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Workshop 06/29/2022

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Best practices in data visualization and dashboard design are numerous and sometimes contradictory, but a straightforward method to apply design thinking to creating dashboards is effective and universally applicable. This session will cover the details of design thinking and how it can be applied to dashboard development to create impactful dashboards that meet user needs and provide valuable insights.

Image classification is a task in the Computer Vision domain that takes in an image as input and outputs a label for that image. Deep learning is the most effective modern method for modeling this task. In this interactive workshop, we will walkthrough a Jupyter Notebook which will overview how to perform multi-class image classification in Python using the PyTorch library. The intention is to give the audience a broad overview of this task of classification and inspire participants to explore the vast fields of visual recognition and computer vision at large.

Make answering ‘what if’ analysis questions a whole lot easier by learning about state-of-the-art, end-to-end applied frameworks for causal inference. We will cover:
1. Microsoft’s “Do Why” Package Causal Impact in Python – DoWhy | An end-to-end library for causal inference — DoWhy | An end-to-end library for causal inference documentation (
2. Bayesian Causal Impact in R
3. MLE Causal Impact in Python
4. Bonus: AA Testing, when to use and why it matters

We will apply these models in the context of understanding the impact of a marketing rewards campaign, as well as understand the impact from a product/feature upgrade
Hidden Markov Models (HMMs) are used to describe and analyze sequential data in a wide range of fields, including handwriting recognition, protein folding, and computational finance. In this workshop, we will cover the basics of how HMMs are defined, why we might want to use one, and how to implement an HMM in Python. This workshop might be of particular interest to attendees from May 25’s “Intro to Markov Chains and Bayesian Inference” session. Introductory background in probability, statistics, and linear algebra is assumed.

Event Program

June 29, 2022

8:00 AM - 9:00 AM
11:00 AM - 12:00 PM

Exploring Hidden Markov Models

Julia Christina Costacurta

*All times are UTC -8

Workshop Instructors

Jenn Schilling

Business Intelligence Manager, Albert

Cindy Gonzales

Data Science Team Lead for the Biosecurity and Data Science Applications Group, Lawrence Livermore National Laboratory

Jennifer Vlasiu

Data Science & Big Data Instructor, York University

Julia Christina Costacurta

PhD Candidate, Stanford University


Introduction to Deep Learning for Image Classification | Cindy Gonzales

TOPICS: Algorithms , Foundations (Mathematics/Statistics) , Software Design and Engineering , Values

Dashboard Design Thinking | Jenn Schilling

TOPICS: Algorithms , Data Science as a Career , Software Design and Engineering , Values

Alternative approaches to A/B Experiments – 3 Causal Impact Approaches | Jennifer Vlasiu

TOPICS: Algorithms , Data Generation/Collection , Data Wrangling , Foundations (Mathematics/Statistics) , Software Design and Engineering , Values

Exploring Hidden Markov Models | Julia Christina Costacurta

TOPICS: Data Generation/Collection , Software Design and Engineering , Values