Factors models for binary data are extremely common in many social science disciplines. For example, in political science binary factor models are often used to explain voting patterns in deliberative bodies such as the US Congress, leading to an “ideological” ranking of legislators. Binary factor models can be motivated through so-call “spatial” voting models, which posit that legislators have a most preferred policy – their ideal point –, which can be represented as a point in some Euclidean “policy space”. Legislators then vote for/against motions in accordance with the distance between their (latent) preferences and the position of the bill in the same policy space.

In this talk we introduce a novel class of binary factor models derived from spatial voting models in which the policy space corresponds to a non-Euclidean manifold. In particular, we consider embedding legislator’s preferences in the surface of a n-dimensional sphere. The resulting model contains the standard binary Euclidean factor model as a limiting case, and provides a mechanism to operationalize (and extend) the so-called “horseshoe theory” in political science, which postulates that the far-left and far-right are more similar to each other in essentials than either are to the political center. The performance of the model is illustrated using voting data from recent US Congresses. In particular, we show that voting patterns for the 113th US House of Representatives are better explained by a circular factor model than by either a one- or a two-dimensional Euclidean model, and that the circular model yields a ranking of legislators more in accord with expert’s expectations. This is joint work with my Ph.D. student, Xingchen Yu.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This teaching team has decades of practical experience in quant trading, analytics and e-commerce. 

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. 

Let’s parse that.

• Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings. 
• Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R. 
• Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. 

What’s Covered:

• Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames
• Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
• Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2
• Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
• Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

This is a comprehensive course to learn Python Programming for Data Science, Data Analysis and Data Visualization In this course we will learn:

1) Complete understanding of Python from Scratch

2) Python for Data Science and Business Analysis

List of some Topics that we will cover,

1) NumPy : NumPy array, Indexing and Selection, NumPy Operations

2) Pandas : DataFrames, Series, Matrix, Working on missing data, Reading and Writing files

3) Matplotlib : Data Visualization, Plotting different graphs

4) Data Types

5) If-else statement, For loop and While loop

6) Functions and Methods

7) Object Oriented Programming

Learn Python for Data Science to advance your Career in a fun and practical way !!!

This RFA invites applications from multidisciplinary teams to perform secondary data analysis, using existing datasets from two or more multi-site clinical research projects, including clinical trials, natural history studies, and/or comparative effectiveness research. Secondary analyses should address scientific and / or clinical hypotheses that can advance the understanding or care of neurological disorders and conditions within the NINDS mission. In this phased funding mechanism, applications are required to systematically and comprehensively perform cross-project data harmonization and curation, assessed using go/no-go data-quality metrics, prior to funding of the second phase of analyses. Consistent with the FAIR (findable, accessible, interoperable and reusable) data principles, this funding opportunity expects open-source cataloging of the processes and tools used for harmonization, curation, and analysis, as well as controlled access to the curated datasets.

Every year the Center for Teaching and Learning invites UW faculty, staff, and graduate students to present their work on the Scholarship of Teaching and Learning (SoTL) at the UW’s Annual Teaching & Learning Symposium in April.

The Symposium is designed for interactive presentations and discussion on innovative evidence-based teaching strategies, whether it is work you have done in the past or are in the process of implementing. Presentations are in poster format and can not be larger than 4’ x 6′.

• October 21, 2019 – December 9, 2019:  Call for proposals.
• February 10, 2020:  Outcomes of the proposal review process are emailed to all applicants.
• February 19, 2020: Deadline for applicants to confirm their participation.
• March 9, 2020:  Full program (titles, abstracts, and presenters) is posted on the Symposium home page.
• April 6, 2020:  Teaching & Learning Symposium.
• Posters must be set up between 1:35 and 1:45 p.m.
• Symposium schedule: TBD

 

Are your students looking for other funding options to do research overseas?

We highly encourage students to explore the following nationally competitive options, open to U.S. citizens. The Office of Fellowships & Awards can provide assistance with preparing applications:

Boren Fellowships: fund up to 12 months overseas—students must incorporate language study into their plans but can combine with independent research, other academic study, or internships (or propose exclusively to study language). This opportunity is currently quite undersubscribed at the graduate level—nationally, approximately 1 out of 3 applicants were awarded last year! Application deadline is in January; please see our website for scheduled information sessions and application support.

Fulbright U.S. Student Program: funds 8-10 months of overseas independent research or academic study (or a combination of both). Fulbright information sessions on campus will be announced in late March and held throughout spring quarter. Application deadline is in September; please see our website for more information and read about our recent graduate student/alumni awardees.

Fulbright-Hays Doctoral Dissertation Research Abroad Award: funds 6-12 months of dissertation research overseas (must have candidacy by the start of the grant). At least part of the research must be done in a language other than English; applicants are expected to have a thorough background of relevant area studies coursework and applicable language skills. Application deadline varies and may be announced any time between January – April; information sessions forthcoming. Permanent residents may also apply for this funding. Students must work through the Office of Fellowships & Awards to submit an application—see our website.

