEARCH QUESTIONS
RQ1: DO STEM EDUCATORS IDENTIFY WITH THE SKILLS,
QUALIFICATIONS, AND CHARACTERISTICS OF A DATA
SCIENTIST’S CLUSTER TYPE?
RQ2: IS THERE A DEFICIENT IN MINORITY STEM
EDUCATORS WITH DATA SCIENCE SKILLS?
RQ3: ARE INDIVIDUALS WITHIN THE CLUSTER TYPES OF
DATA DEVELOPER AND DATA RESEARCHER CONSIDERED
INNOVATIVE?
RQ4: WHAT IS THE RELATIONSHIP BETWEEN SKILLS GAP
AND DATA INNOVATION TOWARDS GLOBAL
COMPETIVENESS?
Introducti
4. THEORETICAL
FRAMEWORK
The focal point of this
research is on tool
evaluation, which
Cleveland (2001, p. 3)
describes as “surveys of
tools in use in practice,
surveys of perceived
needs for new tools, and
studies of the processes
for developing new
tools.”
Data
Science
Multidisciplin
ary
Investigation
Pedagogy
Models
and
Methods
Computin
g with
Data
Theory
Tool
Evaluation
Introducti
5. LITERATURE REVIEW OUTLINE
• What is Data Science?
• Effects of Metadata
• Investigating Data Scientist
• Self-Identification Theory
• Importance of STEM
• Influential Factors of Minority Gap in STEM
• NC STEM Programs
• STEM Educator’s Guidelines
6. CORE LITERATURE
• Harris, H. D., Murphy, S. P., & Vaisman, M. (2013). Analyzing
the Analysts: An Introspective Survey of Data Scientists and
Their Work. Sebastopol, CA: O'Reilly Media.
• Harris, J. G., & Craig, E. (2012). Developing analytical
leadership. Strategic HR Review, 11(1), 25-30.
doi:10.1108/14754391211186287.
• Kandel, S., Paepcke, A., Hellerstein, J. M., & Heer, J. (2012).
Enterprise data analysis and visualization: An interview study.
Visualization and Computer Graphics, IEEE Transactions on,
18(12), 2917-2926.
7. SEMINAL LITERATURE
• Cleveland, W. S. (2001). Data science: an action plan for
expanding the technical areas of the field of statistics.
International statistical review, 69(1), 21-26.
• Naur, P. (1968). 'Datalogy', the science of data and data
processes. In IFIP Congress (2), pp. 1383-1387.
• Naur, P. (1974). Concise Survey of Computer Methods.
8. RESEARCH DESIGN
Exploratory quantitative descriptive survey design
• Self identification of data science, analytical, & 21st century
skills
SAMPLE
NC high school qualified STEM educators
INSTRUMENTATION/MEASURE
Adapted survey from Analyzing the Analyst from Harris,
Murphy, & Vaisman, 2012
Cluster analysis/Nonnegative Matrix Factorization in R
Methodolo
9. • Distributed survey link to 300 STEM educators of 6 high
schools
• Qualtrics collected and stored data results online# Descriptive Propositions
P1 STEM educators are identified by the skills,
qualifications, and characteristics of a data scientist’s
cluster type.
P2 The deficient of data science skills is evident among
minority STEM educators.
P3 STEM educators categorized as Data Developers and
Data Researchers are innovators.
P4 Global competitiveness depends on the relationship
between skills gap and data innovation.
DATA COLLECTION
Methodolo
11. STATISTICAL
RESULTS OF R
Main Skill Group –
k = 5; No Machine Learning
skills; agglomerative coefficient
= 0.32; Highly stressed Business
Skills
Data Science Cluster Type –
k = 4; agglomerative coefficient
= 0.42; Most aligned in Data
Creatives
21st Century Skills –
66 dissimilarities; agglomerative
coefficient = 0.68; Rarely
collaborate with teammates or
have entrepreneurial skills
Result
12. SUMMARY OF FINDINGS
• Survey revealed NC STEM educators are Data Scientists
• Data Developers/Data Researchers/Data Creatives/Data Businesspeople
• Many STEM educators do not have a related degree within STEM
• Minority STEM educators lack data science skills which
correlates to low participation of minorities in STEM (Dossey, et
al., 1988; Spielhagen, 2010; Tatsuoka, et.al., 2004; Epstein &
Miller, 2011; Kim-O, 2011)
• Data Developers & Data Researchers possess innovative skills
embedded in 21st century skills and analytical skills
Conclusi
13. IMPLICATION OF RESULTS
• Non-field degreed STEM educators lack skills in advanced
mathematics, which is critical for analytical development
• Deficiency in Big Data Skills
• Programming languages, machine-learning ability, statistical
visualization
• Professional Development or further education is needed to
improve/develop data science skills in machine learning and
advanced mathematics
• Businesses should internally mentor, train, or collaborate skills
to develop a well-rounded data scientist team Conclusi
14. FUTURE RESEARCH RECOMMENDATION
• Operational definition of Data Scientist
• Research other industry populations – health, finance, STEM
elementary educators, etc.
• Minority Data Scientists in other industries to clarify specific
data science skills, analytical skills, or 21st century skills
• How advanced mathematics effects the development of Data
Scientist’s Big Data Skills
• Study data scientist among STEM educators in another state
Conclusi
15. RESEARCH LIMITATION
• Low response rate
• Limited self-identification of skills and titles
• Timing of the survey distribution during the year
• No incentives for survey completion
• Email addresses not up-to-date
Conclusi
