This document describes the ASA DataFest competition, which aims to give undergraduate students experience analyzing complex, real-world datasets. It involves teams of students working over a weekend to analyze a provided dataset and identify meaningful insights. They then present their findings to judges on Sunday. Past datasets have come from sources like the LAPD, Kiva lending data, and eHarmony. The goals are to encourage risk-taking, give students access to rich data, and foster a community experience through friendly competition. Choosing engaging datasets and cultural aspects like mentorship from statisticians are emphasized as key to success.
Data Driven College Counseling by SchooLinksKatie Fang
This workshop will expose school counselors and administrators to a framework for data-driven college planning and accountability. Attendees will learn about data collection, pattern analysis, and translating insight into intervention to best support students in their college planning process. No special statistical knowledge is required for this session, just enthusiasm to understand how using data unlock better student outcomes.
Understanding, predicting and optimizing learning with Learning AnalyticsCITE
Author: Jingyan Lu, The University of Hong Kong
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http://www.cite.hku.hk/news.php?id=501&category=cite
This is the sixth segment in the NISO training series, Assessment Practices and Metrics in the 21st Century. The presentation was provided by guest lecturer, Nancy Turner of Temple University on November 30, 2018.
This presentation was provided by Rachel Lewellen of Harvard University during the NISO event, Assessment Practices and Metrics in the 21st Century Training Session Four held on Friday, November 9th.
Data Driven College Counseling by SchooLinksKatie Fang
This workshop will expose school counselors and administrators to a framework for data-driven college planning and accountability. Attendees will learn about data collection, pattern analysis, and translating insight into intervention to best support students in their college planning process. No special statistical knowledge is required for this session, just enthusiasm to understand how using data unlock better student outcomes.
Understanding, predicting and optimizing learning with Learning AnalyticsCITE
Author: Jingyan Lu, The University of Hong Kong
--------------------------------------------------------
http://www.cite.hku.hk/news.php?id=501&category=cite
This is the sixth segment in the NISO training series, Assessment Practices and Metrics in the 21st Century. The presentation was provided by guest lecturer, Nancy Turner of Temple University on November 30, 2018.
This presentation was provided by Rachel Lewellen of Harvard University during the NISO event, Assessment Practices and Metrics in the 21st Century Training Session Four held on Friday, November 9th.
Research output in Irish H.E. academic libraries 2000-2015 Terry O'Brien
Presentation given by Terry O'Brien & Kieran Cronin at CONUL (Consortium of National & University Libraries) 2017 Annual Conference - Inspiring and Supporting Research (Athlone, Ireland, May 2017)
Strategic Metrics, presented at the Leadership Seminar on Strategy, Assessment and Service Development. University of Lund, Sweden. 19th September 2012. Presentation by Selena Killick, Cranfield University. Presentation discusses the need for assessment of Library Strategies and some of the techniques available to achieve this.
This presentation was provided by Martha Kyrillidou of QualityMetrics, LLC during the initial session of the NISO Training Series, Assessment Practices and Metrics for the 21st Century, held on Friday, October 19, 2018.
Case Study: Increasing Access through OER AdoptionJeremy Anderson
Presentation delivered at EDUCAUSE 2018 on the three methods used for increasing adoption of OER at Bay Path University. A special focus and emphasis is placed on the practical learnings and future directions at The American Women's College.
College Board #DeliveringOpportunity Presentation - 3-5-14CollegeBoardSM
College Board president David Coleman's slideshow presentation from the announcement about the new SAT and #DeliveringOpportunity to more students. Presented in Austin, TX, on March 5, 2014.
Presentation for the Center for Teaching Excellence at Lansing Community College to share results from my sabbatical project, as well as practical applications for developing research assignments. Thanks to Maricopa Community College for sharing an <a>assignment planning checklist and sample assignment</a> that I adapted and used in the workshop.
In this session, PhD students will investigate the significance of developing a research agenda and its role in professional development. Participants will explore how to craft and refine their own research agendas. Participants are invited to bring their research agendas (or statements of research interests) to share/critique.
