EdX

Probability: Distribution Models & Continuous Random Variables (edX)

Offered by Purdue University, PurdueX,
Probability: Distribution Models & Continuous Random Variables (edX)

Learn about probability distribution models, including normal distribution, and continuous random variables to prepare for a career in information and data science. In this statistics and data analysis course, you will learn about continuous random variables and some of the most frequently used probability distribution models including, exponential distribution, Gamma distribution, Beta distribution, and most importantly, normal distribution.

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

You will learn how these distributions can be connected with the Normal distribution by Central limit theorem (CLT). We will discuss Markov and Chebyshev inequalities, order statistics, moment generating functions and transformation of random variables.
This course along with the recommended pre-requisite, Probability: Basic Concepts & Discrete Random Variables, will you give the skills and knowledge to progress towards an exciting career in information and data science.

What you'll learn:

  • Probability concepts and rules
  • Some of the most widely used probability models with continuous random variables
  • How distribution models we have encountered connect with Normal distribution
  • Advanced probability topics

Syllabus

**Units 1 - 6 are available in 416.1x Probability: Basic Concepts & Discrete Random Variables

Unit 7: Continuous Random Variables**
In this unit, we start from the instruction of continuous random variables, then discuss the joint density/CDF and properties of independent continuous random variables.

Unit 8: Conditional Distributions and Expected Values
Conditional distributions for continuous random variables, expected values of continuous random variables, and expected values of functions of random variables.

Unit 9: Models of Continuous Random Variables
In this unit we will discuss four common distribution models of continuous random variables: Uniform, Exponential, Gamma and Beta distributions.

Unit 10: Normal Distribution and Central Limit Theorem (CLT)
Introduction to Normal distribution and CLT, as well as examples of how CLT can be used to approximate models of continuous uniform, Gamma, Binomial, Bernoulli and Poisson.

Unit 11: Covariance, Conditional Expectation, Markov and Chebychev Inequalities

Unit 12: Order Statistics, Moment Generating Functions, Transformation of RVs

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

MathTrackX: Statistics (edX) EdX
University of Adelaide,AdelaideX

MathTrackX: Statistics (edX)

Sci: Mathematics

Understand fundamental concepts relating to statistical inference and how they can be applied to solve real world problems. This course will build on probability and random variable knowledge gained from previous courses in the MathTrackX XSeries with the study of statistical inference, one of the most important parts of statistics.

Self Paced
Self-Paced
Math Statistics Statistical Inference
Details
Introduction to Data Science (edX) EdX
IBM

Introduction to Data Science (edX)

Statistics & Data Analysis Computer Science

Learn about the world of data science first-hand from real data scientists. The art of uncovering the insights and trends in data has been around for centuries. The ancient Egyptians applied census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year.

Self Paced
Self-Paced
Data Science Data Scientist IBM Data Science Professional Certificate
Details
Analyzing and Visualizing Data with Power BI (edX) EdX
Davidson College,DavidsonX

Analyzing and Visualizing Data with Power BI (edX)

Statistics & Data Analysis Computer Science

Step up your analytics game and learn one of the most in-demand job skills in the United States. Power BI is a robust business analytics and visualization tool from Microsoft that helps data professionals bring their data to life and tell more meaningful stores. This four-week course is a beginner's guide to working with data in Power BI and is perfect for professionals. You'll become confident in working with data, creating data visualizations, and preparing reports and dashboards.

Self Paced
Self-Paced
Data Analysis Data Visualization Power BI
Details
Data Science: R Basics (edX) EdX
HarvardX,Harvard University

Data Science: R Basics (edX)

Statistics & Data Analysis Data Science

Build a foundation in R and learn how to wrangle, analyze, and visualize data. This course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

Self Paced
Self-Paced
Probability Machine Learning Inference
Details
Case Studies in Functional Genomics (edX) EdX
HarvardX,Harvard University

Case Studies in Functional Genomics (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor. We will explain how to perform the standard processing and normalization steps, starting with raw data, to get to the point where one can investigate relevant biological questions.

Self Paced
Self-Paced
Data Analysis Biological Data Genomics
Details
Probability and Statistics in Data Science using Python (edX) EdX
University of California, San Diego,UC San DiegoX

Probability and Statistics in Data Science using Python (edX)

Statistics & Data Analysis CS: Programming

Using Python, learn statistical and probabilistic approaches to understand and gain insights from data. The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

Self Paced
Self-Paced
Programming Python Statistics
Details
Técnicas Cuantitativas y Cualitativas para la Investigación (edX) EdX
Universitat Politècnica de València,UPValenciaX

Técnicas Cuantitativas y Cualitativas para la Investigación (edX)

Statistics & Data Analysis

El curso pretende acercar al alumno al método científico y, en concreto, cómo éste se aplica al estudio y análisis de los métodos de casos. El curso que se propone es ideal para investigadores y alumnos que se encuentren cursando trabajos de fin de grado, trabajos de fin de máster o realizando tesis, así como todos aquellos del área de la administración que quieran realizar un análisis cuantitativo o cualitativo en sus estudios.

Self Paced
Self-Paced
Research Methods Data Analysis Scientific Methods
Details
BioStatistics (edX) EdX
DoaneX,Doane University

BioStatistics (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

This college-level, credit-eligible Biostatistics course will teach you the skills required for success in future analytical studies in biology. In this undergraduate-level biostatistics course, the learners will be introduced to the use of statistics and study designs in biology. Upon successful completion of this course, learners will be able to design experimental, quasi-experimental and observational studies that will meet regulatory guidelines; collect, analyze, and interpret data using appropriate statistical tools.

Self Paced
Self-Paced
Biology Statistics Biostatistics
Details
Data Science Ethics (edX) EdX
University of Michigan,MichiganX

Data Science Ethics (edX)

CS: Software Engineering Engineering

Learn how to think through the ethics surrounding privacy, data sharing, and algorithmic decision-making. As patients, we care about the privacy of our medical record; but as patients, we also wish to benefit from the analysis of data in medical records. As citizens, we want a fair trial before being punished for a crime; but as citizens, we want to stop terrorists before they attack us. As decision-makers, we value the advice we get from data-driven algorithms; but as decision-makers, we also worry about unintended bias.

Self Paced
Self-Paced
Ethics Decision Making Privacy
Details
Data Science: Probability (edX) EdX
HarvardX,Harvard University

Data Science: Probability (edX)

Statistics & Data Analysis Data Science

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008. In this course, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007–2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.

Self Paced
Self-Paced
Probability Monte Carlo Method Data Science
Details