This document summarizes a research study that surveyed STEM educators to see how they self-identify with the skills and characteristics of data scientists. The study found that STEM educators fell into four main data scientist cluster types and many lacked skills in machine learning and advanced mathematics. It was also found that minority STEM educators lacked important data science skills, which correlates to low minority participation in STEM fields. The study implications are that professional development is needed to improve data science skills among STEM educators.
In February, 2015, Jeanne Century and Amy Cassata presented a one hour webinar to over 150 education researchers, evaluators, faculty, and other individuals who are part of the MSPnet (Math and Science Partnership Network) online community. The MSPnet serves recipients of and participants in NSF’s “STEM + Computing Partnerships” and “Math and Science Partnerships” research programs. The webinar provided a high-level overview of the key issues related to implementation measurement, including definitions, theory, and study design and measurement approaches at different stages of research.
In February, 2015, Jeanne Century and Amy Cassata presented a one hour webinar to over 150 education researchers, evaluators, faculty, and other individuals who are part of the MSPnet (Math and Science Partnership Network) online community. The MSPnet serves recipients of and participants in NSF’s “STEM + Computing Partnerships” and “Math and Science Partnerships” research programs. The webinar provided a high-level overview of the key issues related to implementation measurement, including definitions, theory, and study design and measurement approaches at different stages of research.
The Business Value of Reinforcement Learning and Causal InferenceHanan Shteingart
Israeli Reinforcement Learning Day 2021
A talk by Hanan Shteingart, VIANAI about what is the business value of causal inference and reinforcement learning.
Data science training presentation for high-quality education and training in...testingggg0101
https://nareshit.in/data-science-training/
We are a Data Science Training Institute, dedicated to providing comprehensive education and practical skills in the dynamic field of Data Science.
Here, we believe in empowering individuals with the knowledge and expertise to excel in the rapidly evolving world of data-driven decision-making.
Our Data Science Training Institute offers a wide range of courses, workshops, and hands-on projects designed to cater to learners of all levels, from beginners to advanced professionals.
Opening/Framing Comments: John Behrens, Vice President, Center for Digital Data, Analytics, & Adaptive Learning Pearson
Discussion of how the field of educational measurement is changing; how long held assumptions may no longer be taken for granted and that new terminology and language are coming into the.
Panel 1: Beyond the Construct: New Forms of Measurement
This panel presents new views of what assessment can be and new species of big data that push our understanding for what can be used in evidentiary arguments.
Marcia Linn, Lydia Liu from UC Berkeley and ETS discuss continuous assessment of science and new kinds of constructs that relate to collaboration and student reasoning.
John Byrnes from SRI International discusses text and other semi-structured data sources and different methods of analysis.
Kristin Dicerbo from Pearson discusses hidden assessments and the different student interactions and events that can be used in inferential processes.
Panel 2: The Test is Just the Beginning: Assessments Meet Systems Context
This panel looks at how assessments are not the end game, but often the first step in larger big-data practices at districts/state/national levels.
Gerald Tindal from the University of Oregon discusses State data systems and special education, including curriculum-based measurement across geographic settings.
Jack Buckley Commissioner of the National Center for Educational Statistics discussing national datasets where tests and other data connect.
Lindsay Page, Will Marinell from the Strategic Data Project at Harvard discussing state and district datasets used for evaluating teachers, colleges of education, and student progress.
Panel 3: Connecting the Dots: Research Agendas to Integrate Different Worlds
This panel will look at how research organizations are viewing the connections between the perspectives presented in Panels 1 and 2; what is known, what is still yet to be discovered in order to achieve the promised of big connected data in education.
Andrea Conklin Bueschel Program Director at the Spencer Foundation
Ed Dieterle Senior Program Officer at the Bill and Melinda Gates Foundation
Edith Gummer Program Manager at National Science Foundation
How data fellows open doors to data careers:
This talk will draw on findings from a Data Fellowship programme that was established in 2013 through the University of Manchester’s Q-Step programme. The data fellows are drawn from social science undergraduate degrees and since starting with 19 in 2014 we have now placed 330 student into around 60 organisations to do data-led research projects. The results have been published in articles and a book and Jackie will provide insight into these placements and talk about how the programme is opening up opportunities for social science graduates to enter data and statistical careers. She has developed a ‘research and analytical skills’ and ‘professional skills’ framework based on British Academy and LinkedIn and McKinsey reports. She is currently talking to employers about their ‘data skills’ needs and she is hoping her current research will result in a data skills framework that is more inclusive and not focused predominantly on STEM subjects. Her aim is to contribute to creating a more diverse talent pipeline into data careers.
BbWorld 2013 - Learning Analytics: A Journey to Implementationekunnen
This session will focus on the strategy used for the selection, implementation, and deployment of Blackboard Analytics for Blackboard LearnTM at Grand Rapids Community College. Participants in this session will learn first hand from the
CIO, Director of Enterprise Applications, and the Director of Distance Learning and Instructional Technologies about the need for analytics, the power of a data warehouse, the implementation of the system, and finally the opportunities that data provides as the college focuses on improving student success, completion, return on investment, and leveraging data to make strategic campus decisions.
Leveraging demographic data along with campus organizational structures in the student information system alongside student and faculty activities in Blackboard presents opportunities never before possible.
The Business Value of Reinforcement Learning and Causal InferenceHanan Shteingart
Israeli Reinforcement Learning Day 2021
A talk by Hanan Shteingart, VIANAI about what is the business value of causal inference and reinforcement learning.