Customer churn is an area that business owners and operators are trying to figure out. With the help of Microsoft Azure Machine Learning, businesses are now able to see how customer churn affects their bottom line and how to predict major customer changes.
Research output in Irish H.E. academic libraries 2000-2015 Terry O'Brien
Presentation given by Terry O'Brien & Kieran Cronin at CONUL (Consortium of National & University Libraries) 2017 Annual Conference - Inspiring and Supporting Research (Athlone, Ireland, May 2017)
Strategic Metrics, presented at the Leadership Seminar on Strategy, Assessment and Service Development. University of Lund, Sweden. 19th September 2012. Presentation by Selena Killick, Cranfield University. Presentation discusses the need for assessment of Library Strategies and some of the techniques available to achieve this.
This presentation was provided by Martha Kyrillidou of QualityMetrics, LLC during the initial session of the NISO Training Series, Assessment Practices and Metrics for the 21st Century, held on Friday, October 19, 2018.
Case Study: Increasing Access through OER AdoptionJeremy Anderson
Presentation delivered at EDUCAUSE 2018 on the three methods used for increasing adoption of OER at Bay Path University. A special focus and emphasis is placed on the practical learnings and future directions at The American Women's College.
College Board #DeliveringOpportunity Presentation - 3-5-14CollegeBoardSM
College Board president David Coleman's slideshow presentation from the announcement about the new SAT and #DeliveringOpportunity to more students. Presented in Austin, TX, on March 5, 2014.
Presentation for the Center for Teaching Excellence at Lansing Community College to share results from my sabbatical project, as well as practical applications for developing research assignments. Thanks to Maricopa Community College for sharing an <a>assignment planning checklist and sample assignment</a> that I adapted and used in the workshop.
In this session, PhD students will investigate the significance of developing a research agenda and its role in professional development. Participants will explore how to craft and refine their own research agendas. Participants are invited to bring their research agendas (or statements of research interests) to share/critique.
Customer churn is an area that business owners and operators are trying to figure out. With the help of Microsoft Azure Machine Learning, businesses are now able to see how customer churn affects their bottom line and how to predict major customer changes.
Slides from the presentation of this NYC meetup : http://www.meetup.com/Data-Modeling/events/224554990/
I talked about how to model churn before even thinking about the machine learning model.
Amazon Machine Learning Case Study: Predicting Customer ChurnAmazon Web Services
We do a deeper dive into Amazon Machine Learning, using a specific business problem as an example – predicting if the customer is about to leave your service, also known as customer churn. We examine several practical aspects of building and using a model, including the use of the recipe language for training data manipulation and modeling the costs of false positive/negative errors.
AWS re:Invent 2016: Predicting Customer Churn with Amazon Machine Learning (M...Amazon Web Services
In this session, we take a specific business problem—predicting Telco customer churn—and explore the practical aspects of building and evaluating an Amazon Machine Learning model. We explore considerations ranging from assigning a dollar value to applying the model using the relative cost of false positive and false negative errors. We discuss all aspects of putting Amazon ML to practical use, including how to build multiple models to choose from, put models into production, and update them. We also discuss using Amazon Redshift and Amazon S3 with Amazon ML.
Beyond Churn Prediction : An Introduction to uplift modelingPierre Gutierrez
These slides are from a talk I at the papis conference in Boston in 2016. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them.
I tried to bridge the gap between causal inference theory and uplift theory, especially concerning how to properly cross validate the results. The notation used is the one from uplift modelling.
Spotify uses a range of Machine Learning models to power its music recommendation features including the Discover page and Radio. Due to the iterative nature of training these models they suffer from IO overhead of Hadoop and are a natural fit to the Spark programming paradigm. In this talk I will present both the right way as well as the wrong way to implement collaborative filtering models with Spark. Additionally, I will deep dive into how Matrix Factorization is implemented in the MLlib library.
Algorithmic Music Recommendations at SpotifyChris Johnson
In this presentation I introduce various Machine Learning methods that we utilize for music recommendations and discovery at Spotify. Specifically, I focus on Implicit Matrix Factorization for Collaborative Filtering, how to implement a small scale version using python, numpy, and scipy, as well as how to scale up to 20 Million users and 24 Million songs using Hadoop and Spark.
