R Programming For Statistics And Data Science 2023

R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R

What you'll learn

Learn the fundamentals of programming in R

Work with R’s conditional statements, functions, and loops

Build your own functions in R

Get your data in and out of R

Learn the core tools for data science with R

Manipulate data with the Tidyverse ecosystem of packages

Systematically explore data in R

The grammar of graphics and the ggplot2 package

Visualise data: plot different types of data & draw insights

Transform data: best practices of when and how

Index, slice, and subset data

Learn the fundamentals of statistics and apply them in practice

Hypothesis testing in R

Understand and carry out regression analysis in R

Work with dummy variables

Learn to make decisions that are supported by the data!

Have fun by taking apart Star Wars and Pokemon data, as well some more serious data sets

Requirements

You’ll need to install R Studio. We will show you how to do it in one of the first lectures of the course

All software and data used in the course are free.

Description

R Programming for Statistics and Data Science 2023R Programming is a skill you need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn't you?  Data scientist is the hottest ranked profession in the US.But to do that, you need the tools and the skill set to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream title.   This course is packing all of this, and more, in one easy-to-handle bundle, and it’s the perfect start to your journey.   So, welcome to R for Statistics and Data Science!   R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others’.    Laying strong foundations    This course wastes no time and jumps right into hands-on coding in R. But don’t worry if you have never coded before, we start off light and teach you all the basics as we go along! We wanted this to be an equally satisfying experience for both complete beginners and those of you who would just like a refresher on R.What makes this course different from other courses?   Well-paced learning.Receive top class training with content which we’ve built - and rigorously edited - to deliver powerful and efficient results.    Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that sticks.    Introductory guide to statistics.We will take you through descriptive statistics and the fundamentals of inferential statistics.   We will do it in a step-by-step manner, incrementally building up your theoretical knowledge and practical skills.     You’ll master confidence intervals and hypothesis testing, as well as regression and cluster analysis.   The essentials of programming – R-based.Put yourself in the shoes of a programmer, rise above the average data scientist and boost the productivity of your operations.   Data manipulation and analysis techniques in detail.Learn to work with vectors, matrices, data frames, and lists.    Become adept in ‘the Tidyverse package’ - R’s most comprehensive collection of tools for data manipulation – enabling you to index and subset data, as well as spread(), gather(), order(), subset(), filter(), arrange(), and mutate() it.   Create meaning-heavy data visualizations and plots.   Practice makes perfect.Reinforce your learning through numerous practical exercises, made with love, for you, by us.What about homework, projects, & exercises?   There is a ton of homework that will challenge you in all sorts of ways. You will have the chance to tackle the projects by yourself or reach out to a video tutorial if you get stuck.You: Is there something to show for the skills I will acquire?Us: Indeed, there is – a verifiable certificate.   You will receive a verifiable certificate of completion with your name on it. You can download the certificate and attach it to your CV and even post it on your LinkedIn profile to show potential employers you have experience in carrying out data manipulations & analysis in R.    If that sounds good to you, then welcome to the classroom :)

Overview

Section 1: Introduction

Lecture 1 Ten Things You Will Learn in This Course

Section 2: Getting started

Lecture 2 Intro

Lecture 3 Downloading and installing R & RStudio

Lecture 4 Quick guide to the RStudio user interface

Lecture 5 Changing the appearance in RStudio

Lecture 6 Installing packages in R and using the library

Section 3: The building blocks of R

Lecture 7 Creating an object in R

Lecture 8 Exercise 1 Creating an object in R

Lecture 9 Data types in R - Integers and doubles

Lecture 10 Data types in R - Characters and logicals

Lecture 11 Exercise 2 Data types in R

Lecture 12 Coercion rules in R

Lecture 13 Exercise 3 Coercion rules in R

Lecture 14 Functions in R

Lecture 15 Exercise 4 Using functions in R

Lecture 16 Functions and arguments

Lecture 17 Exercise 5 The arguments of a function

Lecture 18 Building a function in R (basics)

Lecture 19 Exercise 6 Building a function in R

Lecture 20 Using the script vs. using the console

Section 4: Vectors and vector operations

Lecture 21 Intro

Lecture 22 Introduction to vectors

Lecture 23 Vector recycling

Lecture 24 Exercise 7 Vector recycling

Lecture 25 Naming a vector in R

Lecture 26 Exercise 8 Vector attributes - names

Lecture 27 Getting help with R

Lecture 28 Slicing and indexing a vector in R

Lecture 29 Exercise 9 Indexing and slicing a vector

Lecture 30 Changing the dimensions of an object in R

Lecture 31 Exercise 10 Vector attributes - dimensions

Section 5: Matrices

Lecture 32 Creating a matrix in R

Lecture 33 Faster code: creating a matrix in a single line of code

Lecture 34 Exercise 11 Creating a matrix in R

Lecture 35 Do matrices recycle?

