Master R for Statistics and Data Science

سرفصل های دوره

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

1. Introduction

1. Introduction

2. Install R and RStudio.html

2. Creating vectors and assignment

1. Variables and assignment

2. Variables and assignment.html

3. Variables and assignment coding assignment

4. Variables and assignment.html

5. Vectors with c()

6. vectors with c().html

7. Vectors with c() solution

8. The colon

9. Vector and colon quiz.html

10. colon .html

11. colon coding solution

12. seq()

13. seq() quiz.html

14. seq() exercise 1.html

15. seq() exercise 1 solution

16. seq() exercise 2.html

17. seq() exercise 2 solution

18. rep()

19. rep() quiz.html

20. rep() exercise 1.html

21. rep() exercise 1 solution

22. rep() exercise 2.html

23. rep() exercise 2 solution

3. Vectorized operations

1. Introduction to vectorized operations

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

5. Converting Celsius to Fahrenheit.html

6. Converting Celsius to Fahrenheit solution

7. Adding two vectors - weights of twins.html

8. Adding two vectors - weights of twins solution

4. Basic functions on vectors

1. Common functions in R

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

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

4. sd() and var().html

5. sd() and var() solution

6. summary().html

7. summary() solution

8. Missing data and na.rm

5. Subsetting vectors

1. Basics of subsetting

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

2. Boolean Quiz 1.html

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

4. Subsetting a vector with a boolean

7. Matrix basics

1. Creating matrices with cbind and rbind

2. cbind() and rbind().html

3. cbind and rbind quiz.html

4. Creating matrices with matrix()

5. matrix() quiz.html

6. matrix() 1.html

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

8. Matrix subsetting

1. Matrix subsetting

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

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

9. Matrix operations

1. apply()

2. apply() quiz 1.html

3. rowSums() and rowMeans().html

4. apply().html

10. data frames

1. Data frames

2. creating data frames.html

3. data frame quiz.html

11. lists

1. Lists

2. list quiz.html

12. Generating random numbers and splitting training and testing data

1. sample()

2. sample().html

3. sample() quiz.html

4. Subsetting data randomly with sample()

5. test and train data.html

6. testtrain quiz.html

13. Bernoulli random variables

1. Binary random variables, sample space

2. Simulating Bernoulli random variables with rbinom()

3. rbinom() exercise.html

4. Parameters - The population proportion

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

6. dbinom()

7. dbinom() exercise.html

14. Binomial random variables

1. Binomial random variables

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

3. Bernoulli trials - counting successes and failures.html

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

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

6. dbinom() intro

7. dbinom().html

8. Expected Value

9. Variance and Standard Deviation

10. cdf and pbinom()

11. pbinom().html

12. Other types of inequalities and intervals

13. Visualizing the cdf

14. The median

15. qbinom()

16. qbinom().html

17. Problem-solving with qbinom()

15. Binom.test

1. binom.test

2. binom.test.html

16. Hypergeometric distribution

1. Hypergeometric random variables

17. Normal Distribution

1. Normal random variables and the empirical rule

2. Empirical rule with rnorm()

3. dnorm() and continuous distributions

4. pnorm() and the empirical rule

5. qnorm()

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

2. Standard deviation of a sum

3. Sums of normal random variable is normal

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

19. Probability Geometric distribution

1. Geometric random variables

2. rgeom()

3. dgeom()

4. dgeom().html

5. Expected value and standard deviation of geometric distribution

6. pgeom(), the cdf

7. pgeom().html

8. qgeom()

9. qgeom() coding exercise.html

20. Probability Negative Binomial distribution

1. Negative binomial random variables

2. rnbinom()

3. dnbinom()

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

5. Mean and standard deviation of negative binomial distribution

6. pnbinom()

7. qnbinom()

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

21. Probability Exponential Distribution

1. Exponential random variables

2. rexp()

3. rexp() coding exercise.html

4. dexp()

5. Expected value and sd of exponential distribution

6. pexp() and memorylessness

7. pexp() coding exercise.html

8. qexp()

9. qexp() coding exercise.html

22. Probability Gamma distribution

1. Gamma distribution and rgamma()

2. Expected value and standard deviation of gamma distribution

3. dgamma() and pgamma()

4. qgamma()

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

6. Normal approximation to gamma distribution

7. Normal approximation to gamma distribution coding exercise.html

23. Probability Poisson distribution

1. Poisson distribution and rpois()

2. rpois() coding exercise.html

3. dpois()

4. dpois() coding exercise.html

5. Expected value and standard deviation

6. ppois()

7. ppois() coding exercise.html

8. qpois()

9. qpois() coding exercise.html

10. Dealing with different time periods

11. Normal approximation to the Poisson distribution

12. Poisson Normal Approximation coding exercise.html

24. Probability Uniform distribution

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

2. Mean and standard deviation of the uniform distribution

3. punif()

4. qunif() and the inverse transform method

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

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