With the U.S. midterms approaching, we are exploring election and voting data this week with the collection of resources below. In addition, the latest Harvard Data Science Review Podcast episode features four experts who discuss their predictions for the upcoming midterm elections and how these elections might impact the presidential race in 2024.
Looking for a work in data science? The Dominici Lab, led by HDSI Faculty Co-Director Francesca Dominici, is seeking a Senior Programmer to join its dynamic team. Check out more full-time opportunities at Harvard and internship opportunities at Microsoft Research New England and Netflix below!
Happy (almost) Halloween! Have a safe and fun Halloweekend!
With the midterms less than three weeks away, The Upshot examined the president’s policy goals and Congress’s successes in previously unreported detail. It matched every single line of spending and revenue in the jobs and families plans from Mr. Biden’s 2022 budget with cost estimates for the five large policy bills passed by Congress in the last year.
Latest HDSR Podcast Episode
It's Election Time Again – Do We Predict Better This Time?
Harvard Data Science Review Podcast | Episode 22
With the 2022 U.S. midterms right around the corner, this month’s podcast is all about elections. Who is going to win and why? Listen to four experts discuss their predictions for the upcoming midterm elections in November and how these elections might impact the presidential race in 2024.
Episode Guests:
Caroline Carlson, Senior Data Science Analyst at Dynata and Analyst for Decision Desk HQ
Ryan Enos, Professor of Government and Director of the Center for American Political Studies at Harvard University and co-author of Predicting the 2020 Presidential Election for HDSR
Tuesday, November 15
9:00 AM – 5:00 PM EST Science + Engineering Complex, Harvard SEAS
Wednesday, November 16, 2022
8:00 AM – 6:30 PM EST
Klarman Hall,
Harvard Business School
Two days of in-person workshops, tutorials, + plenary sessions
The invites you to the HDSI Annual Conference 2022, a two-day, in-person event that will showcase data science in research and education through panels, keynotes, workshops, and tutorials featuring speakers from across Harvard, academia, and industry. Join us to connect with data science professionals, expert methodologists, and educators across disciplines to ignite new discoveries with impacts on health, education, economics, social policy, business, and the humanities.
This event is free and open to the public. Ticket required for admission. Please RSVP to reserve your spot.
Thursday, October 27, 2022
5:00 PM – 6:00 PM EST
Virtual (Zoom)
Hosted by R User Group at the Harvard Data Science Initiative
Speaker:
Fayette Klaassen, Postdoctoral Researcher, Department of Global Health and Population, Harvard T.H. Chan School of Public Health
Join RUG at the HDSI to learn about how you can use RStan to perform Bayesian statistics in R! This talk will provide an introduction to what Bayesian statistics and RStan are and how you can get started using them.
Fayette Klaassen works on Bayesian statistical models to predict COVID-19 infections. Fayette will introduce how to write a Stan program and describe the workflow using Stan as well as go over some example Stan models.
Optimal nonparametric estimation of heterogeneous
causal effects
Thursday, November 3, 2022
3:30 PM – 5:30 PM EST
Hawes Hall, Classroom 203, Harvard Business School
HDSI Causal Seminar: Edward Kennedy, Carnegie Mellon
Edward Kennedy, Associate Professor of Statistics and Data Science, Carnegie Mellon University
Edward's research interests include causal inference, missing data, functional estimation, machine learning, and general nonparametrics, especially in settings involving high dimensional and otherwise complex data.
Abstract:
Estimation of heterogeneous causal effects -- i.e., how effects of policies and treatments vary across units -- is fundamental to medical, social, and other sciences, and plays a crucial role in optimal treatment allocation, generalizability, subgroup effects, and more. Many methods for estimating conditional average treatment effects (CATEs) have been proposed in recent years, but there have remained important theoretical gaps in understanding if and when such methods make optimally efficient use of the data at hand. This is especially true when the CATE has nontrivial structure (e.g., smoothness or sparsity). Read more.
Symposium on Science, Technology, + the Human Future
November 3 – 5, 2022
Harvard University
Hosted by the Program on Science, Technology + Society at Harvard University in celebration of its 20th Anniversary
The Program on Science, Technology & Society is celebrating its 20th anniversary with a Symposium on Science, Technology and the Human Future, to be held at Harvard from November 3-5, 2022. This major event will feature a wide range of high profile speakers across political, academic, and broader society.
