The Analytics and Data Science Landscape
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This document discusses the evolving landscape of analytics and data science, emphasizing the increasing demand for skilled professionals and the need for interdisciplinary collaboration in education. It highlights the importance of data in making informed decisions across various sectors, including government, healthcare, and business, and introduces initiatives aimed at addressing workforce challenges in data science. Additionally, it outlines the curriculum and guiding principles of a data science program focused on societal benefits and practical applications.
![• Data/code as first
class citizen – Part
of promotion and
tenure
http://www.ncbi.nlm.nih.gov/pubmed/26207759
Why FAIR?
[Adapted from Carole Goble]
Only 12% of data
from research is
preserved](https://image.slidesharecdn.com/tax1-201116132524/85/The-Analytics-and-Data-Science-Landscape-21-320.jpg)
![The 4+1 Model
The model is based
on the core insight
that all definitions of
data science assume
a pipeline and that
this pipeline forms a
parallel process
[From Raf Alvarado]](https://image.slidesharecdn.com/tax1-201116132524/85/The-Analytics-and-Data-Science-Landscape-24-320.jpg)
![Our Representation of Data Science
The 4+1 Model
• Value – assuring
societal benefit
• Design -
Communication of the
value of data
• Systems – the means
to communicate and
convey benefit
• Analytics – models
and methods
• Practice – where
everything happens
[From Raf Alvarado]](https://image.slidesharecdn.com/tax1-201116132524/85/The-Analytics-and-Data-Science-Landscape-25-320.jpg)
![The 4+1 Model Interplay
[From Raf Alvarado]
• Value + Design = Openness,
responsibility
• Value + Analytics = Human
centered AI, algorithmic bias
• Value + Systems =
sustainability, access,
environmental impact
• Design + Analytics = literate
programming, visualization
• Design + Systems =
dashboards, engineering
design
• Analytics + Systems = ML
engineering](https://image.slidesharecdn.com/tax1-201116132524/85/The-Analytics-and-Data-Science-Landscape-26-320.jpg)
The Analytics and Data Science Landscape
- 1. The Analytics & Data Science Landscape Philip E. Bourne peb6a@virginia.edu Analytics Challenges in Modern Tax Administration November 16, 2020
- 2. Disclaimer • I pay taxes but typically do not get it right • My PhD is actually in physical chemistry • I did work for the NIH for 3 years
- 3. On a Positive Note • I have been working with “big” data for many years • As Dean I am very interested in mapping the capabilities of our students to the needs of the workplace • As a researcher I am concerned that the research our school undertakes is for societal benefit
- 4. This moment in time….
- 5. What of the future? One view is the 6D’s 5
- 6. Digitization Deception Disruption Demonetization Dematerialization Democratization Time Volume,Velocity,Variety Digital camera invented by Kodak but shelved Megapixels & quality improve slowly; Kodak slow to react Film market collapses; Kodak goes bankrupt Phones replace cameras Instagram, Flickr become the value proposition Digital media becomes bona fide form of communication From a presentation to the Advisory Board to the NIH Director Example - photography 6
- 7. Everything is Digital Data to be Analyzed… Play the data science game – pick an object/subject and you will immediately see a reason why data science is important …
- 9. If I were a tie maker I would be undertaking a data science analysis right now… Large collection of random images with metadata before and during the pandemic Who is still wearing ties? • Age • Profession • Ethnicity • Socioeconomic status • Location • ….. Causality – Does the pandemic represent a shift in tie wearing? If so by how much? Prediction – What will be the market post COVID?
- 11. A Paradigm Shift Reflected in the Workforce Increased Demand over the Past Five Years 74% Artificial Intelligence specialists Top industries hiring this talent: Computer software, internet, information technology and services, higher education, consumer electronics 37% Data Scientist Top industries hiring this talent: Information technology and services, computer software, internet, financial services, higher education 33% Data Engineer Top industries hiring this talent: Information technology and services, internet, computer software, financial services, hospital and healthcare
- 12. How is Academia Responding?
- 13. Every University has Some Initiative
- 14. Workforce Demand Outweighs Supply – A Problem for the IRS?
