University of Amsterdam Courses

Logic for Economists

2.3

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis course provides a very brief introduction to basic mathematical concepts like propositional and predicate logic, set theory, the number system, and proof techniques.At the end of the course, students will be able to(1) detect the logical structure behind simple puzzles(2) be able to manipulate logical expressions(3) explain the connection between logic and set theory(4) explain the differences between natural, integer, rational, real and complex numbers(5) recognise different basic proof techniquesIntroductionOverview and motivation of the topics to be treated in the coursePropositional logicLogical propositions and the rules that govern them.Predicate logic, set theory, and functionsLogical statements that depend on a variable.NumbersProofsFinal test

Methods and Statistics in Social Sciences

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIdentify interesting questions, analyze data sets, and correctly interpret results to make solid, evidence-based decisions.This Specialization covers research methods, design and statistical analysis for social science research questions. In the final Capstone Project, you’ll apply the skills you learned by developing your own research question, gathering data, and analyzing and reporting on the results using statistical methods.

Data Analytics for Lean Six Sigma

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeWelcome to this course on Data Analytics for Lean Six Sigma. In this course you will learn data analytics techniques that are typically useful within Lean Six Sigma improvement projects. At the end of this course you are able to analyse and interpret data gathered within such a project. You will be able to use Minitab to analyse the data. I will also briefly explain what Lean Six Sigma is.I will emphasize on use of data analytics tools and the interpretation of the outcome. I will use many different examples from actual Lean Six Sigma projects to illustrate all tools. I will not discuss any mathematical background. The setting we chose for our data example is a Lean Six Sigma improvement project. However data analytics tools are very widely applicable. So you will find that you will learn techniques that you can use in a broader setting apart from improvement projects. I hope that you enjoy this course and good luck!Dr. Inez Zwetsloot & the IBIS UvA team

Inferential Statistics

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeInferential 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).

Introduction to Communication Science

4

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeSince Antiquity, scholars have appreciated the importance of communication: as social beings, we cannot exist without communication. We need to interact with people around us, to make sense of the world and to position ourselves in a wider social and cultural reality. In this course, we look at how and why communication evolved as a science and reflect on today’s dominant paradigms. The course also extends beyond the boundaries of communication science itself, exploring dimensions of history, sociology and psychology. Join our class, together with people all over the world.Introduction to Communication Science explores some of the basic theories, models and concepts from the fields of mass, interpersonal and intrapersonal communication. The course begins with a consideration of several basic models, subsequently progressing to the history of communication theory, linear effect-oriented theories, the reception approach and, finally, exploring theories on the production and reinforcement of culture through communication. Upon completion of this course, students should:•have knowledge of the history and development of communication science; •have knowledge of the dominant theoretical approaches within communication science; •have knowledge and understanding of the most important models and concepts in this field. Beginning the week of February 16, 2015, you will be able to join Signature Track, a system that verifies your identity when you take an exam. This option will allow you to earn a Verified Certificate, which provides formal recognition of your achievements in the course and includes the University of Amsterdam logo. Before then, you can complete a “test run” of the exam. You can then re-take the exam after the Verified Certificate becomes available. For information regarding Verified Certificates, see https://courserahelp.zendesk.com/hc/en-us/articles/201212399-Verified-Certificates"

Inferential Statistics

3.2

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeInferential 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).

Big History - From the Big Bang until Today

3

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeWelcome to this Big History course! In this course, renowned scientists and scholars from the University of Amsterdam and beyond will take you on a journey from the Big Bang until today while addressing key questions in their fields. After completing this journey you will have developed a better understanding of how you and everything around you became the way they are today. You will also have gained an understanding of the underlying mechanisms that have helped shape the history of everything and how they wil helpshape the future. Last but not least, you will have developed the skill to use this knowledge to put smaller subjects into a bigger perspective with the aid of the little big history approach, which can help you develop some new ideas on these smaller subjects.

Alternative Mobility Narratives

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeReady to imagine a radically different mobility future? This course is about the stories that we tell ourselves about why and how we move. By critically examining our current narratives, we help you think about mobility in a new way. Using systems dynamics modelling, we explore how a mobility innovation (of your choice) impacts our mobility system as a whole, for better or for worse. This course will invite you to reflect on our mainstream mobility narrative built on engineering and economics. But warning: you may end up never looking at mobility in the same way again! This online course is supported by the EIT Urban Mobility’s Competence Hub. EIT Urban Mobility is an initiative of the European Institute of Innovation and Technology (EIT) that has been working since January 2019 to encourage positive changes in the way people move around cities in order to make them more sustainable and liveable places.

