Probability
Distribution Models & Continuous Random Variables
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.
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.
The Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information 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
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| Rating | Not enough ratings |
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| Length | 6 weeks |
| Effort | 6 weeks, 4–6 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $49 |
| From | Purdue University, PurdueX via edX |
| Instructor | Mark D. Ward |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science Mathematics |
| Tags | Computer Science Data Analysis & Statistics Math |
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| Rating | Not enough ratings |
|---|---|
| Length | 6 weeks |
| Effort | 6 weeks, 4–6 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $49 |
| From | Purdue University, PurdueX via edX |
| Instructor | Mark D. Ward |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science Mathematics |
| Tags | Computer Science Data Analysis & Statistics Math |
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