Master R For Statistics And Data Science

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

Requirements

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

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 approachHands-on exercises for practical experienceUnderstand data structures and data manipulation in R:VectorsMatricesData framesListsSubsetting dataapply() functions on matricesLearn about probability distributions and R's tools for probability.r functions for generating random variablesd functions for finding the probability of single eventsp functions for finding cumulative probabilitiesq functions for finding percentilesLearn 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 updatesTarget audience and pre-requisites:This course is designed for all levels, and assumes no prior knowledge of R.  Some experience programming or analyzing data is helpful, but we will build all knowledge from scratch!  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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Install R and RStudio

Section 2: Creating vectors and assignment

Lecture 3 Variables and assignment

Lecture 4 Variables and assignment coding assignment

Lecture 5 Vectors with c()

Lecture 6 Vectors with c() solution

Lecture 7 The colon :

Lecture 8 colon : coding solution

Lecture 9 seq()

Lecture 10 seq() exercise 1 solution

Lecture 11 seq() exercise 2 solution

Lecture 12 rep()

Lecture 13 rep() exercise 1 solution

Lecture 14 rep() exercise 2 solution

Section 3: Vectorized operations

Lecture 15 Introduction to vectorized operations

Lecture 16 Adding a number to all elements of a vector solution

Lecture 17 Converting Celsius to Fahrenheit solution

Lecture 18 Adding two vectors - weights of twins solution

Section 4: Basic functions on vectors

Lecture 19 Common functions in R

Lecture 20 mean() median() sum() length() solution

Lecture 21 sd() and var() solution

Lecture 22 summary() solution

Lecture 23 Missing data and na.rm

Section 5: Subsetting vectors

Lecture 24 Basics of subsetting

Section 6: Booleans

Lecture 25 Booleans

Lecture 26 Subsetting a vector with a boolean

Section 7: Matrix basics

Lecture 27 Creating matrices with cbind and rbind

Lecture 28 Creating matrices with matrix()

Section 8: Matrix subsetting

Lecture 29 Matrix subsetting

Lecture 30 Matrix subsetting with booleans

Section 9: Matrix operations

Lecture 31 apply()

Section 10: data frames

Lecture 32 Data frames

Section 11: lists

Lecture 33 Lists

Section 12: Generating random numbers and splitting training and testing data

Lecture 34 sample()

Lecture 35 Subsetting data randomly with sample()

Section 13: Bernoulli random variables

Lecture 36 Binary random variables, sample space

Lecture 37 Simulating Bernoulli random variables with rbinom()

Lecture 38 Parameters - The population proportion

Lecture 39 Sample statistics - mean() for calculating sample proportions

Lecture 40 dbinom()

Section 14: Binomial random variables

Lecture 41 Binomial random variables

Lecture 42 mean(rbinom()), Law of Large Numbers

Lecture 43 Estimating probabilities with mean() and rbinom()

Lecture 44 dbinom() intro

Lecture 45 Expected Value

Lecture 46 Variance and Standard Deviation

Lecture 47 cdf and pbinom()

Lecture 48 Other types of inequalities and intervals

Lecture 49 Visualizing the cdf

Lecture 50 The median

Lecture 51 qbinom()

Lecture 52 Problem-solving with qbinom()

Section 15: Binom.test

Lecture 53 binom.test

Section 16: Hypergeometric distribution

Lecture 54 Hypergeometric random variables

Section 17: Normal Distribution

Lecture 55 Normal random variables and the empirical rule

Lecture 56 Empirical rule with rnorm()

Lecture 57 dnorm() and continuous distributions

Lecture 58 pnorm() and the empirical rule

Lecture 59 qnorm()

Section 18: Sums, CLT, Normal Approximations

Lecture 60 Expected value of a sum

Lecture 61 Standard deviation of a sum

Lecture 62 Sums of normal random variable is normal

Lecture 63 Mean and sd of binomial distribution, Normal approximation to the binomial

Section 19: Probability: Geometric distribution

Lecture 64 Geometric random variables

Lecture 65 rgeom()

Lecture 66 dgeom()

Lecture 67 Expected value and standard deviation of geometric distribution

Lecture 68 pgeom(), the cdf

Lecture 69 qgeom()

Section 20: Probability: Negative Binomial distribution

Lecture 70 Negative binomial random variables

Lecture 71 rnbinom()

Lecture 72 dnbinom()

Lecture 73 Mean and standard deviation of negative binomial distribution

Lecture 74 pnbinom()

Lecture 75 qnbinom()

Section 21: Probability: Exponential Distribution

Lecture 76 Exponential random variables

Lecture 77 rexp()

Lecture 78 dexp()

Lecture 79 Expected value and sd of exponential distribution

Lecture 80 pexp() and memorylessness

Lecture 81 qexp()

Section 22: Probability: Gamma distribution

Lecture 82 Gamma distribution and rgamma()

Lecture 83 Expected value and standard deviation of gamma distribution

Lecture 84 dgamma() and pgamma()

Lecture 85 qgamma()

Lecture 86 Normal approximation to gamma distribution

Section 23: Probability: Poisson distribution

Lecture 87 Poisson distribution and rpois()

Lecture 88 dpois()

Lecture 89 Expected value and standard deviation

Lecture 90 ppois()

Lecture 91 qpois()

Lecture 92 Dealing with different time periods

Lecture 93 Normal approximation to the Poisson distribution

Section 24: Probability: Uniform distribution

Lecture 94 Uniform distribution, runif() and dunif()

Lecture 95 Mean and standard deviation of the uniform distribution

Lecture 96 punif()

Lecture 97 qunif() and the inverse transform method

Aspiring data analysts or data scientists who want to learn R