Master R for Statistics and Data Science

Focused View

Brian Greco

5:53:07

5 View

1. Introduction

1. Introduction.mp4

00:54

2. Install R and RStudio.html

2. Creating vectors and assignment

1. Variables and assignment.mp4

02:05

2. Variables and assignment.html

3. Variables and assignment coding assignment.mp4

01:24

4. Variables and assignment.html

5. Vectors with c().mp4

01:52

6. vectors with c().html

7. Vectors with c() solution.mp4

00:37

8. The colon .mp4

01:06

9. Vector and colon quiz.html

10. colon .html

11. colon coding solution.mp4

00:31

12. seq().mp4

02:35

13. seq() quiz.html

14. seq() exercise 1.html

15. seq() exercise 1 solution.mp4

00:42

16. seq() exercise 2.html

17. seq() exercise 2 solution.mp4

00:41

18. rep().mp4

02:25

19. rep() quiz.html

20. rep() exercise 1.html

21. rep() exercise 1 solution.mp4

00:53

22. rep() exercise 2.html

23. rep() exercise 2 solution.mp4

00:53

3. Vectorized operations

1. Introduction to vectorized operations.mp4

02:41

2. Vectorized operations quiz.html

3. Adding a number to all elements of a vector.html

4. Adding a number to all elements of a vector solution.mp4

01:01

5. Converting Celsius to Fahrenheit.html

6. Converting Celsius to Fahrenheit solution.mp4

01:30

7. Adding two vectors - weights of twins.html

8. Adding two vectors - weights of twins solution.mp4

01:13

4. Basic functions on vectors

1. Common functions in R.mp4

04:14

2. mean(), median(), sum(), length().html

3. mean() median() sum() length() solution.mp4

01:34

4. sd() and var().html

5. sd() and var() solution.mp4

01:04

6. summary().html

7. summary() solution.mp4

01:51

8. Missing data and na.rm.mp4

01:30

5. Subsetting vectors

1. Basics of subsetting.mp4

03:33

2. Subsetting quiz 1.html

3. subsetting.html

4. subsetting with c().html

5. subsetting with .html

6. removing elements with -.html

6. Booleans

1. Booleans.mp4

05:33

2. Boolean Quiz 1.html

3. ==, , , =, =.html

4. Subsetting a vector with a boolean.mp4

01:51

7. Matrix basics

1. Creating matrices with cbind and rbind.mp4

03:12

2. cbind() and rbind().html

3. cbind and rbind quiz.html

4. Creating matrices with matrix().mp4

03:37

5. matrix() quiz.html

6. matrix() 1.html

7. dim(), nrow(), ncol() quiz.html

8. Matrix subsetting

1. Matrix subsetting.mp4

02:47

2. Matrix subsetting quiz 1.html

3. matrix subsetting rows and columns.html

4. matrix subsetting, only certain columns.html

5. matrix subsetting, only certain rows.html

6. Matrix subsetting with booleans.mp4

02:27

7. matrix subsetting, rows that meet a boolean criterion.html

9. Matrix operations

1. apply().mp4

05:10

2. apply() quiz 1.html

3. rowSums() and rowMeans().html

4. apply().html

10. data frames

1. Data frames.mp4

03:06

2. creating data frames.html

3. data frame quiz.html

11. lists

1. Lists.mp4

03:44

2. list quiz.html

12. Generating random numbers and splitting training and testing data

1. sample().mp4

02:25

2. sample().html

3. sample() quiz.html

4. Subsetting data randomly with sample().mp4

03:56

5. test and train data.html

6. testtrain quiz.html

13. Bernoulli random variables

1. Binary random variables, sample space.mp4

05:50

2. Simulating Bernoulli random variables with rbinom().mp4

08:47

3. rbinom() exercise.html

4. Parameters - The population proportion.mp4

05:07

5. Sample statistics - mean() for calculating sample proportions.mp4

04:38

6. dbinom().mp4

03:32

7. dbinom() exercise.html

14. Binomial random variables

1. Binomial random variables.mp4

09:27

2. mean(rbinom()), Law of Large Numbers.mp4

03:45

3. Bernoulli trials - counting successes and failures.html

4. Generating Binomial random variables with rbinom().html

5. Estimating probabilities with mean() and rbinom().mp4

03:07

6. dbinom() intro.mp4

01:55

7. dbinom().html

8. Expected Value.mp4

08:33

9. Variance and Standard Deviation.mp4

08:35

10. cdf and pbinom().mp4

09:22

11. pbinom().html

12. Other types of inequalities and intervals.mp4

06:43

13. Visualizing the cdf.mp4

03:10

14. The median.mp4

06:25

15. qbinom().mp4

09:12

16. qbinom().html

17. Problem-solving with qbinom().mp4

04:24

15. Binom.test

1. binom.test.mp4

03:36

2. binom.test.html

16. Hypergeometric distribution

1. Hypergeometric random variables.mp4

12:00

17. Normal Distribution

1. Normal random variables and the empirical rule.mp4

09:29

2. Empirical rule with rnorm().mp4

04:34

3. dnorm() and continuous distributions.mp4

10:51

4. pnorm() and the empirical rule.mp4

01:17

5. qnorm().mp4

07:40

6. rnorm(), mean, and sd.html

7. pnorm().html

8. dnorm() and plotting, continuous distributions.html

9. qnorm().html

10. Normal approximation to the binomial distribution.html

11. prop.test() one sample.html

12. prop.test two sample.html

13. prop.test two sample example.html

18. Sums, CLT, Normal Approximations

1. Expected value of a sum.mp4

03:51

2. Standard deviation of a sum.mp4

03:05

3. Sums of normal random variable is normal.mp4

01:05

