R Programming for Statistics and Data Science
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       Video Length : 22h00m0s
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       Tasks Number : 143
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       Students Enrolled : 847
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       Medium Level
  • Curriculum
  • 1. Introduction
    • videocam
      Getting started
      10m0s
    • videocam
      Intro
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    • videocam
      Downloading and installing R & RStudio
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    • videocam
      Quick guide to the RStudio user interface
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      RStudio's GUI
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      Changing the appearance in RStudio
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      Installing packages in R and using the library
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  • 2. The building blocks of R
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      Creating an object in R
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      Exercise 1 Creating an object in R
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    • videocam
      Data types in R - Integers and doubles
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      Data types in R - Characters and logicals
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      Objects and Data Types
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      Exercise 2 Data types in R
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      Coercion rules in R
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      Exercise 3 Coercion rules in R
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      Functions in R
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      Exercise 4 Using functions in R
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      Functions and arguments
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      Exercise 5 The arguments of a function
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    • videocam
      Building a function in R (basics)
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      Objects and Functions
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      Exercise 6 Building a function in R
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      Using the script vs. using the console
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  • 3. Vectors and vector operations
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      Intro
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      Introduction to vectors
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      Vector recycling
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      Exercise 7 Vector recycling
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      Naming a vector in R
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      Exercise 8 Vector attributes - names
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    • videocam
      Introduction to vectors
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    • videocam
      Getting help with R
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    • videocam
      Getting Help with R
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    • videocam
      Slicing and indexing a vector in R
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      Extracting elements from a vector
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      Exercise 9 Indexing and slicing a vector
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      Changing the dimensions of an object in R
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      Exercise 10 Vector attributes - dimensions
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  • 4. Matrices
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      Creating a matrix in R
      10m0s
    • videocam
      Faster code: creating a matrix in a single line of code
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    • videocam
      Creating a matrix
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    • videocam
      Exercise 11 Creating a matrix in R
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    • videocam
      Do matrices recycle?
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      Indexing an element from a matrix
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      Slicing a matrix in R
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      Exercise 12 Indexing and slicing a matrix
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      Matrix arithmetic
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      Exercise 13 Matrix arithmetic
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      Matrix operations in R
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      Matrix operations
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      Exercise 14 Matrix operations
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      Categorical data
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      Creating a factor in R
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      Factors in R
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      Exercise 15 Creating a factor in R
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      Lists in R
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      Completed 33% of the course
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  • 5. Fundamentals of programming with R
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      Relational operators in R
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      Logical operators in R
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      Vectors and logicals operators
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      Relational and Logical operators in R
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    • videocam
      Exercise Logical operators
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      If, else, else if statements in R
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      If, else, else if statements - Keep-In-Mind's
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      For loops in R
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      While loops in R
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      Repeat loops in R
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      Loops in R
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      Building a function in R 2.0
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      Building a function in R 2.0 - Scoping
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      Exercise Scoping
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      Completed 50% of the course
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  • 6. Data frames
    • videocam
      Intro
      10m0s
    • videocam
      Creating a data frame in R
      10m0s
    • videocam
      Exercise 16 Creating a data frame in R
      10m0s
    • videocam
      The Tidyverse package
      10m0s
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      Data import in R
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      Importing a CSV in R
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      Data export in R
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    • videocam
      Exercise 17 Importing and exporting data in R
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    • videocam
      Creating data frames
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      Getting a sense of your data frame
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      Indexing and slicing a data frame in R
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      Data frame operations
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      Extending a data frame in R
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      Exercise 18 Data frame operations
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      Dealing with missing data in R
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  • 7. Manipulating data
    • videocam
      Intro
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    • videocam
      Data transformation with R - the Dplyr package - Part I
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      Data transformation with R - the Dplyr package - Part II
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      Sampling data with the Dplyr package
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      Using the pipe operator in R
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    • videocam
      Manipulating data
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      Exercise 19 Data transformation with Dplyr
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    • videocam
      Tidying data in R - gather() and separate()
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      Tidying data in R - unite() and spread()
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    • videocam
      Tidying data
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      Exercise 20 Data tidying with Tidyr
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  • 8. Visualizing data
    • videocam
      Intro
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      Intro to data visualization
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      Intro to ggplot2
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    • videocam
      Variables: revisited
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      Building a histogram with ggplot2
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      Exercise 21 Building a histogram with ggplot2
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      Building a bar chart with ggplot2
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      Exercise 22 Building a bar chart with ggplot2
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      Building a box and whiskers plot with ggplot2
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      Exercise 23 Building a box plot with ggplot2
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      Building a scatterplot with ggplot2
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  • 9. Exploratory data analysis
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      Population vs. sample
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      Mean, median, mode
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      Skewness
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      Exercise 25 Determining Skewness
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      Variance, standard deviation, and coefficient of variability
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      Covariance and correlation
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      Exercise 26 Practical example with real estate data
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  • 10. Hypothesis Testing
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      Distributions
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      Standard Error and Confidence Intervals
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      Hypothesis testing
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      Type I and Type II errors
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      Test for the mean - population variance known
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      The P-value
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      Test for the mean - Population variance unknown
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      Comparing two means - Dependent samples
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      Comparing two means - Independent samples
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  • 11. Linear Regression Analysis
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      The linear regression model
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      Correlation vs regression
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      Geometrical representation
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    • videocam
      First regression in R
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      How to interpret the regression table
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    • videocam
      Decomposition of variability: SST, SSR, SSE
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    • videocam
      R-squared
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    • videocam
      Completed 100% of the course
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

R Programming for Statistics and Data Science


R Programming for Statistics and Data Science

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