 

On 1 October 2019, the University of Vienna has opened a new Department of Demography in the Faculty of Social Sciences. Headed by Wolfgang Lutz, this new department will be a strong university-based pillar of the Wittgenstein Centre for Demography and Global Human Capital, which also includes IIASA’s World Population Program and the Vienna Institute of Demography (VID) of the Austrian Academy of Sciences. In 2020 it plans to start the Vienna Doctoral School in Demography and in due course an English language Masters program with a focus on population and sustainable development.

Over the coming months 2-3 professorships and several post-doc and prae-doc positions will be announced, beginning with the announcement for two University Assistants (prae-doc) at the Department of Demography (see link below).

Deadline: 11/11/2019 

CUHK Shenzhen and Princeton University Postdoctoral Fellowship Program: 

Within the framework of their Memorandum of Understanding, the Chinese University of Hong Kong, Shenzhen and Princeton University have established a Postdoctoral Fellowship Program: CUHK Shenzhen-Princeton Postdoctoral Program (CPPP), aiming at training highly-qualified, early-career postdoctoral researchers in studies of contemporary China so that they are positioned to become leaders in their respective academic fields.  Princeton University and CUHK Shenzhen invite applications for a postdoctoral research associate in studies of contemporary China.

The fellowship is expected to be awarded for up to two years: the first 12 consecutive months at CUHK Shenzhen, and the next 12 consecutive months at Princeton University with a visiting appointment at CUHK Shenzhen, with renewal after the first year contingent on satisfactory performance.  Preferred start date is September 1, 2019. The position is open to early-career scholars who would be in residence and participate in the host organization’s activities, including student-faculty seminars, workshops, and public lectures.  The position is primarily open to data science-relevant disciplines, but it can be open to any discipline, as long as the fellow conducts research on contemporary China under the guidance of faculty at both Universities. The candidate’s research must be supervised by a faculty member at each University and, as such, must receive the endorsement of a faculty member at each institution in order to apply. 

To apply for a postdoctoral position, please see listing D-19-PII-00008 in Princeton’s Academic Listings here:  https://puwebp.princeton.edu/AcadHire/apply/application.xhtml?listingId=….

Application deadline:  May 1, 2019 by 11:59 pm EST.

If the applicant has any questions about the process and documentation, please refer to the program FAQs, here: https://ccc.princeton.edu/postdocfaq

The Department of Policy Analysis & Management (PAM) at Cornell University invites applications for a Postdoctoral Associate position in Big Data/Data Science. The position starts in July/August 2020 and will continue for two years, subject to a satisfactory first-year evaluation. Successful candidates will have demonstrated strengths in applying data science or computational approaches to applied questions in economics, demography, sociology, or policy, with expertise in areas such as machine learning, digital trace data collection, text analysis, or other techniques.

The postdoctoral position will include considerable time to develop independent research and to form collaborative research projects with faculty in PAM and throughout Cornell. In addition, the Postdoctoral Associate will be expected to assist in the development and instruction (once per year) of an undergraduate introduction to data science for social science majors, along with faculty from the Department of Policy Analysis and Management. The Postdoctoral Associate will also be expected to be actively involved with department and associated-center activities and events, including workshops on data science techniques and other programming for graduate training in the social sciences.   

The Postdoctoral Associate will have access to university resources and receive a competitive annual salary plus benefits and a research/travel allowance. Applicants must have completed a Ph.D. in demography, economics, public policy, sociology, or a related social science discipline, by the starting date. Screening of applications begins December 2, 2019, and will continue until the position is filled.

Applications must include: (a) letter of application; (b) curriculum vita; (c) a statement describing expertise in data science, including research applications and training experience; (d) examples of written work; (e) diversity statement and (f) three letters of recommendation. These materials must be submitted online via Academic Jobs Online, https://academicjobsonline.org/ajo/jobs/15116.  For questions, please contact Matt Hall (mhall@cornell.edu ) or Maria Fitzpatrick (maria.d.fitzpatrick@cornell.edu), co-chairs of the search committee.

CU Population Center (CUPC) at the University of Colorado Boulder is currently recruiting two postdoctoral fellows with expertise in population-environment research, to start August 2020. The initial term of appointment is one year, but reappointment for a second year is possible, subject to performance evaluation.

CUPC, housed in the Institute of Behavioral Science, is a national leader in demographic research on population health, environmental demography and migration patterns and processes. This postdoctoral research position builds on CUPC’s strengths in environmental demography, and within that area, candidates should have research expertise in migration-climate-health linkages, rural demography, social vulnerability and natural hazards, and/or urbanization processes and their effects on the environment and health.

Candidates must have experience in quantitative methods, the use of computational, statistical or data scientific approaches applied to social science or interdisciplinary research settings as well as data integration that involves spatial and non-spatial data. They are expected to bring particular interest in interdisciplinary research and will be expected to participate in, and develop, projects collaborative with Earth Lab Boulder, an initiative harmonizing the wealth of Earth observation data to facilitate innovative scholarship using combinations of satellite, survey, and field data at various spatial and temporal scales.

Application Deadline: December 31, 2019

Job posting: https://jobs.colorado.edu/jobs/JobDetail/CUPC-PostDoctoral-Associate/15689

Application Details: Marisa.Seitz@colorado.edu

Position Details: Lori.Hunter@colorado.edu; Stefan.Leyk@colorado.edu