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Development Programming Languages R

Introduction to R

Introduction to R
Rating: 4.0 out of 54.0 (6 ratings)
17 students
Created by Integrity Training
Last updated 10/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • R Language and Mathematics
  • How to work with R Vectors
  • How to read and write data from files, and how to categorize data in factors
  • How to work with Dates and perform Date math
  • How to work with multiple dimensions and DataFrame essentials
  • Essential Data Science and how to use R with it
  • Visualization in R
  • How R can be used in Spark

Course content

4 sections • 60 lectures • 5h 14m total length

  • Preview01:43
  • Preview13:24
  • About R - Part 3
    00:58
  • Topic B: RStudio - Part 1
    00:26
  • RStudio - Part 2
    10:14
  • RStudio - Part 3
    00:32
  • Topic C: Workspaces - Part 1
    00:31
  • Workspaces - Part 2
    11:42
  • Workspaces - Part 3
    00:33
  • Topic D: Basic Types - Part 1
    00:28
  • Basic Types - Part 2
    13:22
  • Basic Types - Part 3
    00:45
  • Review - Question
    13 questions

  • Topic A: Basic Types Demo - Part 1
    00:23
  • Basic Types Demo - Part 2
    17:24
  • Basic Types Demo - Part 3
    00:45
  • Topic B: Dates Demo - Part 1
    00:30
  • Dates Demo - Part 2
    17:11
  • Dates Demo - Part 3
    00:37
  • Topic C: Variables - Part 1
    00:31
  • Variables - Part 2
    09:55
  • Variables - Part 3
    00:30
  • Topic D: Missing Values - Part 1
    00:35
  • Missing Values - Part 2
    11:24
  • Missing Values - Part 3
    00:35
  • Review - Question
    10 questions

  • Topic A: Vectors - Part 1
    00:31
  • Vectors - Part 2
    20:00
  • Vectors - Part 3
    00:43
  • Topic B: Matrices - Part 1
    00:31
  • Matrices - Part 2A
    16:21
  • Matrices - Part 2B
    04:19
  • Matrices - Part 3
    00:42
  • Topic C: Arrays - Part 1
    00:32
  • Arrays - Part 2
    08:14
  • Arrays - Part 3
    00:26
  • Topic D: Lists and Factors - Part 1
    00:32
  • Lists and Factors - Part 2
    17:03
  • Lists and Factors - Part 3
    00:46
  • Topic E: Arithmetic and Relational Operators - Part 1
    00:29
  • Arithmetic and Relational Operators - Part 2
    16:45
  • Arithmetic and Relational Operators - Part 3
    00:44
  • Topic F: Logical and Assignment Operators - Part 1
    00:26
  • Logical and Assignment Operators - Part 2
    09:42
  • Logical and Assignment Operators - Part 3
    00:54
  • Review - Question
    16 questions

  • Topic A: Data Frames - Part 1
    00:39
  • Data Frames - Part 2
    09:33
  • Data Frames - Part 3
    00:37
  • Topic B: Working with Data Frames - Part 1
    00:26
  • Working with Data Frames - Part 2A
    14:27
  • Working with Data Frames - Part 2B
    14:58
  • Working with Data Frames - Part 3
    00:42
  • Topic C: Data Tables - Part 1
    00:41
  • Data Tables - Part 2
    13:53
  • Data Tables - Part 3
    00:42
  • Topic D: Working with Data Tables - Part 1
    00:31
  • Working with Data Tables - Part 2
    17:53
  • Working with Data Tables - Part 3
    00:49
  • Topic E: Shortcuts - Part 1
    00:30
  • Shortcuts - Part 2A
    14:53
  • Shortcuts - Part 2B
    07:09
  • Shortcuts - Part 3
    01:16
  • Review - Question
    20 questions

Requirements

  • The R programming language course requires the students to have functional knowledge and basic skills of SQL and databases.
  • In addition to this, no prior knowledge of R or any other programming language is required.

Description

The Introduction to R is a primary course for aspiring data scientists who are currently working with Microsoft Excel, Matlab, Mathematica or SAS for numerical analysis of large sets of data. The course enables the candidates in using more powerful OpenSource environments especially the R programming language. R is a functional programming environment employed by many data analysts and data scientists, easily accessible to non-programmers and naturally extending a skill set that is common to data analysts and data scientists. It's the perfect tool for when the one has a statistical, numerical, or probabilities-based problem based on real data and they've pushed those tools past their limits. This fundamental course covers all the necessary topics required to kick start the candidates in working with R programming language.

This introductory course provides a complete coverage of the fundamentals of R programming language, including its uses, benefits and applications. The students who enroll in this course will move one step closer to become accomplished data scientists. The course covers the umbrella of technologies that are on the leading edge of data science development focused on R and related tools.


Who this course is for:

  • Beginner level data analysts
  • Data scientists who need to learn the essentials of how to program in R
  • Graduating students aspiring to become data scientists
  • Analysts working with MS Excel, Matlab, Mathematica or SAS

Instructor

Integrity Training
Certification Trainers for Over 20 Years
Integrity Training
  • 4.2 Instructor Rating
  • 9,683 Reviews
  • 77,735 Students
  • 148 Courses

Hi, we're new to Udemy but we've been producing and selling high quality online training for over 20 years. Over a million students have taken our training through our corporate, government and education partners. Our content was developed for institutions that require the absolute highest standards in teaching quality. We're now bringing our content to consumers like you. Don't settle for anything less than the best quality content available. 

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