Stefanie Molin

Stefanie Molin

Data Scientist, Software Engineer at Bloomberg | Author of Hands-On Data Analysis with Pandas

Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of "Hands-On Data Analysis with Pandas," which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

All Sessions by Stefanie Molin

Data Morph: A Cautionary Tale of Summary Statistics

<span class="etn-schedule-location"> <span class="firstfocus">Deep Learning</span> <span class="secfocus"> Beginner</span> <p class="locationvirtlive">In-person conference</p> </span>

Statistics do not come intuitively to humans; they always try to find simple ways to describe complex things. Given a complex dataset, they may feel tempted to use simple summary statistics like the mean, median, or standard deviation to describe it. However, these numbers are not a replacement for visualizing the distribution. To illustrate this fact, researchers have generated many datasets that are very different visually, but share the same summary statistics. In this talk, I will discuss """"""""Data Morph"""""""" (https://github.com/stefmolin/data-morph), an open source package that builds on previous research from Autodesk (the """"""""Datasaurus Dozen"""""""" (https://damassets.autodesk.net/content/dam/autodesk/research/publications-assets/pdf/same-stats-different-graphs.pdf)) using simulated annealing to perturb an arbitrary input dataset into a variety of shapes, while preserving the mean, standard deviation, and correlation to multiple decimal points. I will showcase how it works, discuss the challenges faced during development, and explore the limitations of this approach.

Data Morph: A Cautionary Tale of Summary Statistics

<span class="etn-schedule-location"> <span class="firstfocus">Deep Learning</span> <span class="secfocus"> Beginner</span> <p class="locationvirtlive">In-person conference</p> </span>

Statistics do not come intuitively to humans; they always try to find simple ways to describe complex things. Given a complex dataset, they may feel tempted to use simple summary statistics like the mean, median, or standard deviation to describe it. However, these numbers are not a replacement for visualizing the distribution. To illustrate this fact, researchers have generated many datasets that are very different visually, but share the same summary statistics. In this talk, I will discuss """"""""Data Morph"""""""" (https://github.com/stefmolin/data-morph), an open source package that builds on previous research from Autodesk (the """"""""Datasaurus Dozen"""""""" (https://damassets.autodesk.net/content/dam/autodesk/research/publications-assets/pdf/same-stats-different-graphs.pdf)) using simulated annealing to perturb an arbitrary input dataset into a variety of shapes, while preserving the mean, standard deviation, and correlation to multiple decimal points. I will showcase how it works, discuss the challenges faced during development, and explore the limitations of this approach.

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