EdX

Statistics for Business Analytics: Probability (edX)

Statistics for Business Analytics: Probability (edX)

This is a great course for anyone who wants to gain foundational and critical analysis and statistics skills with no prior background. In this course, we explore the different ways of determining the probability of different events and outcomes. We want to be able to answer questions like: what is the probability that more than 90% of patients will show up for their appointments at a medical practice? what are the chances of a store having more than 20 customers in its first two hours of being open? how likely is it that a staff member will use 6 or fewer days of sick leave in a year?

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

This course is divided into three topics:

  • In Topic 1, we talk about the basics of probability – ideas like whether or not events can happen together, whether events can influence each other, and processes for calculating probabilities in simple situations.
  • Topic 2 is about conditional probability ; that is, the idea that we can become more certain about particular outcomes when we know the circumstances under which they are occurring.
  • In Topic 3, we discuss the "big 5" probability distributions ; powerful tools that can be used to calculate probabilities in a variety of complex situations. These distributions form the "heart" of the course and have practical applications in a wide range of real-life scenarios.

This course is part of the Statistics for Business Analytics Professional Certificate.

What you'll learn
Upon successful completion of this course, you will be able to:

  • Describe the basic concepts of probability.
  • Calculate basic probabilities for mutually exclusive, non-mutually exclusive and multiple independent events.
  • Calculate conditional probabilities in simple and complex situations.
  • Determine the probability of a given number of successes for discrete variables using binomial, Poisson and hypergeometric distributions.
  • Determine the probability of outcomes above or below a threshold for continuous variables using normal and exponential distributions.
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Data Science: Inference and Modeling (edX) EdX
HarvardX,Harvard University

Data Science: Inference and Modeling (edX)

Learn inference and modeling, two of the most widely used statistical tools in data analysis. Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting.

Self Paced
Self-Paced
Introducción a Ciencias de Datos y Estadística Básica para Negocios (edX) EdX
Tecnológico de Monterrey,TecdeMonterreyX

Introducción a Ciencias de Datos y Estadística Básica para Negocios (edX)

En este curso adquirirás los métodos estadísticos para la toma de decisiones en los negocios, así como herramientas tecnológicas para desarrollar habilidades cuantitativas. Áreas como el “big data” requieren un conocimiento muy claro de la estadística; en las áreas de negocios, la tecnología pone a nuestro alcance diversas aplicaciones que requieren una sólida formación en estadística para su correcto uso e interpretación.

Self Paced
Self-Paced
Introduction to Data Science and Basic Statistics for Business (edX) EdX
Tecnológico de Monterrey,TecdeMonterreyX

Introduction to Data Science and Basic Statistics for Business (edX)

In this course you will acquire statistical methods for decision making in business, as well as technological tools to develop quantitative skills. Areas such as " big data" require very clear knowledge of statistics and business, technology provides us various applications that require solid training in statistics for proper use and interpretation .

Self Paced
Self-Paced
Statistics (edX) EdX
Delft University of Technology,DelftX

Statistics (edX)

This course provides an overview of bachelor-level statistics. You will review the concepts of descriptive and inferential statistics. You will use the statistical software package R on real data to gain insight in these topics. A strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.

Self Paced
Self-Paced
Statistics and R (edX) EdX
HarvardX,Harvard University

Statistics and R (edX)

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation.

Self Paced
Self-Paced
Data Science: Capstone (edX) EdX
HarvardX,Harvard University

Data Science: Capstone (edX)

Show what you’ve learned from the Professional Certificate Program in Data Science. To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Self Paced
Self-Paced
MathTrackX: Probability (edX) EdX
University of Adelaide,AdelaideX

MathTrackX: Probability (edX)

Understand probability and how it manifests in the world around us. This course introduces probability and how it manifests in the world around us. Beginning with discrete random variables, together with their uses in modelling random processes involving chance and variation, you will start to uncover the framework for statistical inference.

Self Paced
Self-Paced