R Programming for Statistics and Data Science 2020
4.6 (2,041 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
14,321 students enrolled

R Programming for Statistics and Data Science 2020

R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R
4.6 (2,041 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
14,321 students enrolled
Last updated 4/2020
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Current price: $135.99 Original price: $194.99 Discount: 30% off
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This course includes
  • 6.5 hours on-demand video
  • 38 articles
  • 34 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 125 lectures 06:41:00
+ Getting started
5 lectures 18:47
Intro
00:53
Quick guide to the RStudio user interface
07:37
RStudio's GUI
3 questions
Changing the appearance in RStudio
01:47
Installing packages in R and using the library
05:10
+ The building blocks of R
14 lectures 35:54
Creating an object in R
05:21
Exercise 1 Creating an object in R
00:27
Data types in R - Integers and doubles
04:40
Objects and Data Types
4 questions
Exercise 2 Data types in R
00:29
Coercion rules in R
02:39
Exercise 3 Coercion rules in R
00:21
Functions in R
03:22
Exercise 4 Using functions in R
00:49
Functions and arguments
02:30
Exercise 5 The arguments of a function
00:25
Objects and Functions
3 questions
Exercise 6 Building a function in R
00:25
Using the script vs. using the console
02:55
+ Vectors and vector operations
11 lectures 29:48
Intro
01:10
Introduction to vectors
03:31
Vector recycling
01:39
Exercise 7 Vector recycling
00:37
Exercise 8 Vector attributes - names
00:18
Introduction to vectors
2 questions
Getting help with R
06:37
Getting Help with R
2 questions
Slicing and indexing a vector in R
07:01
Extracting elements from a vector
3 questions
Exercise 9 Indexing and slicing a vector
00:15
Changing the dimensions of an object in R
04:50
Exercise 10 Vector attributes - dimensions
00:27
+ Matrices
17 lectures 49:58
Creating a matrix in R
06:51
Faster code: creating a matrix in a single line of code
02:46
Creating a matrix
3 questions
Exercise 11 Creating a matrix in R
00:16
Do matrices recycle?
01:36
Indexing an element from a matrix
04:37
Slicing a matrix in R
03:33
Exercise 12 Indexing and slicing a matrix
00:29
Matrix arithmetic
07:07
Exercise 13 Matrix arithmetic
00:44
Matrix operations in R
04:18
Matrix operations
4 questions
Exercise 14 Matrix operations
00:28
Categorical data
03:29
Factors in R
2 questions
Exercise 15 Creating a factor in R
00:14
Lists in R
06:01
Exercise: Lists in R
00:55
Completed 33% of the course
00:32
+ Fundamentals of programming with R
16 lectures 45:53
Relational operators in R
05:06
Logical operators in R
03:22
Vectors and logicals operators
02:29
Relational and Logical operators in R
5 questions
Exercise Logical operators
00:01
If, else, else if statements in R
05:47
Exercise If, else, else if statements in R
00:56
If, else, else if statements - Keep-In-Mind's
03:50
For loops in R
06:24
Exercise: For Loops in R
00:05
While loops in R
04:05
Exercise: While loops in R
00:05
Repeat loops in R
03:05
Loops in R
4 questions
Building a function in R 2.0
04:33
Building a function in R 2.0 - Scoping
05:16
Exercise Scoping
00:10
Completed 50% of the course
00:38
+ Data frames
13 lectures 37:56
Intro
00:54
Creating a data frame in R
05:54
Exercise 16 Creating a data frame in R
00:16
The Tidyverse package
03:19
Data import in R
03:28
Importing a CSV in R
03:14
Data export in R
02:31
Exercise 17 Importing and exporting data in R
00:26
Creating data frames
5 questions
Getting a sense of your data frame
03:58
Indexing and slicing a data frame in R
04:09
Data frame operations
4 questions
Extending a data frame in R
04:20
Exercise 18 Data frame operations
00:38
Dealing with missing data in R
04:48
+ Manipulating data
9 lectures 26:45
Intro
01:15
Data transformation with R - the Dplyr package - Part I
05:44
Data transformation with R - the Dplyr package - Part II
03:22
Sampling data with the Dplyr package
01:44
Using the pipe operator in R
03:27
Manipulating data
5 questions
Exercise 19 Data transformation with Dplyr
00:33
Tidying data in R - gather() and separate()
07:27
Tidying data in R - unite() and spread()
02:44
Tidying data
5 questions
Exercise 20 Data tidying with Tidyr
00:28
+ Visualizing data
11 lectures 43:43
Intro
01:00
Intro to data visualization
03:59
Variables: revisited
05:51
Building a histogram with ggplot2
06:31
Exercise 21 Building a histogram with ggplot2
00:22
Building a bar chart with ggplot2
06:29
Exercise 22 Building a bar chart with ggplot2
00:28
Building a box and whiskers plot with ggplot2
06:17
Exercise 23 Building a box plot with ggplot2
00:37
Building a scatterplot with ggplot2
05:21
+ Exploratory data analysis
7 lectures 26:01
Population vs. sample
04:02
Skewness
03:21
Exercise 25 Determining Skewness
00:06
Variance, standard deviation, and coefficient of variability
06:11
Covariance and correlation
06:41
Exercise 26 Practical example with real estate data
00:36
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 2020

R 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 :)

Who this course is for:
  • 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