Data science training presentation for high-quality education and training in...testingggg0101
https://nareshit.in/data-science-training/
We are a Data Science Training Institute, dedicated to providing comprehensive education and practical skills in the dynamic field of Data Science.
Here, we believe in empowering individuals with the knowledge and expertise to excel in the rapidly evolving world of data-driven decision-making.
Our Data Science Training Institute offers a wide range of courses, workshops, and hands-on projects designed to cater to learners of all levels, from beginners to advanced professionals.
Opening/Framing Comments: John Behrens, Vice President, Center for Digital Data, Analytics, & Adaptive Learning Pearson
Discussion of how the field of educational measurement is changing; how long held assumptions may no longer be taken for granted and that new terminology and language are coming into the.
Panel 1: Beyond the Construct: New Forms of Measurement
This panel presents new views of what assessment can be and new species of big data that push our understanding for what can be used in evidentiary arguments.
Marcia Linn, Lydia Liu from UC Berkeley and ETS discuss continuous assessment of science and new kinds of constructs that relate to collaboration and student reasoning.
John Byrnes from SRI International discusses text and other semi-structured data sources and different methods of analysis.
Kristin Dicerbo from Pearson discusses hidden assessments and the different student interactions and events that can be used in inferential processes.
Panel 2: The Test is Just the Beginning: Assessments Meet Systems Context
This panel looks at how assessments are not the end game, but often the first step in larger big-data practices at districts/state/national levels.
Gerald Tindal from the University of Oregon discusses State data systems and special education, including curriculum-based measurement across geographic settings.
Jack Buckley Commissioner of the National Center for Educational Statistics discussing national datasets where tests and other data connect.
Lindsay Page, Will Marinell from the Strategic Data Project at Harvard discussing state and district datasets used for evaluating teachers, colleges of education, and student progress.
Panel 3: Connecting the Dots: Research Agendas to Integrate Different Worlds
This panel will look at how research organizations are viewing the connections between the perspectives presented in Panels 1 and 2; what is known, what is still yet to be discovered in order to achieve the promised of big connected data in education.
Andrea Conklin Bueschel Program Director at the Spencer Foundation
Ed Dieterle Senior Program Officer at the Bill and Melinda Gates Foundation
Edith Gummer Program Manager at National Science Foundation
How data fellows open doors to data careers:
This talk will draw on findings from a Data Fellowship programme that was established in 2013 through the University of Manchester’s Q-Step programme. The data fellows are drawn from social science undergraduate degrees and since starting with 19 in 2014 we have now placed 330 student into around 60 organisations to do data-led research projects. The results have been published in articles and a book and Jackie will provide insight into these placements and talk about how the programme is opening up opportunities for social science graduates to enter data and statistical careers. She has developed a ‘research and analytical skills’ and ‘professional skills’ framework based on British Academy and LinkedIn and McKinsey reports. She is currently talking to employers about their ‘data skills’ needs and she is hoping her current research will result in a data skills framework that is more inclusive and not focused predominantly on STEM subjects. Her aim is to contribute to creating a more diverse talent pipeline into data careers.
BbWorld 2013 - Learning Analytics: A Journey to Implementationekunnen
This session will focus on the strategy used for the selection, implementation, and deployment of Blackboard Analytics for Blackboard LearnTM at Grand Rapids Community College. Participants in this session will learn first hand from the
CIO, Director of Enterprise Applications, and the Director of Distance Learning and Instructional Technologies about the need for analytics, the power of a data warehouse, the implementation of the system, and finally the opportunities that data provides as the college focuses on improving student success, completion, return on investment, and leveraging data to make strategic campus decisions.
Leveraging demographic data along with campus organizational structures in the student information system alongside student and faculty activities in Blackboard presents opportunities never before possible.
Analytics (as if learning mattered) - RIDE Symposium, University of London 10...Adam Cooper
These slides are from a presentaion by Adam Cooper, entitled "Analytics (as if learning mattered)" in the In Focus: Learner analytics and big data symposium, University of London, December 10th 2013
The recorded audio from the session is available at: https://soundcloud.com/cdelondon/analytics-as-if-learning
Related blog post at: http://blogs.cetis.ac.uk/adam/2013/10/31/policy-and-strategy-for-systemic-deployment-of-learning-analytics-barriers-and-potential-pitfalls/
Unveiling the Dynamics of Exploratory Data Analysis_ A Deep Dive into Data Sc...Assignment Help
The goal of data science, a multidisciplinary topic, is to extract valuable knowledge and insights from both organized and unstructured data using a variety of methods, algorithms, procedures, and systems. In order to evaluate, analyze, and visualize data in order to extract useful knowledge and information. It entails applying scientific methods, processes, and systems. Data Science is a disruptive force in the rapidly evolving field of technology innovation. It powers decision-making approaches and extracts valuable insights from large and varied information. Students find it difficult to navigate the complexities of Data Science projects as the need for data-driven solutions keeps growing. In order to have complete information about data science they connect with dissertation help Australia experts.
Certified Data Science Training in Chennai-MarchDataMites
Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data.
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Analytics: as if learning mattered
Presentation from 'In Focus: Learner analytics and big data', a CDE technology symposium held at Senate House on 10 December 2013. Conducted by Adam Cooper (Co-Director, Cetis)
Audio of the session and more details can be found at www.cde.london.ac.uk.
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unknown knowledge (patterns, concepts and relations) related with multiple factors from past database records of
specific students. Data mining is science and engineering study of extracting previously undiscovered patterns
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A Survey of Mathematics Education Technology Dissertation Scope and Quality ...
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