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Regular version of the site

Recommended online-courses

Below we suggest a wide range of interdisciplinary courses for students with various research interests. The choice of the specific course is to be approved by the academic director of the programme. Note that one of the course is to be chosen within the regular curriculum.

Interdisciplinary courses

1.  Questionnaire Design for Social Surveys (Coursera)

https://www.coursera.org/learn/questionnaire-design - questionnaires in sociology
University of Michigan
This course will cover the basic elements of designing and evaluating questionnaires. We will review the process of responding to questions, challenges and options for asking questions about behavioral frequencies, practical techniques for evaluating questions, mode specific questionnaire characteristics, and review methods of standardized and conversational interviewing.

 2.  Sampling People, Networks and Records (Coursera)

https://www.coursera.org/learn/sampling-methods - sampling methods in sociology
University of Michigan
Good data collection is built on good samples. But the samples can be chosen in many ways. Samples can be haphazard or convenient selections of persons, or records, or networks, or other units, but one questions the quality of such samples, especially what these selection methods mean for drawing good conclusions about a population after data collection and analysis is done. Samples can be more carefully selected based on a researcher’s judgment, but one then questions whether that judgment can be biased by personal factors. Samples can also be draw in statistically rigorous and careful ways, using random selection and control methods to provide sound representation and cost control. It is these last kinds of samples that will be discussed in this course. We will examine simple random sampling that can be used for sampling persons or records, cluster sampling that can be used to sample groups of persons or records or networks, stratification which can be applied to simple random and cluster samples, systematic selection, and stratified multistage samples. The course concludes with a brief overview of how to estimate and summarize the uncertainty of randomized sampling.

3.  Towards language universals through lexical semantics: introduction to lexical and semantic typology (Coursera)

https://ru.coursera.org/learn/lexical-semantic-typology - semantics, typology
HSE
The aim of the course is to obtain the idea of the lexicon as a complex system and to get the methodology of the typological approach to the lexicon cross-linguistically, as well as to learn about the general mechanisms of semantic shift and their typological relevance. By the end of the course the students should know the basic principles of lexical organization, the main parameters of semantic variations in lexicon, and be able to apply the basic methods of the analysis of lexical meaning to different lexical domains. The course is designed for students of linguistic programs (BA, MA, PhD), as well as for teachers and researchers in the named field. The course contains the overview of different approaches to the semantic description of lexical items and lexical systems in different languages and discusses the methodology of Moscow Lexical Typology Group (lecture 1). This methodology (“frame approach”) is illustrated with the data of the following domains: aquamotion verbs (lecture 2), verbs of falling (lecture 3), adjectives denoting oldness (lecture 4) and pain metaphors (lecture 5 and 6). The results of the analyses are visualized with specially constructed lexical semantic maps.

4.  The Bilingual Brain (Coursera) 

https://www.coursera.org/learn/bilingual – psychologinguistics
University of Houston System
This course explores the brain bases of bilingualism by discussing literature relevant to differences in age of initial learning, proficiency, and control in the nonverbal, single language and dual-language literature. Participants will learn about the latest research related to how humans learn one or two languages and other cognitive skills.

5.  Introduction to Catalan Sign Language: Speaking with Your Hands and Hearing with Your Eyes (Future learn)

https://www.futurelearn.com/courses/lsc - sign languages and linguistics
Pompeu Fabra University Barcelona
Learn about Catalan Sign Language in daily life, its grammar, and importance to the deaf community with this free online course.

6.  Découvrir l'anthropologie (EDX)

https://www.edx.org/course/decouvrir-lanthropologie-louvainx-louv6x-1 - cultural anthropology (read in French)
LouvainX - Free online courses from Université catholique de Louvain
Comment les humains s’organisent-ils en sociétés? Qu’appelle-t-on « familles » et comment se constituent-elles ? Quel regard et quels types d’analyse l’anthropologie propose-t-elle sur les systèmes de parenté, symboliques, politiques, religieux?
L’anthropologie prospective nous aide à comprendre et à anticiper les phénomènes qui transforment nos modes de vie contemporains. Elle pose un regard transversal sur les actions, les pratiques et les dires des personnes avec lesquelles travaillent les chercheurs.

7.  American Deaf Culture (Coursera)

https://www.coursera.org/learn/deaf-culture - anthropology, deaf culture
University of Houston System
This is a six-week course providing a historical overview of the American Deaf community and its evolving culture. Theoretical frameworks from sociology are explored. Deafness as a culture and not a disability is explained as participants are guided into the world of Deaf culture.

Statistics and data management

1.  Statistics with R Specialization

https://www.coursera.org/specializations/statistics
This course will cover the basic elements of designing and evaluating questionnaires. We will review the process of responding to questions, challenges and options for asking questions about behavioral frequencies, practical techniques for evaluating questions, mode specific questionnaire characteristics, and review methods of standardized and conversational interviewing.

2.  Statistical Inference

https://www.coursera.org/learn/statistical-inference
Johns Hopkins University
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
3.  Inferential Statistics
https://www.coursera.org/learn/inferential-statistics-intro
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data

4.  Inferential Statistics

https://www.coursera.org/learn/inferential-statistics
University of Amsterdam
Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population. We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. You will also learn how to perform these tests using freely available software. For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test).

5.  Regression Models

https://www.coursera.org/learn/regression-models
Johns Hopkins University
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

6.  Regression Modeling in Practice

https://www.coursera.org/learn/regression-modeling-practice
Wesleyan University
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you

7.  Bayesian Statistics: From Concept to Data Analysis

https://www.coursera.org/learn/regression-modeling-practice
University of California, Santa Cruz
This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.

8.  Bayesian Statistics

https://www.coursera.org/learn/bayesian
This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."

9.  Getting and Cleaning Data

https://www.coursera.org/learn/data-cleaning
Johns Hopkins University
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

10. Dealing With Missing Data

https://www.coursera.org/learn/missing-data
University of Maryland, College Park
This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®