This week (10/10-14), join us for the following workshops. Plan ahead for future weeks by exploring the Library Class Calendar(for online workshops) or the Engage (for in-person workshops).
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Industry Reports and Where to Find Them (I): Basic Elements
An industry is a group of related companies based on their primary business activities, including raw materials, goods, or services. What are the basic elements of an industry report? What are the methods of conducting an industry analysis? Where can you find industry reports? You will find answers to the questions after the workshop.
This workshop will be held in person and virtually, please register in advance on Engage:
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Introduction to SQL
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Hopelessly Computational (I): Blue Team Dynamics
In this session, we start with an overview of the field of computational social science, broadly defined. We then follow one path into the field, beginning with the seminal case of Google Flu Trends. It was a surveillance tool that Google launched in 2008 to estimate influenza activity in near-real time, and it stopped publishing estimates in 2015. Parts of this story you may be aware of: the claim of the ascendance of data-driven approach that raised hope of faster and easier estimates than “old-school” methods of data collection and statistical analysis, and the model’s major stumble in subsequent seasons.
However, there is much more to this story. Besides, scientific efforts on harnessing Google web search query data to predict endemics and pandemics have continued to the present. We review publications along this line of research. We focus on one issue, among others, the “blue team dynamics”. This describes a process where the algorithm producing the data has been modified by the service provider in accordance with their business model, inducing specific user behaviors and introducing patterns into data.
More generally, we discuss the benefits and biases of digital social research associated with algorithm-driven big data for forecasting. For instance, using Twitter data for political forecasting also falls prey to this “blue team dynamics”.
Please visit this web page for full details of the series Hopelessly Computational, including the outline and references for each past and upcoming session.
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Bloomberg Training Session – Equities & Company Analysis
This training session provides an opportunity for students to learn the basics and functions of using the Bloomberg terminals, focusing on the module of Equities and Company Analysis, including Stock/Company Screening, Company Description and Overview, Fundamentals and Financial Statements, Historical Price Table, Total Return Comparison, Monitor Equity Offerings, Stock Comparison, News Search, Drag & Drop and Data API.
The Bloomberg analyst will exemplify those functions in real business and practical implications. Welcome to bring your questions for the Q&A part. Open to all faculty and students online. Due to Bloomberg’s policy, there will be no recording provided after the session. Workshop slides can be shared with the participants if needed.
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Hopelessly Computational (II): Wisdom of Crowds
In this session, we turn to the second facet of computational social research—research design utilizing crowdsourcing technology to harness collective intelligence. We focus on one stage in a research project, data generation, including data collection and data production processes such as text annotation.
We discuss three such types of research designs: 1) an open survey that evolves over time based on the ideas of its participants; 2) a system that distributes microtasks in the crowd, whose outputs are as reliable and valid as those from expert human readers; and 3) a software application that interfaces with crowdsourcing technology, and that automates recruiting, collecting both behavior and survey data, and providing incentives to generate responses all in one stop.
These kinds of research design have the potential to improve the scope, efficiency, cost, scalability, sampling, response rates, and convenience of social scientific projects, compared to research in the analog age. Challenges of data quality control, assessing response biases, or adjusting sampling biases can be addressed in the design phase or analysis of data.
Please visit this web page for full details of the series Hopelessly Computational, including the outline and references for each past and upcoming session.
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Hopelessly Computational (III): Science of Where
It is always intriguing to acquire data, pin it on a location, and decipher the meaning behind the information we acquired and how that is related to others. Snow’s cholera map is one of the earliest disease maps that presents the co-relationship between patients and water pumps. However, with limited technology, it was impossible to further explore and discover the more dynamic and complex patterns, let alone make predictions of the potential development.
Nowadays, thanks to the rapid development of technology, to name a few, satellite imagery and the Internet of things (IoT), the acquisition process is more streamlined. Besides, GIS and computation together reveals the underlying patterns of the data and makes more accurate predictions with the data. This session, using three studies on the influence of COVID19 over urban regions as examples, presents the approaches from a spatial perspective.
However, technology doesn’t automatically solve every problem, and researchers have to be aware of distortions and potential issues in stages from obtaining data to making decisions. We will discuss some common cases, including the causes and the steps to eliminate their effects.
Please visit this web page for full details of the series Hopelessly Computational, including the outline and references for each past and upcoming session.