- 15. The Rising Demand for Data Scientists *for graduates seeking employment 100% 100% 100% 98% 97% UVA School of Data Science Graduate Job Placement 2019 2018 2017 2016 2015 * Roles Machine Learning Engineer, Director of Data Science, Deep Learning Research Scientist, Senior Data Analyst, Data Science Developer, Consultant, Product Data Analyst, Financial Engineer, Engagement Manager & more Industries ● Finance ● Government ● Healthcare & Medicine ● Professional Sports ● Commerce ● Media ● Higher Ed ● Technology
- 16. Recent Poll of Machine Learning PhD Students
- 17. A New School for a New Century A School Without Walls Mission To be a national and international leader in responsible data science emphasizing interdisciplinary collaboration which results in furthering discovery, sharing knowledge, and societal benefit
- 18. Our Working Definition • Use of the ever increasing amount of open, complex, diverse digital data frequently in ubiquitous cloud environments • Finding ways to ask and then answer relevant questions by combining such diverse data sets • Arriving at statistically significant conclusions not otherwise obtainable • Sharing such findings in a useful way • Translating such findings into actions that improve the human condition
- 19. Use Case – Data Integration Researcher and Assistant Professor of Medicine Dr. Thomas Hartka, also a current online Masters in Data Science student, is combining two disparate data sets—electronic health records and DMV crash data—to save lives after motor vehicle crashes. “I enrolled in the MSDS program to expand my research on automotive safety. I have already used techniques from classes in my work. I hope to expand my research to real-time analytics to improve emergency room care.” — Dr. Thomas Hartka, UVA School of Medicine
- 20. Guiding Principles • Excellence • Integrity • Diversity • Openness and transparency • FAIR data - the ability to Find, Access, Interoperate and Reuse data • Innovation • For the social good
- 21. • Data/code as first class citizen – Part of promotion and tenure http://www.ncbi.nlm.nih.gov/pubmed/26207759 Why FAIR? [Adapted from Carole Goble] Only 12% of data from research is preserved
- 22. Infrastructure Commons - Platform Stack https://datascience.nih.gov/commons Compute Platform: Cloud or HPC Services: APIs, Containers, Indexing, Software: Services & Tools scientific analysis tools/workflows Data “Reference” Data Sets User defined data DigitalObjectCompliance App store/User Interface Why not more like AirBnB? https://doi.org/10.1371/journal.pbio.2001818
- 23. How and What are We Teaching …
- 24. The 4+1 Model The model is based on the core insight that all definitions of data science assume a pipeline and that this pipeline forms a parallel process [From Raf Alvarado]
- 25. Our Representation of Data Science The 4+1 Model • Value – assuring societal benefit • Design - Communication of the value of data • Systems – the means to communicate and convey benefit • Analytics – models and methods • Practice – where everything happens [From Raf Alvarado]
- 26. The 4+1 Model Interplay [From Raf Alvarado] • Value + Design = Openness, responsibility • Value + Analytics = Human centered AI, algorithmic bias • Value + Systems = sustainability, access, environmental impact • Design + Analytics = literate programming, visualization • Design + Systems = dashboards, engineering design • Analytics + Systems = ML engineering
- 27. The 4+1 Model 27 Integration Practice of DS, Capstones Analytics Linear Models, Data Mining, Bayesian ML, Deep Learning, Text Analytics, Foundations of CS Systems Programming and Systems, Big Data Analytics Value Ethics of Big Data Design Practice of DS, Visualization We strive to build a curriculum that aligns with our model
- 28. Distinctive Features 28 Foundational topics in analytics from linear models to data mining and machine learning An integrated curriculum developed in consultation with practicing data scientists that incorporates a challenging capstone experience Applications and data drawn from different disciplines, e.g., science, business, and health Instruction in the best practices in the management and conduct of data science projects Computational methods built on the latest techniques in R and Python Required course in data ethics Emphasis on team science — data science is a team sport
- 29. Analytics and Machine Learning 29 STAT 6021 Linear Models - Multiple linear regression, logistic regression. (R) SYS 6018 Data Mining - Tree-based methods, kernel methods, unsupervised learning. Uses An Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani. (R) SYS 6014 Bayesian Machine Learning - Methods to handle uncertainty and to apply per variable weight distributions (as opposed to a single optimal value). (Python) SYS 6016 Machine Learning - Focuses on neural networks, including deep learning, convolutional neural networks, recurrent neural networks, and autoencoders. (Python)
- 30. Computer Science 30 CS 5010 Programming and Systems for Data Science -- Python, Pandas, data analysis at scale and in context, some development practices. CS 5021 Foundations of CS -- Data structures, algorithms, complexity, relational and noSQL databases; "CS in a box."
- 31. Data Ethics 31 Virtuous cycle between {computer science, statistics, applied mathematics} and the humanities Exemplified with use cases It’s a culture not a tick of the box Computer Science Statistics Applied Mathematics Humanities
- 32. Practice and Application of Data Science 32 DS 6001 and 6003 focus on data design Flow of data between Human and Machine domains H → M: Establishing data so that it can be analyzed M → H: Presenting results of analysis to the world 6001: Data engineering pipeline -- acquiring, cleaning, exploring 6003: Data product development -- presenting, visualizing, app dev
- 33. Electives 33 CS 6160 Theory of Computation CS 6444 Parallel Computing CS 6501 Text Mining CS 6750 Database Systems DS 5001 Exploratory Text Analytics DS 6559 Biomedical Cloud Computing Seminar SARC 5400 Data Visualization STAT 6250 Longitudinal Data Analysis STAT 6260 Categorical Data Analysis SYS 6023 Cognitive Systems Engineering SYS 6050 Risk Analysis SYS 6582 Reinforcement Learning SYS 7001 System and Decision Sciences
- 34. Capstones 34 A parallel and culminating experience that focuses on a real world data science problem Emphasizes problem definition and scoping Employ project management Involves developing, evaluating, and creating a data product for a client Requires presentations, a proposal and a published paper (IEEE) Teams of students work on separate projects under guidance of an advisor
- 35. Furthering Discovery to Build a Better World RESEARCH Cybersecurity Detecting broad-spectrum cyber threats almost immediately after they are launched through a $7.6 million Defense Advanced Research Projects Agency (DARPA) grant. Environment Using NASA data collected aboard the International Space Station to examine climate change in the Shenandoah National Forest and beyond, and find solutions Health & Medicine Securing high-performance computing equipment and personnel to allow collaboration across the university on brain science research like Autism, Alzheimer’s, mental health disorders, traumatic brain injuries and more. Business Discovering what makes a job interview successful for the candidate and the recruiter, and how to mitigate bias in the recruiting process Democracy Investigating how terrorist groups recruit women through propaganda and examining risk and threat assessment for extremist violence perpetrated by women. Education Helping economically disadvantaged, underrepresented populations pursue tailored educational workforce pathways that have a higher probability of leading them to success.