The Symposium begins at 5pm on Thursday, November 3 with a keynote lecture by novelist Arundhati Roy, including performances of original music and fiction written by Harvard students. We continue on Friday with panels on the role of science and technology in shaping the human future, including the future of knowledge, life, policy, and cities. Saturday includes open discussions on how STS can position us to better understand and govern ourselves, our societies, and our Earth.
Biomedical Informatics Entrepreneurs Salon: Anne Wojcicki, 23andMe
Tuesday, November 8, 2022
5:00 PM – 6:00 PM EST
Virtual (Zoom)
Hosted by the Harvard Office of Technology Development and the Harvard Medical School Department of Biomedical Informatics
Speaker:
Anne Wojcicki, CEO and Co-Founder of 23andMe
Anne is a pioneer in the direct-to-consumer DNA testing space and her vision and persistence have allowed 23andMe to provide people with unprecedented access to genetic information. Through its research platform, 23andMe has brought personalized medicine directly to millions of consumers. Read more.
Thursday, November 10, 2022
1:30 PM – 2:30 PM EST
Virtual (Zoom)
HDSI Industry Seminar: Tammy Levy, Captain.tv
Tammy Levy, Chief Games Officer, Captain.tv
At Captain.tv, a startup pioneering streamer-led multiplayer community games, Tammy draws on her background in computer science and design to drive the decisions behind the business and the fun of building games.
Abstract:
Underneath the fun of games we can find complex economies. In the last 15 years, with the rise of accessible broadband internet, video game developers have been able to regularly release game updates or "patches" through a process called live servicing. In addition to new content, game designers often add, remove, and rebalance the resources in the game– effectively manipulating the game's economy on a regular basis. In this talk, I will cover the basic principles of game economies and the core business KPIs used to monitor a game's performance. Then I'll walk through real examples behind the data-driven decisions for game optimization.
A simple process for estimating child health impact at a parcel level by cleaning and synthesizing municipal datasets that are commonly available but seldom joined due to data quality issues authored by IACS Fellow Isaac Slavitt.
New model acts as search engine for large databases of pathology images, has potential to identify rare diseases and therapies. Senior author HDSI Faculty Affiliate Faisal Mahmood.
An interactive piece of work funded in part by the Inequality in America Initiative and the HDSI on how our inability to change America’s most important document is deforming our politics and government by Jill Lepore, David Woods Kemper ’41 Professor of American History at Harvard University.
Learn the concepts and techniques that make up the foundation of data science and machine learning
About the course:
Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?
What you'll learn:
Gain hands-on experience and practice using Python to solve real data science challenges
Practice Python programming and coding for modeling, statistics, and storytelling
Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn
Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems
Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study
Meets weekly on Wednesdays
12:15 – 1:45 PM EST
November 9 – December 7 (no class on November 23)
A month-long program designed to help students understand and process data as it is typically communicated via news media and popular culture
Instructor:
Emily Oster, Professor of Economics, Brown University
Everyday Analytics will focus on four key analytic concepts which, when deeply understood, can feed back into better understanding of data in and outside the workplace. The goal of the program is to enhance facility with these concepts to develop fluency in applying them in a wide range of situations. Front Row students will have an opportunity to work directly with data, alongside analysis of existing evidence.
Harvard Data Science Initiative Postdoctoral Fellowship Program
Deadline: Monday, November 14th, 11:59 PM EST
The Harvard University Data Science Initiative is seeking applications for itsHarvard Data Science Initiative Postdoctoral Fellows Programfor the 2023-2024 academic year. The normal duration of the Fellowship is two years. Fellows will receive a generous salary as well as an annual allocation for research and travel expenses.
We are looking for researchers whose interests are in data science, broadly construed, and including researchers with a primarily methodological focus as well as researchers who advance both methodology and application. Fellows will be provided with the opportunity to pursue their research agenda in an intellectually vibrant environment with ample mentorship. We are looking for independent researchers who will seek out collaborations with other fellows and with faculty across all schools of Harvard University.
We recognize that strength comes through diversity and actively seek and welcome people with diverse backgrounds, experiences, and identities.
Interested in reading more about data science projects and news at Harvard? Check out our blog for features, top stories, and what we are learning now in the world of data.
Interested in engaging more with the Data Science community at Harvard? Join our Slack! The Slack is currently Harvard only, so if you are interested simply click the button below and send us an email from your Harvard email address.