Solid Science: Research Methods

4

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeCan we still put our trust in the social and behavioural sciences? Cases of social scientists exposed as frauds keep turning up and many disciplines are under fire for their failure to replicate key results. No wonder the integrity of our field is being questioned; sloppy science is starting to seem the norm rather than the exception! As social scientist Daniel Kahneman suggests, it is time for the social sciences to clean house. We will try to answer his call with a series of courses that explain the scientific principles of research and how methodology and statistics can help to ensure that research is solid. We will explain the basics and put them into context by showing you how things can go horribly wrong when methods and statistics are abused. And we will teach you how to recognize these questionable research practices - after the fact - in published articles. This first course, Solid Science: Research Methods (in the Social and Behavioral Sciences), will cover the fundamental principles of science, some history and philosophy of science, research designs, measurement, sampling and ethics. This basic material will lay the groundwork for the more technical stuff in subsequent courses. The course is comparable to a university level introductory course on quantitative research methods in the social sciences, but has a strong focus on research integrity. We will use examples from sociology, political sciences, educational sciences, communication sciences and psychology.Please note that this course will focus on quantitative methods, qualitative methods will be treated in a separate course.

Methods and Statistics in Social Science - Final Research Project

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThe Final Research Project consists of a research study that you will perform in collaboration with fellow learners. Together you will formulate a research hypothesis and design, come up with operationalizations, create manipulation and measurement instruments, collect data, perform statistical analyses and document the results. In this course you will go through the entire research process and will be able to help determine what research question we will investigate and how we design and perform the research. This is an invaluable experience if you want to be able to critically evaluate scientific research in the social and behavioral sciences or design and perform your own studies in the future.

Being Smart about Cycling Futures

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeWhat is the future of cycling in our cities that struggle to transition to more sustainable and inclusive forms of mobility? What is the role of innovation in ensuring that cycling becomes easier, safer and more accessible for different groups of people? What are Great Bikes and what are Great Cycling Cities?In this course we tackle these questions, but we do so without providing recipes, one-size-fits-all solutions or rankings of innovations. Instead, this course helps you to develop your own approach to cycling futures and innovation. It teaches you to ask critical questions about various aspects of cycling practice and its place in mobility systems, about cycling innovation and the way in which various stakeholders imagine cycling futures.This unique course is grounded in the results of the Smart Cycling Futures project (2016-2020), conducted in the Netherlands but through readings and assignments it engages with the wider world. Course development was made possible by sponsor enviolo.

Introduction to Communication Science

5

Since antiquity, scholars have appreciated the importance of communication: as social beings, we cannot exist without communication. We need to interact with people around us, to make sense of the world and to position ourselves in a wider social and cultural reality. This course explores some of the basic theories, models and concepts from the fields of mass, interpersonal and intrapersonal communication.

Reclaiming the Street for Livable Urban Spaces

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn Reclaiming the Street, you will learn about the mechanisms of change and will be challenged to apply this knowledge to start creating vibrant streetscapes in your neighbourhood. This six week course will guide you through seminal academic work on the topics of transition management and street experiments while providing practical insights from practitioners from around the world. A final peer-reviewed project integrates key takeaways from each module of this course to help you write an actionable plan for change.This online course is supported by the EIT Urban Mobility’s Competence Hub. EIT Urban Mobility is an initiative of the European Institute of Innovation and Technology (EIT) that has been working since January 2019 to encourage positive changes in the way people move around cities in order to make them more sustainable and liveable places.

Media ethics & governance

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeMedia Ethics and Governance About this course: This course explores some of the basic theories, models and concepts in the field of media ethics. We will introduce influential ethical theories and perspectives, explore changing societal demands and expectations of media creation and media use, and we will elaborate on existing ethical norms for media professionals. After following this course, you will be able to reflect on ethical dilemmas and develop a well-substantiated argumentation for ethical decision making in a variety of media-related contexts.Upon completion of this course, students should:•have knowledge of the history and development of perspectives on media ethics;•have knowledge of the dominant theoretical approaches and concepts;•be able to use this knowledge to develop a well-substantiated argumentation

Descriptive Statistics

5

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeUnderstanding statistics is essential to understand research in the social and behavioral sciences. In almost all research studies, statistics are necessary to decide whether the results support the research hypothesis. In this course you will learn the basics of descriptive statistics; not just how to calculate them, but also how to evaluate them. An important part of the material treated in this course will prepare you for the next course in the specialization, namely the course Inferential Statistics. We will start with the concepts variable and data, the difference between population and sample and types of data. Then we will consider the most important measures for centrality (mean, median and mode) and spread (standard deviation and variance). These will be followed by the concepts contingency, correlation and regression. All these statistics make it possible to represent large amounts of data in a clear way, enabling us to spot interesting patterns. The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work. We will end the course with a short preview of inferential statistics - statistics that help us decide whether the differences between groups or correlations between variables that we see in our data are strong enough to conclude that our predictions were confirmed and our hypothesis is supported.You will not only learn about all these concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software.