- 36. Applying Data Science Across Industries “To tackle challenges in science and medicine.” — Elizabeth Driskell, MSDS ‘20 “To inform public policy and government.” — Bradley Katcher, MSDS ‘20 “I want to use data science to find a new way of thinking.” — Alex Gromadzki, MSDS ‘21 “I want to use data science to solve complex business problems.” — Ruslan Askerov, MSDS ‘21 “To address poverty and income inequality.” — Arti Patel, MSDS ‘20
- 37. Growing the School M.S. IN DATA SCIENCE Residential & Online 2020 2020-2023 UNDERGRADUATE COURSES increase to 18 courses per AY 2021 PH.D. PROGRAM 2023 UNDERGRADUATE MAJOR Building occupied Team Size (FTEs) 5 40 60 80 120 Exec. Ed.
- 38. SDS and IRS - Actions • Workforce pipeline - awareness • Continuing Ed opportunities • Provision of synthetic data • Funded and collaborative research • Faculty, Capstone, Presidential Fellowship, PhD Internships • IRS Admits – MSDS, PhD • Join the corporate commons • ….
- 40. SDS Faculty Research Data Science Faculty member or affiliated faculty Website Research Interests Nada Basit https://engineering.virginia.edu/facul ty/nada-basit Machine Learning, Bioinformatics, Data Mining, Pattern Recognition Phil Bourne https://engineering.virginia.edu/facul ty/philip-e-bourne Multiscale Modeling Using Data Science Techniques Early Stage Drug Discovery and Drug Repurposing Early Stage Drug Methods and Tools for Macromolecular Don Brown https://engineering.virginia.edu/facul ty/donald-e-brown-phd Data Fusion, Knowledge Discovery, and Simulation Optimization Sallie Keller https://biocomplexity.virginia.edu/sal lie-keller social and decision informatics, statistical underpinnings of data science, and data access and confidentiality. Daniel Mietchen https://tools.wmflabs.org/scholia/aut hor/Q20895785 Computational Biology, Biodiversity integrating research workflows with the World Wide Web through open licensing, open standards, and open collaboration via Rafael Avarado http://transducer.ontoligent.com/ Cultural Analytics and Machine Learning, Digital Humanities, Text Analysis Heman Shakeri https://www.hemanshakeri.com/ structure and function of interconnected networks, often expressed via graphs that comprise a set of nodes and a set of connections between them. Jonathan Kropko https://facultydirectory.virginia.edu/f aculty/jk8sd methods to examine historical data, to test theories of voting in U.S. presidential elections, and to handle nonresponse in surveys. Michael Porter https://engineering.virginia.edu/facul ty/michael-d-porter event prediction, pattern and anomaly detection, and data linkage - applications for Criminology, Transportation, Terrorism, Defense, Security, Forensics, Business Mohammad Fallahi-Sichani new hire designing and building new experimental and computational tools to enable the analysis, interpretation and rational modulation of multi-scale processes that Jack Van Horn https://scholar.google.com/citations? user=i9bGqbgAAAAJ&hl=en Psychology and Data Science, Cognitive Neuroscience Pete Alonzi https://github.com/alonzi Vicente Ordonez https://engineering.virginia.edu/facul ty/vicente-ordonez-roman Computer Vision, Natural Language Processing and Machine Learning Tim Clark https://scholar.google.com/citations? user=k-iwlCUAAAAJ&hl=en next generation approaches for biomedical communications and data integration, including semantically integrated data repositories, claims and Gerard Learmonth https://www.researchgate.net/profil e/Gerard_Learmonth Generation and testing of pseudorandom number generators; Abstract database design; Strategic applications of information systems and technology Hongning Wang http://www.cs.virginia.edu/~hw5x/ data mining, machine learning, and information retrieval, with a special emphasis on computational user behavior modelin Stephen Adams http://www.nsfcvdi.org/wordpress/c vdi_personnel/steven-adams-ph-d/ Adaptive Decision Systems Lab at UVA and his research is applied to several domains including activity recognition, prognostics and health management for manufacturing Aidong Zhang https://engineering.virginia.edu/facul ty/aidong-zhang ML, Data mining, bioinformatics Jundong Li http://people.virginia.edu/~jl6qk/ Data Mining, Machine Learning, Social Computing, and Deep Learning Brian Wright https://www.linkedin.com/in/brian- wright-ph-d-90063027/