Is your relationship with statistics dysfunctional? We can help: Get to know stats, build a healthy bond, and maybe even fall in love!
When you meet a new person, it is hard to know what to expect. You may not be able to read the person or understand what they mean. Even if you want to have a good relationship with them, this lack of understanding can make interactions tense, unpredictable and scary! The same is true for a lot of people as they encounter statistics and mathematical ways of working with data. Statistics can be confusing and opaque. Symbols, Greek letters, very large and very small numbers, and how to interpret all of this can leave to feeling cold and disengaged—even fearful and resentful.
But in the modern information age, having a healthy relationship with statistics can make life a whole lot easier. We are constantly faced with an onslaught of data and claims about it—from news articles, to Facebook and blog posts, casual and professional conversations, reports at our workplace, advertising, and claims from politicians and public officials. How can we process that information, make sense of it, evaluate truth claims, and put ourselves in a position to act on the information? One of the most important ways is by befriending statistics and consistently using statistical ways of thinking.
The purpose of this course, then is to help you develop a functional, satisfying, and useful life-long relationship with statistics. To achieve that goal, we will take a non-technical approach—you will learn how statistics work and why they are so helpful in evaluating the world of information that is around us. You will learn about the logic of statistical thinking and the concepts (rather than the mathematical details and probability theory) that guide statistical inferences and conclusions.
You do not need to be a math whiz to take this course. If you can add, subtract, multiply, and divide (or just be able to use a calculator to do that!), you will be more than able to handle what will happen as this relationship develops.
But by the end of the course you will be able to:
- Identify the most important features of a data set
- Select a statistical test based on the features of the data
- Think like a statistical detective
- Understand the relationship between two different characteristics or variables
- Perform some simple statistical calculations and draw some conclusions from real data
- Hopefully, love stats!
We’ll do all of this using entertaining examples related to real-life situations we all encounter in everyday life.
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.
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.
Financial Engineering is a multidisciplinary field involving finance and economics, mathematics, statistics, engineering and computational methods. The emphasis of FE & RM Part II will be on the use of simple stochastic models to (i) solve portfolio optimization problems (ii) price derivative securities in various asset classes including equities and credit and (iii) consider some advanced applications of financial engineering including algorithmic trading and the pricing of real options. We will also consider the role that financial engineering played during the financial crisis.
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.