The Comprehensive Statistics and Data Science with R Course

The Comprehensive Statistics and Data Science with R Course, available at $54.99, has an average rating of 4.55, with 219 lectures, based on 255 reviews, and has 2917 subscribers.

You will learn about Students will understand what R is, and how to input and output data files into their R sessions. Students will know how to manipulate numbers and vectors, and will understand objects and classes. Students will understand how to create data structures in R: vectors; arrays and matrices; lists and data frames. Students will know how to use R as a statistical environment following many examples. Students will understand how to create, estimate and interpret ANOVA, regression, GLM and GAM statistical models with many examples of each. Students will learn how to create statistical and other visualizations using both the base and ggplot graphics capabilities in R. This course is ideal for individuals who are This course will benefit anyone wishing to learn R and especially those who seek an in-depth "hands-on" tutorial on performing statistical analyses with R. or The course is useful for graduate students, college and university faculty, and working quantitative analysis professionals. It is particularly useful for This course will benefit anyone wishing to learn R and especially those who seek an in-depth "hands-on" tutorial on performing statistical analyses with R. or The course is useful for graduate students, college and university faculty, and working quantitative analysis professionals.

Enroll now: The Comprehensive Statistics and Data Science with R Course

Summary

Title: The Comprehensive Statistics and Data Science with R Course

Price: $54.99

Average Rating: 4.55

Number of Lectures: 219

Number of Published Lectures: 219

Number of Curriculum Items: 219

Number of Published Curriculum Objects: 219

Original Price: $159.99

Quality Status: approved

Status: Live

What You Will Learn

Students will understand what R is, and how to input and output data files into their R sessions.
Students will know how to manipulate numbers and vectors, and will understand objects and classes.
Students will understand how to create data structures in R: vectors; arrays and matrices; lists and data frames.
Students will know how to use R as a statistical environment following many examples.
Students will understand how to create, estimate and interpret ANOVA, regression, GLM and GAM statistical models with many examples of each.
Students will learn how to create statistical and other visualizations using both the base and ggplot graphics capabilities in R.

Who Should Attend

This course will benefit anyone wishing to learn R and especially those who seek an in-depth "hands-on" tutorial on performing statistical analyses with R.
The course is useful for graduate students, college and university faculty, and working quantitative analysis professionals.

Target Audiences

This course will benefit anyone wishing to learn R and especially those who seek an in-depth "hands-on" tutorial on performing statistical analyses with R.
The course is useful for graduate students, college and university faculty, and working quantitative analysis professionals.

This course, The Comprehensive Statistics and Data Science with R Course, is mostly based on the authoritative documentation in the online “An Introduction to R” manual produced with each new R release by the Comprehensive R Archive Network (CRAN) development core team. These are the people who actually write, test, produce and release the R code to the general public by way of the CRAN mirrors. It is a rich and detailed 10-session course which covers much of the content in the contemporary 105-page CRAN manual. The ten sessions follow the outline in the An Introduction to R online manual and specifically instruct with respect to the following user topics:

1. Introduction to R; Inputting data into R

2. Simple manipulation of numbers and vectors

3. Objects, their modes and attributes

4. Arrays and matrices

5. Lists and data frames

6. Writing user-defined functions

7. Working with R as a statistical environment

8. Statistical models and formulae; ANOVA and regression

9. GLMs and GAMs

10. Creating statistical and other visualizations with R

It is a comprehensive and decidedly “hands-on” course. You are taught how to actually use R and R script to create everything that you see on-screen in the course videos. Everything is included with the course materials: all software; slides; R scripts; data sets; exercises and solutions; in fact, everything that you see utilized in any of the 200+ course videos are included with the downloadable course materials.

The course is structured for both the novice R user, as well as for the more experienced R user who seeks a refresher course in the benefits, tools and capabilities that exist in R as a software suite appropriate for statistical analysis and manipulation. The first half of the course is suited for novice R users and guides one through “hands-on” practice to master the input and output of data, as well as all of the major and important objects and data structures that are used within the R environment. The second half of the course is a detailed “hands-on” transcript for using R for statistical analysis including detailed data-driven examples of ANOVA, regression, and generalized linear and additive models. Finally, the course concludes with a multitude of “hands-on” instructional videos on how to create elegant and elaborate statistical (and other) graphics visualizations using both the base and gglot visualization packages in R.

The course is very useful for any quantitative analysis professional who wishes to “come up to speed” on the use of R quickly. It would also be useful for any graduate student or college or university faculty member who also seeks to master these data analysis skills using the popular R package.

