R Programming for Statistics and Data Science

Video description

R Programming for data science and data analysis. Apply R for statistics and data visualization with GGplot2 in R

About This Video

  • Introductory guide to statistics - descriptive statistics and the fundamentals of inferential statistics
  • Essentials of R-based programming - soar above the average data scientist and boost the productivity of your operations.
  • Data manipulation and analysis techniques - learn to work with R's most comprehensive collection of tools and create meaning-heavy data visualizations and plots.

In Detail

R Programming is a skill you'll 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 skillset 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 job. This course packs all of this, and more, in one easy-to-handle bundle, and it's the perfect start to your journey. So, welcome to R Programming for Statistics and Data Science, the course that will get you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skillset to tackle any new data science project with confidence and critically assess your work and other people's.

Practicability is the key to this course, Using R, you have a wide variety of options where you can take the code provided within this course and expand on it in any number of directions. You'll reinforce your learning through numerous practical exercises.

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Table of contents

  1. Chapter 1 : Introduction
    1. Ten Things You Will Learn in This Course
  2. Chapter 2 : Getting started
    1. Intro
    2. Downloading and installing R RStudio
    3. Quick guide to the RStudio user interface
    4. Changing the appearance in RStudio
    5. Installing packages in R and using the library
  3. Chapter 3 : The building blocks of R
    1. Creating an object in R
    2. Data types in R - Integers and doubles
    3. Data types in R - Characters and logicals
    4. Coercion rules in R
    5. Functions in R
    6. Functions and arguments
    7. Building a function in R (basics)
    8. Using the script vs. using the console
  4. Chapter 4 : Vectors and vector operations
    1. Intro
    2. Introduction to vectors
    3. Vector recycling
    4. Naming a vector in R
    5. Getting help with R
    6. Slicing and indexing a vector in R
    7. Changing the dimensions of an object in R
  5. Chapter 5 : Matrices
    1. Creating a matrix in R
    2. Faster code: creating a matrix in a single line of code
    3. Do matrices recycle?
    4. Indexing an element from a matrix
    5. Slicing a matrix in R
    6. Matrix arithmetic
    7. Matrix operations in R
    8. Categorical data
    9. Creating a factor in R
    10. Lists in R
  6. Chapter 6 : Fundamentals of programming with R
    1. Relational operators in R
    2. Logical operators in R
    3. Vectors and logicals operators
    4. If, else, else if statements in R
    5. If, else, else if statements - Keep-In-Mind's
    6. For loops in R
    7. While loops in R
    8. Repeat loops in R
    9. Building a function in R 2.0
    10. Building a function in R 2.0 - Scoping
  7. Chapter 7 : Data frames
    1. Intro
    2. Creating a data frame in R
    3. The Tidyverse package
    4. Data import in R
    5. Importing a CSV in R
    6. Data export in R
    7. Getting a sense of your data frame
    8. Indexing and slicing a data frame in R
    9. Extending a data frame in R
    10. Dealing with missing data in R
  8. Chapter 8 : Manipulating data
    1. Intro
    2. Data transformation with R - the Dplyr package - Part I
    3. Data transformation with R - the Dplyr package - Part II
    4. Sampling data with the Dplyr package
    5. Using the pipe operator in R
    6. Tidying data in R - gather() and separate()
    7. Tidying data in R - unite() and spread()
  9. Chapter 9 : Visualizing data
    1. Intro
    2. Intro to data visualization
    3. Intro to ggplot2
    4. Variables: revisited
    5. Building a histogram with ggplot2
    6. Building a bar chart with ggplot2
    7. Building a box and whiskers plot with ggplot2
    8. Building a scatterplot with ggplot2
  10. Chapter 10 : Exploratory data analysis
    1. Population vs. sample
    2. Mean, median, mode
    3. Skewness
    4. Variance, standard deviation, and coefficient of variability
    5. Covariance and correlation
  11. Chapter 11 : Hypothesis Testing
    1. Distributions
    2. Standard Error and Confidence Intervals
    3. Hypothesis testing
    4. Type I and Type II errors
    5. Test for the mean - population variance known
    6. The P-value
    7. Test for the mean - Population variance unknown
    8. Comparing two means - Dependent samples
    9. Comparing two means - Independent samples
  12. Chapter 12 : Linear Regression Analysis
    1. The linear regression model
    2. Correlation vs regression
    3. Geometrical representation
    4. First regression in R
    5. How to interpret the regression table
    6. Decomposition of variability: SST, SSR, SSE
    7. R-squared

Product information

  • Title: R Programming for Statistics and Data Science
  • Author(s): 365 Careers
  • Release date: October 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781789950298