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Data Science Workshops

The Kansas Data Science Consortium (KDSC) was tasked by the National Science Foundation to create data-science based workforce development opportunities. The KDSC is proud to offer a series of workshops to students and professionals that are career relevant and cutting edge.

Contact us

Is your organization looking for a specific training or workshop? Contact us at kansasdata@ku.edu!

Previous Workshops

No Really, What is Data Ethics? 

Hosted by Ramón Alvarado of the University of Oregon

Roughly a decade ago Floridi and Taddeo (2016) defined data ethics as a field that draws insights from the ethics of data (e.g., gathering, curation, storage), the ethics of algorithms (the use, deployment, and design of computational technologies) and the ethics of practice (oaths, mission statements, best- practices, etc.). A more sophisticated overview of such field could also easily identified that, despite its repeated omission from the literature, business ethics is additionally one of the most insightful sources of careful consideration and precedent for the field (De George, 2009). Underlying any and all of these sources is of course an exploration of profound moral dilemmas and frameworks which— for better or for worse— strongly hinge on utilitarian values. For the past decade, however, the strict focus of data ethics on harm mitigation related to transparency, bias, fairness, compliance, and regulation have exhausted its program without much left to show for it other than tired narratives that have lead to either ethics washing or ethics bashing (Bietti, 2020). In this talk we will explore what it means for a data ethics program to go beyond this paradigm of mitigation and start asking questions from a philosophical perspective in which data, databases, and data technologies inevitably drive the most central aspects of our contemporary civilization.  

View Recording - Data Ethics Webinar

The KDSC hosted a two part time series workshop featuring Joonha Park (KU Math Department) and Trambak Banerjee (KU Business School).

Accessing Google CoLab

  1. Each participant must have access to Google Colab (https://colab.research.google.com/). Colab is free and they only need a Gmail ID for this.
  2. Participants with a `.edu' email id (such as KU students, staff and faculty) should get the pro version of Colab (https://colab.research.google.com/signup), which is also free.

Python Workshop File

KDSC hosted a weather data activity as part of the Family STEM night hosted by ARISE and the KU Natural History Museum. Follow the link below to review the slides and preview the activity! This activity can also be found in the KDSC Online Repository.

 

Find the Tornado Activity

The Introduction to Tableau Workshop began with an overview of best practices for data visualization and ended with two hands-on tutorials using real world data!

Tableau Workshop - Materials

 

The Introduction to ArcGIS workshop provided a step-by-step walkthrough of how to use basic features as well as an applied tutorial using local transit data.

ArcGIS Workshop - Materials

 

Topics included a gentle introduction to programming in R and a refresher on probability theory, research design, and measurement. Ideal for incoming graduate students preparing for their first statistical methods course.

S4 Bootcamp - Recording

S4 Bootcamp - Materials

The Intro to R Workshop Series provided an overview of three basic skillsets: Wrangling, Visualizations, and Statistical Analysis. Each workshop can be found below and in the KDSC Online Repository.

Intro & Wrangling:

Visualizations:

Statistical Analysis:

Download R Studio

Access KU Virtual Desktop

KU Community Data Labs hosted workshops to discuss agencies in the federal statistics system. Each session provided an overview of the data and participants worked with these datasets in Stata. Each workshop can be found below and in the KDSC Online Repository.

Bureau of Labor Statistics:

Bureau of Economic Analysis:

National Center for Education Statistics: 

National Center for Health Statistics:

Census Bureau:

Dr. Chaspari's presentation on "Deconstructing and Mitigating Socio-Demographic Bias in AI Algorithms for Mental Health" for the Data Bias and Fairness Speaker Series. In this lecture, Dr. Chaspari spoke about AI algorithms and their relation to mental health outcomes. She relayed useful lessons regarding data science principles in research and machine learning. 

 

Bias in AI Algorithms for Mental Health - Recording

Xuhui Zhou's presentation on "Safety, Ethics, and Biases in AI and NLP Systems" for the Data Bias and Fairness Speaker Series. In this lecture, Xuhui spoke about the biases in AI algorithms and NLP model training. He relayed useful lessons regarding data science principles in AI, machine learning, and data collection.

 

Safety, Ethics, and Biases in AI and NLP Systems - Recording

Dr. Kevin Bowyer's presentation on the "Criminality from Face Illusion." This lecture analyzes how published results on this "criminality from face" problem can give the illusion that it can be done. This discusses that any positive results on criminality from face can only be an illusion, and that belief in this illusion is dangerous.

 

"Criminality From Face" Illusion - Recording

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