1 / 17

Mathematics in the Data Science Movement

Mathematics in the Data Science Movement. American Mathematical Society Workshop for Department Chairs and Department Leaders Baltimore, Maryland Tuesday, January 15, 2019 Gloria Marí Beffa University of Wisconsin-Madison Douglas Mupasiri University of Northern Iowa.

dmejia
Télécharger la présentation

Mathematics in the Data Science Movement

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Mathematics in the Data Science Movement American Mathematical Society Workshop for Department Chairs and Department Leaders Baltimore, Maryland Tuesday, January 15, 2019 Gloria MaríBeffa University of Wisconsin-Madison Douglas Mupasiri University of Northern Iowa

  2. Mathematics in the Data Science Movement The Outline of the Session • The case for Data Science - why data science is all the rage? • Who is participating? • Is/should math be involved in data science? • If math should be involved, what should its role be? What can the chair do to facilitate the role? • Some models to consider.

  3. The Case for Data Science • 2016 Federal Big Data Research and Development Strategic Plan identified as a strategic priority the need to address the rapidly increasing demand in the workforce for people with Big Data skill sets by providing training and formal education programs in data science. • Two reports by the National Academies of Sciences, Engineering and Medicine – Envisioning the Data Science Discipline: The undergraduate Perspective: Interim Report (2018)*; Data Science for Undergraduates: Opportunities and Options (2018)**- make a compelling case. • IBM predicts 2.7 million data science jobs by 2020, a 28% increase from 2017 levels (Columbus, L. 2017) • 69% of employers expect to give preference to candidates with data science and analytics skills in their organizations by 2021 (2017 Gallup poll for the Business Higher Education Forum).

  4. Group Task 1 • What do you think data science is? • Is your university considering developing a data science program or does it already have one? • Which departments should be/are participating in the program? • Is the math department involved? If so, what is its role? • Report out

  5. Group Task 2 Assuming math should participate/is participating in a data science program in your institution • What should/was the role of the department chair in facilitating the development of the program? • What is the nature of the program – major, minor, two-year degrees and certificates, other certificates? • If program is interdisciplinary – what other departments are/should be involved? • Potential challenges in developing and implementing a data science program - possible solutions or ways to deal with them? • Report out.

  6. Current Areas of Focus for Data Scientists–According to the National Academies Report** • Computing hardware and software platforms for data science • Data storage and access • Statistical modeling and machine learning • Data visualization • Business analysis

  7. Group Task 3 If you have a data science program, use your experience to inform the group discussion; if you don’t have one, think about what you might want to include if you had the opportunity to propose a program with respect to • Learning outcomes/goals – broadly speaking • Curriculum to achieve the learning outcomes – the role of math • Assessment of program • Report out

  8. Key concepts essential in developing data acumen – according to the National Academies report** • Mathematical Foundations • Computational Foundations • Statistical Foundations • Data management and curation • Data description and visualization • Data modeling and assessment • Workflow and reproducibility • Communication and teamwork • Domain-specific considerations • Ethical problem solving

  9. Group Task 4 Focus now only on the mathematical foundations essential to developing data acumen • What content should form the core of the mathematical foundations for a data science certificate, minor, and major? • What additional content should be included in data science programs requiring deeper mathematical underpinnings? • Report out.

  10. Mathematical Foundations • Set theory and basic logic • Multivariate thinking via functions and graphical displays • Basic probability and randomness • Matrices and basic linear algebra • Networks and graph theory • Optimization

  11. Programs requiring deeper understanding of mathematical underpinnings might also include • Partial derivatives (to understand interactions in a model), • Advanced linear algebra (i.e., Properties of matrices, eigenvalues, decompositions), • “Big O” notation and analysis of algorithms, • Numerical Methods (e.g. approximation and interpolation).

  12. Model Data Science Curricula According to the National Academies Report** an important consideration in a data science program is that “Students --- need to learn how to ensure that outcomes (obtained from data) are valid – extracting the right insights and having confidence that, start to finish, what one says is true, within some margins of error. Repeated exposure to the data science life cycle (i.e., posing a question; collecting, cleaning, and storing data; developing tools and algorithms; performing exploratory analysis and visualization; making inferences and predictions; making decisions; and communicating results) is needed to help hone the skills required to assess the data at hand, extract meaning from them, and communicate those finding to nonexperts. Students also need to consider the provenance of the data used.”

  13. Some Model Data Science Curricula • Bowling Green State University (Public Comprehensive) https://www.bgsu.edu/arts-and-sciences/mathematics-and-statistics/comast1/data-science-program-requirements.html • University of the Pacific (Private Comprehensive) https://www.pacific.edu/academics/schools-and-colleges/school-of-engineering-and-computer-science/academics-/graduate-programs/ms-in-data-science/curriculum.html

  14. Some Model Data Science Curricula • Bryant University (Private College) https://catalog.bryant.edu/undergraduate/collegeofbusiness/datascienceprogram/datasciencemajor/ • Macalester College (Private College)- Minor https://www.macalester.edu/mscs/wp-content/uploads/sites/51/2016/06/DataScienceFlyer.pdf

  15. Some Model Data Science Curricula • University of Wisconsin (Public R1 University) – online M.S. program https://datasciencedegree.wisconsin.edu/data-science-program/data-science-courses/ • Washington State University (Land Grant) • B.S. Data Analytics https://www.catalog.wsu.edu/Pullman/Academics/DegreeProgram/6863

  16. Some Model Data Science Curricula Iowa State University (Land Grand) –major, minor, and certificate programs, respectively. http://catalog.iastate.edu/collegeofliberalartsandsciences/datascience/#undergraduatemajortext http://catalog.iastate.edu/collegeofliberalartsandsciences/datascience/#minorundergraduatetext http://catalog.iastate.edu/collegeofliberalartsandsciences/datascience/#certificatetext

  17. References • Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report (2018) – National Academies Press https://www.nap.edu/catalog/24886/envisioning-the-data-science-discipline-the-undergraduate-perspective-interim-report • Data science for Undergraduates: Opportunities and Options (2018) –National Academies Press https://www.nap.edu/catalog/25104/data-science-for-undergraduates-opportunities-and-options

More Related