Industry Informed Immersive Graduate Training in Data Science

The second in a series of interactive webinars hosted by the Technology Association of Oregon and the University of Oregon to explore problems organizations face that can be addressed through training the next generation of scientists using their successful program model. UO is interested in industry input as they develop broad-based data science MS programs.

In addition to the small cohort, experiential, internship based track, UO is poised to deliver Master’s level data science education in a variety of its domain area strengths including environmental science, communication and journalism, business, computer and information science, prevention science, and economics.

Participants interested in partnering with UO to help shape curriculum and hiring interns as they develop a new track in biological data science are encouraged to participate.

FIND THE CONVERSATION HERE

Event summary provided by Stacey Wagner of UofO:

Attendees were asked to discuss the following prompts in four break out rooms, facilitated by UO staff. Some groups only got to question 1 or did not have an opportunity to finalize their top choices. All groups provided valuable insights!

What tasks do we want a data scientist to be able to do?

What skills are needed for the data scientist(s) to be successful in completing the tasks?

There were several common threads discussed in addition to insights that were specific to one room. Common points have been grouped in buckets (although there is overlap):

• A. Communication and data visualization
• B. Emphasis on analysis that integrates understanding the nature and context of the data
• C. Data wrangling, management,and integration
• D. Inference/statistics/machine learning/modeling
• E. DevOps/software engineering

Interestingly, data management and communication were also top priorities in the survey.

Room 1:

Tasks

• Data Wrangling (C)
• Understanding and implementing basic statistical models, analysis (D)
• Image Analysis
• QA (D)
• Communication (A)

Skills

• Coding (E)
• Databasing(C)
• Write robust code (E)
• Machine learning (logistic regression) (D)
• Interpretation of results (B)
• Data visualization (A)

Other tasks and skills that came up:

• Research and choose methods
• Data engineering
• Natural language processing
• Web dashboard development
• Graphic design

Room 2:

Tasks

• Make sense of the enormous amount of data (B)
• Understand what has happened from start to finish (B)
• Glean meaning (B)
• Communicate the results in a meaningful way to different stakeholders (A)
• How do you use data to use new teaching practices and engagement practices?
• How do we use data to create better practices for the university
• Community understand how data drives so much of one's life (B)

Skills

• Understand statistics (D)
• Use scientific or layperson or business terms
• Have a general understanding of how data science impacts everyday life (B)
• How data and logistics impact everyone’s life (B)
• Critical thinking skills
• Develop one's own takeaways from the data and process (B)
• Communicate the overall impact of data-driven decision making (A)

Room 3:

Tasks

• Data Visualization (A)
• Communicate with stakeholders to determine goals and experimental design (A)
• Evaluate Success of programs and actions (B)
• Informs what questions should be asked (B)
• Design, build, test, and maintain machine learning pipelines (D)

Skills

• Cloud computing (E)
• Pipeline development (E)
• Statistics (D)
• Machine Learning (D)
• Natural Language processing (D)
• Application programming interface (API) utilization (E)
• Understand build vs buy (invest in a outside solution or create your own)
• Data integration (C)

Room 4:

• Develop large database solutions (C)
• Predication and modeling (D)
• Real world significance results (regardless of stats significance) (B)
• Communicate findings for impact (A)
• Data analysis implications and societal context (B)
• Familiarity with policy and privacy issues (B)
• Collect data from disparate sources (C)
• Think creatively and critically about the data (B)
• Programming and stats (E and D)
• Core math concepts (D)
• Optimization approaches (E)
• Domain data literacy (B)
• Curiosity
• Data visualization (A)
• Reproducibility and collaboration

FEATURING

Stacey Wagner
Director of Life Science Tracks, Graduate Internship Program
Knight Campus for Accelerating Scientific Impact - University of Oregon

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