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Our capacity to collect and store data has exponentially increased, but deriving information from data from a scientific perspective requires a foundational knowledge of probability.

Are you interested in a career in the emerging data science field, or as an actuarial scientist? Or want better to understand statistical theory and mathematical modeling?

In this statistics and data analysis course, we will provide an introduction to mathematical probability to help meet your career goals in the exciting new areas becoming known as information science.

In this course, we will first introduce basic probability concepts and rules, including Bayes theorem, probability mass functions and CDFs, joint distributions and expected values.

Then we will discuss a few important probability distribution models with discrete random variables, including Bernoulli and Binomial distributions, Geometric distribution, Negative Binomial distribution, Poisson distribution, Hypergeometric distribution and discrete uniform distribution.

To continue learning about probability, enroll in Probability: Distribution Models & Continuous Random Variables, which covers continuous distribution models, central limit theorem and more.

The Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information science.

  • Basic probability concepts and rules
  • Some of the most widely used probability models with discrete random variables
  • How probability models work in practical problems

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前提

Calculus (including double integrals) & Basic Combinatorics (Lesson 1 of this course gives a combinatorics tutorial)

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课程大纲

Unit 1: Sample Space and Probability
Introduction to basic concepts, such as outcomes, events, sample spaces, and probability.

Unit 2: Independent Events, Conditional Probability and Bayes’ Theorem
Introduction to independent events, conditional probability and Bayes’ Theorem with examples.

Unit 3: Random Variables
Random variables, probability mass functions and CDFs, joint distributions.

Unit 4: Expected Values
In this unit, we will discuss expected values of discrete random variables, sum of random variables and functions of random variables with lots of examples.

Unit 5: Models of Discrete Random Variables I
Bernoulli and Binomial random variables; Geometric random variables; Negative Binomial random variables.

Unit 6: Models of Discrete Random Variables II
Poisson random variables; Hypergeometric random variables; discrete uniform random variables and counting.
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教师

Mark D. Ward
Professor
Purdue University

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内容设计师

Purdue University

Purdue University is a world-renowned, public research university that advances discoveries in science, technology, engineering and math.

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平台

Edx

EdX est une plateforme d'apprentissage en ligne (dite FLOT ou MOOC). Elle héberge et met gratuitement à disposition des cours en ligne de niveau universitaire à travers le monde entier. Elle mène également des recherches sur l'apprentissage en ligne et la façon dont les utilisateurs utilisent celle-ci. Elle est à but non lucratif et la plateforme utilise un logiciel open source.

EdX a été fondée par le Massachusetts Institute of Technology et par l'université Harvard en mai 2012. En 2014, environ 50 écoles, associations et organisations internationales offrent ou projettent d'offrir des cours sur EdX. En juillet 2014, elle avait plus de 2,5 millions d'utilisateurs suivant plus de 200 cours en ligne.

Les deux universités américaines qui financent la plateforme ont investi 60 millions USD dans son développement. La plateforme France Université Numérique utilise la technologie openedX, supportée par Google.

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