Probability with R for Statistics and Data Science

Focused View

Brian Greco

4:48:31

25 View

1 - Introduction

1 - Introduction.mp4

01:29

2 - Download R and RStudio.txt

2 - R Basics.mp4

06:18

2 - Bernoulli Random Variables

3 - Binary Random Variables Sample Space.mp4

05:50

4 - Simulating Bernoulli random variables with rbinom.mp4

08:47

5 - Parameters The population proportion.mp4

05:07

6 - Sample statistics mean for calculating sample proportions.mp4

04:38

7 - dbinom.mp4

03:32

3 - Binomial Random Variables

8 - Binomial random variables.mp4

09:27

9 - meanrbinom Law of Large Numbers.mp4

03:45

10 - Estimating probabilities with mean and rbinom.mp4

03:07

11 - dbinom intro.mp4

01:55

12 - Expected Value.mp4

08:33

13 - Variance and Standard Deviation.mp4

08:35

14 - cdf and pbinom.mp4

09:40

15 - Other types of inequalities and intervals.mp4

06:43

16 - Visualizing the cdf.mp4

03:10

17 - The median.mp4

06:25

18 - qbinom.mp4

09:12

19 - Problemsolving with qbinom.mp4

04:24

4 - Hypergeometric Random Variables

20 - Hypergeometric random variables.mp4

12:00

5 - Normal Random Variables

21 - Normal Random Variables and the empirical rule.mp4

09:29

22 - Empirical rule with rnorm.mp4

04:34

23 - dnorm and probability density functions pdfs.mp4

10:51

24 - pnorm.mp4

01:17

25 - qnorm.mp4

07:40

6 - Sums CLT Normal Approximations

26 - Expected value of sum.mp4

03:45

27 - Variance and standard deviation of sum.mp4

02:55

28 - The sum of normal random variables is a normal random variable.mp4

01:05

29 - Central Limit Theorem Normal Approximation to the Binomial Distribution.mp4

04:01

7 - Geometric Random Variables

30 - Geometric Random Variables.mp4

03:58

31 - rgeom.mp4

01:52

32 - dgeom and the pmf of geometric random variables.mp4

05:29

33 - Expected Value and Standard Deviation.mp4

04:45

34 - CDFs and pgeom.mp4

03:27

35 - Inverse cdf quantiles qgeom.mp4

03:52

8 - Negative Binomial Random Variables

36 - Negative Binomial Random Variables.mp4

04:29

37 - rnbinom.mp4

02:35

38 - dnbinom.mp4

05:38

39 - Mean and standard deviation.mp4

02:10

40 - pnbinom.mp4

03:05

41 - qnbinom.mp4

06:13

42 - Normal Approximations to the negative binomial.mp4

05:48

9 - Exponential Random Variables

43 - Exponential Random Variables.mp4

04:05

44 - rexp.mp4

02:39

45 - dexp.mp4

01:12

46 - Expected value and standard deviation.mp4

03:35

47 - pexp and memorylessness.mp4

04:52

48 - qexp.mp4

01:02

10 - Gamma Random Variables

49 - Gamma Random Variables and rgamma.mp4

04:18

50 - Expected Value and Standard Deviation.mp4

02:34

51 - dgamma and pgamma.mp4

02:19

52 - qgamma.mp4

01:21

53 - Normal approximation to the gamma distribution.mp4

04:46

11 - Poisson Random Variables

54 - Poisson Random Variables and rpois.mp4

08:06

55 - dpois.mp4

01:57

56 - Mean and Standard Deviation.mp4

02:08

57 - ppois.mp4

01:30

58 - qpois.mp4

01:15

59 - Different time periods Sums of Poisson Random Variables.mp4

03:34

60 - Normal Approximation.mp4

03:45

12 - Uniform Random Variables

61 - Uniform Random Variables dunif.mp4

04:33

62 - Mean and standard deviation.mp4

00:58

63 - punif.mp4

01:26

64 - qunif and the inverse transform method.mp4

05:01

Description

Master common probability distributions using the R programming language - with 100+ coding problems included.

What You'll Learn?

Learn how to use R to solve a wide variety of probability problems
Learn about important probability functions like the PMF, PDF, CDF, Inverse CDF, and how to use them to solve problems.
Master important probability distributions including Bernoulli, Binomial, Normal, Geometric, Hypergeometric, Exponential, Poisson, Negative Binomial, and Gamma
Learn how to simulate data to answer questions about probability distributions.

Who is this for?

Current and aspiring data scientists and data analysts

Anyone learning R and wanting to master important probability functions

Anybody wanting to learn probability in an innovative way through programming and R

What You Need to Know?

Strong skills in basic algebra and arithmetic

No knowledge of R is assumed, some experience with coding would be helpful

No prior knowledge of probability is assumed

More details

Description
This course offers an in-depth exploration of probability, shedding light on various statistical distributions using R's r/d/p/q functions.

The course includes:
5 hours of video lectures
30 coding exercises with over 100 problems, including detailed hints and step-by-step solutions, offering hands-on experience with R
You will learn about:
Discrete Distributions including the Bernoulli, Binomial, Hypergeometric, Geometric, Negative Binomial and Poisson distributions
The Normal distribution and the Central Limit Theorem
Other continuous distributions including the Exponential, Gamma, Poisson, and Uniform distributions.
Hands-on examples using R's r/d/p/q functions: generating random numbers, computing probabilities, medians, quantiles, and more.
Contextual understanding and application of probability mass functions (PMFs), probability density functions (PDFs), cumulative distribution functions (CDFs) , and inverse cumulative distribution functions (inverse CDFs/quantile functions)
The expected value and standard deviation of the probability distributions, and how to use these in applications involving the central limit theorem.
This course is perfect for:
Individuals aiming for a strong foundation in probability and the various probabilities distributions used in statistics and data science.
Current and aspiring data analysts and data scientists who wish to harness the potential of R for simplifying probability calculations.
Anybody who uses RÂ and wants to use R to learn probability quickly, using an innovative computer-centric approach.
Who this course is for:
Current and aspiring data scientists and data analysts
Anyone learning R and wanting to master important probability functions
Anybody wanting to learn probability in an innovative way through programming and R

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Focused display

I am a statistician, data scientist, and teacher. I've worked as a data scientist at Google, where I analyzed data from over 1 billion users, and I've created new methods for analyzing genetic sequencing data. But most importantly, I love helping others learn statistics and have been teaching at top universities and online for over ten years. My goal is to help you obtain a deep understanding of statistics and to make it as easy as possible!

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