Lecture 36 Indexing an element from a matrix

Lecture 37 Slicing a matrix in R

Lecture 38 Exercise 12 Indexing and slicing a matrix

Lecture 39 Matrix arithmetic

Lecture 40 Exercise 13 Matrix arithmetic

Lecture 41 Matrix operations in R

Lecture 42 Exercise 14 Matrix operations

Lecture 43 Categorical data

Lecture 44 Creating a factor in R

Lecture 45 Exercise 15 Creating a factor in R

Lecture 46 Lists in R

Lecture 47 Exercise: Lists in R

Lecture 48 Completed 33% of the course

Section 6: Fundamentals of programming with R

Lecture 49 Relational operators in R

Lecture 50 Logical operators in R

Lecture 51 Vectors and logicals operators

Lecture 52 Exercise Logical operators

Lecture 53 If, else, else if statements in R

Lecture 54 Exercise If, else, else if statements in R

Lecture 55 If, else, else if statements - Keep-In-Mind's

Lecture 56 For loops in R

Lecture 57 Exercise: For Loops in R

Lecture 58 While loops in R

Lecture 59 Exercise: While loops in R

Lecture 60 Repeat loops in R

Lecture 61 Building a function in R 2.0

Lecture 62 Building a function in R 2.0 - Scoping

Lecture 63 Exercise Scoping

Lecture 64 Completed 50% of the course

Section 7: Data frames

Lecture 65 Intro

Lecture 66 Creating a data frame in R

Lecture 67 Exercise 16 Creating a data frame in R

Lecture 68 The Tidyverse package

Lecture 69 Data import in R

Lecture 70 Importing a CSV in R

Lecture 71 Data export in R

Lecture 72 Exercise 17 Importing and exporting data in R

Lecture 73 Getting a sense of your data frame

Lecture 74 Indexing and slicing a data frame in R

Lecture 75 Extending a data frame in R

Lecture 76 Exercise 18 Data frame operations

Lecture 77 Dealing with missing data in R

Section 8: Manipulating data

Lecture 78 Intro

Lecture 79 Data transformation with R - the Dplyr package - Part I

Lecture 80 Data transformation with R - the Dplyr package - Part II

Lecture 81 Sampling data with the Dplyr package

Lecture 82 Using the pipe operator in R

Lecture 83 Exercise 19 Data transformation with Dplyr

Lecture 84 Tidying data in R - gather() and separate()

Lecture 85 Tidying data in R - unite() and spread()

Lecture 86 Exercise 20 Data tidying with Tidyr

Section 9: Visualizing data

Lecture 87 Intro

Lecture 88 Intro to data visualization

Lecture 89 Intro to ggplot2

Lecture 90 Variables: revisited

Lecture 91 Building a histogram with ggplot2

Lecture 92 Exercise 21 Building a histogram with ggplot2

Lecture 93 Building a bar chart with ggplot2

Lecture 94 Exercise 22 Building a bar chart with ggplot2

Lecture 95 Building a box and whiskers plot with ggplot2

Lecture 96 Exercise 23 Building a box plot with ggplot2

Lecture 97 Building a scatterplot with ggplot2

Lecture 98 Exercise 24 Building a scatterplot with ggplot2

Section 10: Exploratory data analysis

Lecture 99 Population vs. sample

Lecture 100 Mean, median, mode

Lecture 101 Skewness

Lecture 102 Exercise 25 Determining Skewness

Lecture 103 Variance, standard deviation, and coefficient of variability

Lecture 104 Covariance and correlation

Lecture 105 Exercise 26 Practical example with real estate data

Section 11: Hypothesis Testing

Lecture 106 Distributions

Lecture 107 Standard Error and Confidence Intervals

Lecture 108 Hypothesis testing

Lecture 109 Type I and Type II errors

Lecture 110 Test for the mean - population variance known

Lecture 111 Exercise: Test for the mean - population variance known

Lecture 112 The P-value

Lecture 113 Test for the mean - Population variance unknown

Lecture 114 Exercise: Test for the mean - population variance unknown

Lecture 115 Comparing two means - Dependent samples

Lecture 116 Exercise: Comparing two means - Dependent samples

Lecture 117 Comparing two means - Independent samples

Section 12: Linear Regression Analysis

Lecture 118 The linear regression model

Lecture 119 Correlation vs regression

Lecture 120 Geometrical representation

Lecture 121 First regression in R

Lecture 122 How to interpret the regression table

Lecture 123 Exercise: Doing a regression in R

Lecture 124 Decomposition of variability: SST, SSR, SSE

Lecture 125 R-squared

Lecture 126 Completed 100% of the course

Aspiring data scientists,Beginners to programming,People interested in statistics and data analysis,Anyone who wants to learn how to code and apply their skills in practice