Addressing Open Challenges in Data Science Education Stephanie Hicks Assistant Professor, Biostatistics Johns Hopkins Bloomberg School of Public Health Faculty Member Johns Hopkins Data Science Lab @stephaniehicks

Teaching: Data Science Research: Genomics (analyzing what genes are expressed in individual cells) • R/Bioconductor user and developer (since 2009/2010) Other fun things about me: • Co-founded Baltimore • Creating a children’s book featuring women statisticians and data scientists ABOUT ME JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH

https://jhudatascience.org

The “OG”s ROGER BRIAN JEFF Joined in 2018 STEPHANIE Who are we?

Why data science? Data science is the number one rated job by Glassdoor and there are more than 350,000 new data science jobs expected by 2020.

https://analytics.ncsu.edu/?page_id=4184

So…. what is data science?

“We hold a broad view of data science – we see it as the science of extracting meaningful information from data.”

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What if we define data science based on what a data scientist does?

“Data science” defined by Michael Hochster

“Data science” defined by Michael Hochster

Data science is the science and design of 1. Actively creating a question to investigate a hypothesis with data 2. Connecting that question with the collection of appropriate data and the application of appropriate methods, algorithms, computational tools or languages in a data analysis 3. Communicating and making decisions based on new or already established knowledge derived from the data and data analysis Hicks and Peng (2019) arXiv (https://arxiv.org/abs/1903.07639) …taking this one step further (in production or not) (with or without user interaction) (weak or strong coders) (in particular domains or not) (One-way communication or feedback loop)

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Data science is the science and design of 1. Actively creating a question to investigate a hypothesis with data 2. Connecting that question with the collection of appropriate data and the application of appropriate methods, algorithms, computational tools or languages in a data analysis 3. Communicating and making decisions based on new or already established knowledge derived from the data and data analysis Hicks and Peng (2019) arXiv (https://arxiv.org/abs/1903.07639)

Rest of the talk 1. Lessons learned when teaching intro data science courses Put the problem first; teach with case studies with non-trivial solutions solving real-world challenges with data 2. Some open challenges in data science education - How to describe variation across data analyses? - How to evaluate quality of data analyses?

https://jhu-advdatasci.github.io/2018/ http://cs109.github.io/2014/ http://datasciencelabs.github.io/2016/ (Harvard University – CS – over 400 students online, in person – 25 TAs – Python) (Harvard SPH – Biostats -- 150 students – online, in person – 8 TAs – R) (Johns Hopkins SPH – 25 students – in person – PhD Biostats – 2 TAs – R)

•  Teach R software/tools needed for a complete data analysis •  Use git/GitHub for assignments to learn version control •  Teach collaborative practices with group projects •  Final Project: analyze dataset of choice & create website and 2 min screencast summarizing results •  Focus on key statistical concepts (and less math details) •  Minimize “traditional” slides/lectures, note-taking •  Maximize hands-on code in class using Rstudio & RMarkdown •  Use “mini assessments” & Google Polls to get live feedback •  Motivate concepts with real world data problems Transforming the Classroom to Teach Statistics and Data Science with Active Learning What is Active Learning? “Anything course-related that all students in a class session are called upon to do other than simply watching, listening and taking notes.” - Felder & Brent (2009) http://r4ds.had.co.nz/intro.html 1.  Data Science (Harvard CS 109) (http://cs109.github.io/2014/) 2.  Introduction to Data Science (HSPH BIO 260) (http://datasciencelabs.github.io) Using Active Learning to teach courses in data science and statistics Oct 1, 2015 Feb 3, 2015 Goal Develop a curriculum for an applied statistics and data science course using active learning techniques Course websites ggplot2 dplyr +dyr readr stringr lubridate broom h5r rvest jsonlite Stephanie Hicks Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health Contact information: @stephaniehicks [email protected] My poster presented at the Women in Statistics and Data Science Conference in Fall 2016

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Data Science in Academia? • Statistics was born directly from developing solutions to practical problems by data analysis problems • Galton, Ronald Fisher • Wild and Pfannkuch (1999) describe applied statistics as: • A department that embraces applied statistics defined above is a natural home for data science in academia “part of the information gathering and learning process which, in an ideal world, is undertaken to inform decisions and actions. With industry, medicine and many other sectors of society increasingly relying on data for decision making, statistics should be an integral part of the emerging information era.”

Got it, but what’s missing in current statistics curriculum?

What is missing in the current statistics curriculum? Wild and Pfannkuch (1999) complained that: “Large parts of the investigative process, such as problem analysis and measurement, have been largely abandoned by statisticians and statistics educators to the realm of the particular, perhaps to be developed separately within other disciplines.” They add that “[t]he arid, context-free landscape on which so many examples used in statistics teaching are built ensures that large numbers of students never even see, let alone engage in, statistical thinking.”

What is missing in the current statistics curriculum? Computing • Need more computing in the curriculum

What is missing in the current statistics curriculum? Computing, Connecting • Need more computing in the curriculum • Need to teach how to connect the subject matter question to appropriate dataset and analysis tools

What is missing in the current statistics curriculum? Computing, Connecting, Creating • Need more computing in the curriculum • Need to teach how to connect the subject matter question to appropriate dataset and analysis tools • Instead of being passive, teach students to be active and how create and formulate questions to investigate hypotheses with data

Bridging the gap in the classroom to teach introductory data science courses

Bridging the gap in the classroom to teach introductory data science courses • Educators need to be experienced themselves in creating, connecting and computing • Encourage applied statisticians experienced in creating, connecting, and computing to become involved in the development of courses • Encourage statistics departments to reach out to practicing data analysts, perhaps in other departments or from other disciplines, to collaborate in developing these courses

Principles of Teaching Data Science • Organize the course around a set of diverse case studies • Integrate computing into every aspect of the course • Teach abstraction, but minimize reliance on mathematical notation • Structure course activities to realistically mimic a data scientist’s experience • Demonstrate the importance of critical thinking / skepticism through examples

So you want to teach with case studies too but not sure where to start?

https://opencasestudies.github.io

https://jhu-advdatasci.github.io/2018/ http://cs109.github.io/2014/ http://datasciencelabs.github.io/2016/ (Harvard University – CS – over 400 students online, in person – 25 TAs – Python) (Harvard SPH – Biostats -- 150 students – online, in person – 5 TAs – R) (Johns Hopkins SPH – 25 students – in person – PhD Biostats – 2 TAs – R)

This is me literally all of fall 2018

Me in Sept-Dec 2018: “Students are actually learning how to analyze data with case studies!”

Me in Jan 2019: after grading final projects, realizing it’s not sufficient to just teach with case studies… Me: sad and struggling to put into words why I’m frustrated when evaluating data analyses

Me in Jan 2019: searching the web and literature for how to evaluate quality of data analyses (or even more simply describe differences or variation across data analyses)

Can we define a language to describe differences or variation across data analyses?

Data science is the science and design of 1. Actively creating a question to investigate a hypothesis with data 2. Connecting that question with the collection of appropriate data and the application of appropriate methods, algorithms, computational tools or languages in a data analysis 3. Communicating and making decisions based on new or already established knowledge derived from the data and data analysis Hicks and Peng (2019) arXiv (https://arxiv.org/abs/1903.07639)

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What are the elements of a data analysis?

Elements of data analysis

Principles of data analysis

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What can the elements and principles be used for?

How to select informative elements?

How to evaluate quality of a data analysis? - Success? - Validity? - Honesty?

Feel free to send comments/questions: Twitter: @stephaniehicks Email: [email protected] #rladies Thank you! Normal distribution Weibull distribution Poisson distribution