4. Mean and sd of binomial distribution, Normal approximation to the binomial.mp4

04:01

19. Probability Geometric distribution

1. Geometric random variables.mp4

03:58

2. rgeom().mp4

01:52

3. dgeom().mp4

05:29

4. dgeom().html

5. Expected value and standard deviation of geometric distribution.mp4

04:45

6. pgeom(), the cdf.mp4

03:27

7. pgeom().html

8. qgeom().mp4

03:52

9. qgeom() coding exercise.html

20. Probability Negative Binomial distribution

1. Negative binomial random variables.mp4

04:29

2. rnbinom().mp4

02:35

3. dnbinom().mp4

05:38

4. dnbinom(), pnbinom(), qnbinom() coding exercise.html

5. Mean and standard deviation of negative binomial distribution.mp4

02:10

6. pnbinom().mp4

03:05

7. qnbinom().mp4

06:13

8. Normal approximation to the negative binomial coding exercise.html

21. Probability Exponential Distribution

1. Exponential random variables.mp4

04:05

2. rexp().mp4

02:39

3. rexp() coding exercise.html

4. dexp().mp4

01:12

5. Expected value and sd of exponential distribution.mp4

03:35

6. pexp() and memorylessness.mp4

04:52

7. pexp() coding exercise.html

8. qexp().mp4

01:02

9. qexp() coding exercise.html

22. Probability Gamma distribution

1. Gamma distribution and rgamma().mp4

04:18

2. Expected value and standard deviation of gamma distribution.mp4

02:34

3. dgamma() and pgamma().mp4

02:19

4. qgamma().mp4

01:21

5. pgamma() and qgamma() coding exercise.html

6. Normal approximation to gamma distribution.mp4

04:46

7. Normal approximation to gamma distribution coding exercise.html

23. Probability Poisson distribution

1. Poisson distribution and rpois().mp4

08:06

2. rpois() coding exercise.html

3. dpois().mp4

01:57

4. dpois() coding exercise.html

5. Expected value and standard deviation.mp4

02:08

6. ppois().mp4

01:30

7. ppois() coding exercise.html

8. qpois().mp4

01:15

9. qpois() coding exercise.html

10. Dealing with different time periods.mp4

03:34

11. Normal approximation to the Poisson distribution.mp4

03:45

12. Poisson Normal Approximation coding exercise.html

24. Probability Uniform distribution

1. Uniform distribution, runif() and dunif().mp4

04:33

2. Mean and standard deviation of the uniform distribution.mp4

00:58

3. punif().mp4

01:26

4. qunif() and the inverse transform method.mp4

05:01

5. punif() and qunif() coding exercise.html

6. Inverse transform coding exercise.html

7. rchisq(), pchisq(), qchisq().html

8. table().html

9. chi-square goodness of fit test by hand.html

10. chisq.test.html

11. aov().html

12. filter().html

13. select().html

14. glm().html

15. accuracy.html

16. precision and recall.html

17. 1 sample t test by hand.html

18. t.test() one sample.html

19. 1 mean t interval.html

20. 1 proportion interval, binom.test, prop.test.html

21. lm().html

22. predict.html

23. MSE.html

Description

A former Google data scientist teaches you R starting with the basics, and learning common tools for data science.

What You'll Learn?

Master the basic parts of R like vectors and matrices
Learn more complex data structures like data frames and lists
Learn R's probability functions for simulating data and calculating probabilities
Practice these skills using Udemy's built-in coding exercises

Who is this for?

Aspiring data analysts or data scientists who want to learn R

What You Need to Know?

Some experience in programming or statistics is helpful, but no prior knowledge is assumed.

More details

Description
This comprehensive R course starts from the very basics, covering vectors, matrices, data frames, and more, ensuring a solid foundation for beginners.
Start your journey to becoming an R expert today!

Key Features:
Learn R from scratch with a step-by-step approach
Hands-on exercises for practical experience
Understand data structures and data manipulation in R:
Vectors
Matrices
Data frames
Lists
Subsetting data
apply() functions on matrices
Learn about probability distributions and R's tools for probability.
r functions for generating random variables
d functions for finding the probability of single events
p functions for finding cumulative probabilities
q functions for finding percentiles
Learn about common probability distributions commonly used in data science, including the binomial, geometric, exponential, normal, Poisson, gamma, and uniform distributions.
Lifetime access to course materials and updates

Target audience and pre-requisites:
This course is designed for all levels, and assumes no prior knowledge of R.Â Some experience programming or analyzing data is helpful, but we will build all knowledge from scratch!Â
Some sections, especially in the second half of the course, will assume a foundation in basic algebra and arithmetic.

Start with the fundamentals of R programming, and gain proficiency in R to position yourself as a skilled data scientist.
Who this course is for:
Aspiring data analysts or data scientists who want to learn 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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