This course exposes students to the logic of statistical reasoning and its application in the quantitative social sciences. It is meant as a thorough but accessible introduction to the topics of descriptive statistics, probability theory, and statistical inference with hands-on exercises.
Statistics is the lingua franca of modern science, including the social sciences. It is also of ever greater importance in daily life, as data of all sorts are now ubiquitous. Statistical literacy is hence of great value, both for academic purposes and for our daily routines. This course offers a solid foundation in statistical reasoning and its uses in the quantitative social sciences.
Learning statistics can be a daunting experience. There is a plethora of statistical concepts to master and many of them come with a hefty dose of mathematical notation. The goal of the present course is to develop a clear path through the conceptual forest and to explain each concept both in its narrow meaning and as a part of the larger enterprise of statistical reasoning. Mathematical skills are not taken for granted; instead, we shall review the necessary mathematical tools so that you will not get stuck on this aspect.
The field of statistics is sometimes divided into descriptive and inferential statistics, with probability theory forming a bridge between the two. In this course, we start out descriptively, by considering different ways in which we can learn from data. We then delve into the subject of probability theory, to end with a discussion of statistical inference. The emphasis in this part is on learning how to draw conclusions about populations with the help of data from a sample.
By the end of this course, you should have a good feeling for descriptive statistics, statistical inference, and probability theory. You should also understand the interplay of these elements in the broader enterprise of statistical reasoning. And you should feel more comfortable reading about statistics and using them in your own work.
This course follows on from Data Mining with Weka and provides a deeper account of data mining tools and techniques. Again the emphasis is on principles and practical data mining using Weka, rather than mathematical theory or advanced details of particular algorithms.
This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.
We are always using experiments to improve our lives, our community, and our work. Are you doing it efficiently? Or are you (incorrectly) changing one thing at a time and hoping for the best? In this course, you will learn how to plan efficient experiments - testing with many variables. Our goal is to find the best results using only a few experiments. A key part of the course is how to optimize a system.
Il corso copre la matematica di base, permettendo di colmare eventuali lacune e di mettere a punto la preparazione necessaria all'ingresso all'università. The course covers the fundamentals of Math, thus allowing to fill high school gaps and to optimize students’ knowledge as they start college.
Our world is rich with data sources, and technology makes data more accessible than ever before! To help ensure students are future ready to use data for making informed decisions, many countries around the world have increased the emphasis on statistics and data analysis in school curriculum–from elementary/primary grades through college. This course allows you to learn, along with colleagues from other schools, an investigation cycle to teach statistics and to help students explore data to make evidence-based claims.
This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power.
Use R to learn the fundamental statistical topic of basic inferential statistics. In the second part of a two part course, we’ll learn how to take data and use it to make reasonable and useful conclusions. You’ll learn the basics of statistical thinking – starting with an interesting question and some data.
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.