Statistics

This course introduces a set of actionable tools and methods for the redaction of a master thesis. It is more specifically focused on new emerging digital practices that significantly ease the implementation of core tasks. A first series of five sessions are devoted to reference research. They cover the use of general and academic search engines, the creation of structured bibliography with Zotero, the reuse of free-licensed content and the writing of state of the art synthesis. Six sessions focus on the management and analysis of databases and corpora, building on the principles of "tidy data" as implemented in the R language and the Tidyverse extensions. A last focus is given to text mining techniques as a way to map a corpus of scholar references.

This course teaches quantitative research methods in the political and social sciences. The course covers descriptive statistics, data visualization, data access, probability, sampling, hypothesis testing, inferential statistics, and an introduction linear regression. Students are introduced to the R statistical software and work with data used in academic research.

This course provides an introduction to statistics: the study of collecting, analysing, and interpreting data, which is fundamental to doing any form of quantitative research. Students learn to recognize which analysis procedure is appropriate for a given research problem involving one or two variables; understand principles of study design; and apply probability theory to practical problems. They apply statistical procedures on a computer using RStudio/R; interpret computer output for statistical procedure; calculate confidence intervals and conduct hypothesis tests by hand for small datasets; and understand the usefulness of statistics in their professional area.

Qualitative research means research that concentrates on acquiring a subtle, in-depth understanding of a relatively small group of subjects, as opposed to quantitative research, which emphasizes large sample-size and acquisition of statistical data. Qualitative methods are often used by social for anthropological fieldwork, but also for market research, opinion polling and customer satisfaction surveys. This course shows how to acquire high-quality data on social behavior and attitudes. Topics: introduction to social science methods; what is fieldwork; choosing a topic and planning research; writing a research proposal; interview techniques; focus groups; participant observation; alternative methods; from field notes to data; and writing up and interpreting qualitative data. Units: The regular version of this course is worth 3.0 UC quarter units. The Q version of this course is worth 4 or 4.5 UC quarter units. Students must submit a special study project form which outlines the requirements for the additional units. This is typically an additional paper graded by the instructor of the course.

The course introduces basic statistical techniques and terminology used in analyzing business data and their application to functional areas of business. The course is divided into seven areas: descriptive statistics, probability and probability distribution, discrete and continuous probability distribution models, sampling distributions, hypothesis testing, two-population inferences, and regression analysis and modeling. Topics covered include collection, tabulation, and presentation of numerical data; concepts of probability and probability distributions; sampling; statistical estimation and hypothesis testing; and correlation and regression analysis.

This course offers an introduction to the fundamental concepts and tools of modern machine learning techniques. These tools are at the root of data science and data analytics, which are among the main pillars of the education program. The course discusses topics including the theory of machine learning; probability tools; statistical interference and regression techniques; unsupervised methods such as Principal Component Analysis, hierarchical cluster, and k-means; supervised methods such as K-nearest neighbors, Support Vector Machines, and Multi-Layer Neural Networks; and associative memories. The course consists of lectures, individual and group assignments, and participation in external competitions. The course requires students to have background knowledge in Python programming, elementary calculus, and basic statistics as a prerequisite.

This course provides a foundation of knowledge on Bayesian statistics for advanced undergraduate students or first-year graduate students who have studied introductory statistics. This course introduces basic concepts in Bayesian statistics such as likelihood, prior distribution, posterior distribution, predictive distribution and Bayes factor. This course also offers an opportunity to learn Python programming for Bayesian statistical analysis. It is preferable for students to install Anaconda (https://www.anaconda.com) on their laptop computers and bring them to the classroom.

This is an intermediate course in statistics and data sciences, teaching data analytic skills for a wide range of problems and data. In this course, students learn how to ingest, combine, and summarize data from a variety of models which are typically encountered in data science projects, and they reinforce their programming skills through experience with statistical programming language. Students are exposed to the concept of statistical machine learning and analyze various types of data in order to answer a scientific question. Students learn to embrace data analytic challenges stemming from everyday problems.

The topics covered in this course include introduction of R, probability, independence, conditional probability, Bayes' formula, random variables and distributions, moment generating functions, probability inequalities, law of large numbers, central limit theorem, point estimation, maximum likelihood estimation, Fisher's information, asymptotic efficiency, Hypothesis testing, Wald's test, t-tests, likelihood ratio tests, permutation tests, confidence intervals.