These are slides from Ellen Wagner\'s featured theme presentation Making Learning Analytics Matter in the Educational Enterprise from Blackboard World 2012, New Orleasn, LA, July 12, 2012
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
UX Burlington 2017: Exploratory Research in UX DesignSarah Fathallah
Presentation given at the 2017 UX Burlington conference, on the topic of "Exploratory Research in UX Design."
Exploratory research focuses on gaining a deep understanding of the lives of the end users and the contexts in which they use certain products and services. At its core, it’s about challenging and exploring the problem space, before venturing into the solution space. Using real-life examples of digital tools that help people access affordable housing or register to vote, this talk will explore the different tools used for exploratory research, including ethnographic interviews, contextual inquiry, and co-creation activities and prompts. This talk will leave the audience with a better understanding of the types of insights that exploratory research generates, and how they can complement the findings of evaluative or comparative research.
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Learning to Curate Research Data
Jennifer Doty, Research Data Librarian, Emory Center for Digital Scholarship, Emory University, Robert W. Woodruff Library
Using Qualitative Methods for Library Evaluation: An Interactive WorkshopOCLC
Connaway, Lynn Silipigni, and Marie L. Radford. 2016. "Using Qualitative Methods for Library Evaluation: An Interactive Workshop." Presented at the Libraries in the Digital Age (LIDA) Conference, Zadar, Croatia, June 14.
Using Qualitative Methods for Library Evaluation: An Interactive WorkshopLynn Connaway
Connaway, Lynn Silipigni, and Marie L. Radford. 2016. "Using Qualitative Methods for Library Evaluation: An Interactive Workshop." Presented at the Libraries in the Digital Age (LIDA) Conference, Zadar, Croatia, June 14.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
2. Motivation
• end-of-term projects, and even capstone experiences, can
become run-of-the-mill
• students seeking to please the instructor, sometimes
assuming instructor already knows how to do the analysis;
and so their input isn’t really important.
• depending on project, the best students might not get to
“shine”
• grades are important, but can discourage risk-taking
• there are few opportunities to wrestle with complex, real data
3. What is ASA DataFest?
• A celebration of data!
• A competition for teams of undergraduates to
find insight and meaning in a rich and complex
data set.
• A data hackathon
4. A typical DataFest
• Friday Night
• Meet the data
• Friday night through Sunday afternoon
• Work furiously. Eat.
• Talk to roving statisticians from industry
and academics
• Sunday afternoon
• 5 minute presentations to judges
• Winners announced
5. Data
• 2011: Los Angeles Police Department Arrest Reports
Make a policy recommendation to reduce crime in Los Angeles.
• 2012: kiva.com lending data
What motivates people to lend money, and what factors are associated with paying loans?
• 2013: eHarmony dating data
What qualities do people look for in prospective dates?
• 2014: GridPoint energy consumption data
How can clients best save money and energy?
• 2015: edmunds.com
Detect insights into the process of car shopping to make shopping process easier for visitors.
• 2016: Ticketmaster
How can fans be better connected to the concerts they wish to attend?
7. Not StatsFest
• Not about statistical modeling
• No pre-defined “correct” outcome
• Many access points for students at different
levels
• Emphasis on data
• “fast” analysis
8. Why DataFest?
• Friendly competition brings out best
• “Group work” in a setting that actually requires
teamwork
• Access to complex data that isn’t available to
(most) classrooms
• Cultural indoctrination (the “secret sauce”?)
9. Choosing the data
• The data must have a personality!
• a spokeperson explains why the data are important
and what they hope to learn
• Many variables (p more important than n)
• Aim for about 1 GB
• Context is key: accessible, interesting, cool
• 5-6 months time working with data donor to prep data
10. “Secret Sauce”
• “To my mind, the crucial but unappreciated
methodology driving predictive modeling’s
succcess is…the Common Task Framework”
— D. Donoho “50 Years of Data Science”
11. CTF Key Features
• Shared data
• A set of competitors
• Judges
In Donoho’s setting, the goal is prediction. But
more generally, DF encourages improvement
through shared information between communities.