Course Curriculum

Chapter 1: Introduction to R and Inputting Data into R

Lecture 1: Introduction

Lecture 2: Another Word about the Course and the Materials

Lecture 3: Introduction to Course Materials

Lecture 4: Session 1 Exercises

Lecture 5: Agenda and What is R ? (slides, Part 1)

Lecture 6: What is R ? (slides, part 2)

Lecture 7: What is R ? (slides, part 3)

Lecture 8: What is R ? (slides, part 4)

Lecture 9: What is R ? (slides, part 5)

Lecture 10: Reading in Data (part 1)

Lecture 11: Reading in Data (part 2)

Lecture 12: Reading in Data (part 3)

Chapter 2: Manipulating Numbers and Vectors

Lecture 1: Introduction to Section 2

Lecture 2: Vectors and Assignment (part 1)

Lecture 3: Vectors and Assignment (part 2)

Lecture 4: Vectors and Assignment (part 3)

Lecture 5: Vector Arithmetic (part 1)

Lecture 6: Vector Arithmetic (part 2)

Lecture 7: Vector Arithmetic (part 3)

Lecture 8: Vector Arithmetic (part 4)

Lecture 9: Vector Arithmetic (part 5)

Lecture 10: Generating Regular Sequences

Lecture 11: Logical Vectors

Lecture 12: More Missing Values; Character Vectors

Lecture 13: Index Vectors (part 1)

Lecture 14: Index Vectors (part 2)

Lecture 15: Index Vectors (part 3)

Lecture 16: Index Vectors (part 4)

Lecture 17: Session 2 Exercises

Chapter 3: Objects and Classes: Their Modes and Attributes

Lecture 1: Solutions to Session 2 Exercises (part 1)

Lecture 2: Solutions to Session 2 Exercises (part 2)

Lecture 3: Solutions to Session 2 Exercises (part 3)

Lecture 4: Objects and Classes

Lecture 5: Numeric Types

Lecture 6: Strings

Lecture 7: Factors

Lecture 8: Logical and Missing

Lecture 9: Vectors

Lecture 10: Vectorization and Recycling

Lecture 11: Basic Data Structures in R (slides, part 1)

Lecture 12: Basic Data Structures (slides, part 2)

Lecture 13: Basic Data Structures (slides, part 3)

Lecture 14: Objects: Script Examples (part 1)

Lecture 15: Objects: Script Examples (part 2)

Lecture 16: Objects: Script Examples (part 3)

Lecture 17: Objects: Script Examples (part 4)

Chapter 4: Arrays and Matrices

Lecture 1: Session 4 Exercises

Lecture 2: More on Factors

Lecture 3: More on Factors and Strings (part 1)

Lecture 4: Factors and Strings (part 2)

Lecture 5: Factors and Strings (part 3)

Lecture 6: Function tapply() and Ragged Arrays

Lecture 7: Arrays

Lecture 8: Arrays and Matrices (part 1)

Lecture 9: Arrays and Matrices (part 2)

Lecture 10: Warpbreaks Data (part 1)

Lecture 11: Warpbreaks Data (part 2)

Lecture 12: More about Matrices (part 1)

Lecture 13: More about Matrices (part 2)

Lecture 14: More about Matrices (part 3)

Lecture 15: More about Matrices (part 4)

Lecture 16: More about Matrices (part 5)

Lecture 17: More about Matrices (part 6)

Lecture 18: Creating Matrices (part 1)

Lecture 19: Creating Matrices (part 2)

Lecture 20: Row Names and Column Names

Lecture 21: More on Array Function

Lecture 22: Outer Product of Two Arrays

Chapter 5: List and Data Frame Structures

Lecture 1: Introduction to Lists

Lecture 2: List Features and List Slicing (part 1)

Lecture 3: List Features and List Slicing (part 2)

Lecture 4: Accessing List Components (part 1)

Lecture 5: Accessing List Components (part 2)

Lecture 6: More List Dissection (part 1)

Lecture 7: More List Dissection (part 2)

Lecture 8: More About Lists

Lecture 9: What are Data Frames

Lecture 10: Characteristics of Data Frames

Lecture 11: A Data Frame is a List

Lecture 12: Data Frames are Lists

Lecture 13: Manipulating Data Frames (part 1)

Lecture 14: Manipulating Data Frames (part 2)

Lecture 15: Manipulating Data Frames (part 3)

Lecture 16: Manipulating Data Frames (part 4)

Lecture 17: Manipulating Data Frames (part 5)

Lecture 18: Manipulating Data Frames (part 6)

Lecture 19: Manipulating Data Frames (part 7)

Chapter 6: User-Defined Functions

Lecture 1: Exercise Solutions (part 1)

Lecture 2: Exercise Solutions (part 2)

Lecture 3: Exercise Solutions (part 3)

Lecture 4: Exercise Solutions (part 4)

Lecture 5: Exercise Solutions (part 5)

Lecture 6: Exercise Solutions (part 6)

Lecture 7: Exercise Solutions (part 7)

Instructors

Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics

Rating Distribution

1 stars: 7 votes
2 stars: 8 votes
3 stars: 44 votes
4 stars: 80 votes
5 stars: 116 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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