identifying the data scientist amongst stem educators [autosaved]

IDENTIFYING THE DATA SCIENTIST AMONGST STEM EDUCATORS:AN INTROSPECTIVE SURVEY OF WORK SKILLS

TANYA THAMES PORTER

CAPELLA UNIVERSITYSCHOOL OF BUSINESS AND TECHNOLOGYBUSINESS INTELLIGENCE SPECIALIZATIONMENTOR: DR. WILLIAM MCKIBBINCOMMITTEE MEMBERS: DR. JAVAD SEYED & DR. GREGORY MCLAUGHLIN

BUSINESS PROBLEMMATCHING ANALYTICAL TALENT WITH PROJECTS AND REQUIREMENTS IS A MAJOR TASK AND PROBLEM FOR BUSINESSES (HARRIS, MURPHY, & VAISMAN, 2013).

RESEARCH PURPOSEIS TO SURVEY HOW STEM EDUCATORS SELF-IDENTIFY WITH THE OPERATIONAL QUALIFICATIONS, CHARACTERISTICS, AND VALUES (SKILLS) OF DATA SCIENTISTS.

Introduction

RESEARCH QUESTIONSRQ1: 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?

Introduction

THEORETICAL FRAMEWORKThe 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

Multidisciplinary

Investigation

Pedagogy

Models and

Methods

Computing with Data

Theory

Tool Evaluatio

n

Introduction

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

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.

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.

RESEARCH DESIGNExploratory quantitative descriptive survey design

• Self identification of data science, analytical, & 21st century skills SAMPLE

NC high school qualified STEM educators

INSTRUMENTATION/MEASUREAdapted survey from Analyzing the Analyst from Harris, Murphy, & Vaisman, 2012Cluster analysis/Nonnegative Matrix Factorization in R

Methodology

• Distributed survey link to 300 STEM educators of 6 high schools

• Qualtrics collected and stored data results online# Descriptive PropositionsP1 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

Methodology

DATA ANALYSIS

Cluster AnalysisSkills

Results

STATISTICAL RESULTS OF RMain Skill Group – k = 5; No Machine Learning skills; agglomerative coefficient = 0.32; Highly stressed Business SkillsData Science Cluster Type –k = 4; agglomerative coefficient = 0.42; Most aligned in Data Creatives21st Century Skills – 66 dissimilarities; agglomerative coefficient = 0.68; Rarely collaborate with teammates or have entrepreneurial skills

NC STEM Educator's Skills and Self-ID Top Factors

Data BusinessPerson Data Creative Data Developer Data Researcher

Sta

tistic

sM

ath

Prog

ram

ming

Bus

ines

sM

achine

Lea

rning

Results

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

Conclusion

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

Conclusion

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

Conclusion

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

Conclusion