Data Science Matrix
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Concepts for Data Acumen*Core Elements for DS*On ramps for data producers/analystsOn ramps for data consumers/citizensScaffoldsCurricular, Disciplinary and Research Touchpoints
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Mathematical foundations
Set theory and basic logic
Multivariate thinking via functions and graphical displays,
Basic probability theory and randomness,
Matrices and basic linear algebra,
Networks and graph theory
Optimization
Partial derivatives
Advanced linear algebra (i.e., properties of matrices, eigenvalues, decompositions),
“Big O” notation and analysis of algorithms
Numerical methods (e.g., approximation and interpolation)
bridge programs
calculus
peer tutors
Kahn academy?
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Computational foundations
Basic abstractions
Algorithmic thinking
Programming concepts
Data structures
Simulations
Data Camp
MOOCs
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Statistical foundations
Variability, uncertainty, sampling error, and inference
Multivariate thinking
Nonsampling error, design, experiments (e.g., A/B testing), biases, confounding, and causal
inference;
Exploratory data analysis
Statistical modeling and model assessment
Simulations and experiments
R tutorialsEcon
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Data management and curation
Data provenance
Data preparation, especially data cleansing and data transformation
Data management (of a variety of data types)
Record retention policies
Data subject privacy
Missing and conflicting data
Modern databases
LibrariesIRB, FERPA
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Data description and visualization
Data consistency checking
Exploratory data analysis
Grammar of graphics
Attractive and sound static and dynamic visualizations
Dashboards
GIS
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Data modeling and assessment
Machine learning
Multivariate modeling and supervised learning
Dimension reduction techniques and unsupervised learning
Deep learning
Model assessment and sensitivity analysis
Model interpretation (particularly for black box models)
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Workflow and reproducibility
Workflows and workflow systems
Reproducible analysis
Documentation and code standards
Source code (version) control systems
Collaboration
Project TIER
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Communication and teamwork
Ability to understand client needs
Clear and comprehensive reporting
Conflict resolution skills
Well-structured technical writing without jargon
Effective presentation skills
Group workGroup workGroup work
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Domain-specific considerationsContext
Judgment
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Ethical problem solving
Ethical precepts for data science and codes of conduct
Privacy and confidentiality
Responsible conduct of research
Ability to identify “junk” science
Ability to detect algorithmic bias
Liberal ArtsLiberal ArtsLiberal Arts
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* From NAS 'Data Science for Undergraduates: Opportunities and Options'
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http://nap.edu/25104
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