Data Science and Machine Learning with R from A-Z Course [Updated for 2021]

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Juan E. Galvan

28:50:16

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00001 Data Science and Machine Learning Introduction Section Overview.mp4

02:30

00002 What is Data Science.mp4

09:47

00003 Machine Learning Overview.mp4

05:26

00004 Data Science + Machine Learning Marketplace.mp4

04:38

00005 Who is this Course For.mp4

02:57

00006 Data Science and Machine Learning Job Opportunities.mp4

02:37

00007 Data Science Job Roles.mp4

04:14

00008 Getting Started with R.mp4

10:58

00009 R Basics.mp4

06:24

00010 Working with Files.mp4

11:08

00011 R Studio.mp4

06:58

00012 Tidyverse Overview.mp4

05:19

00013 Additional Resources.mp4

04:02

00014 Data Types and Structures in R Section Overview.mp4

30:03

00015 Basic Types.mp4

08:46

00016 Vectors - Part One.mp4

19:40

00017 Vectors - Part Two.mp4

24:51

00018 Vectors - Missing Values.mp4

15:36

00019 Vectors - Coercion.mp4

14:07

00020 Vectors - Naming.mp4

10:16

00021 Vectors - Miscellaneous.mp4

05:59

00022 Working with Matrices.mp4

31:27

00023 Working with Lists.mp4

31:41

00024 Introduction to Data Frames.mp4

19:20

00025 Creating Data Frames.mp4

19:50

00026 Data Frames - Helper Functions.mp4

31:12

00027 Data Frames - Tibbles.mp4

39:13

00028 Intermedia R Section Introduction.mp4

46:31

00029 Relational Operators.mp4

11:06

00030 Logical Operators.mp4

07:04

00031 Conditional Statements.mp4

11:20

00032 Working with Loops.mp4

07:56

00033 Working with Functions.mp4

14:19

00034 Working with Packages.mp4

11:29

00035 Working with Factors.mp4

28:14

00036 Dates and Times.mp4

30:10

00037 Functional Programming.mp4

36:41

00038 Data Import Export.mp4

22:06

00039 Working with Databases.mp4

27:08

00040 Data Manipulation Section Introduction.mp4

36:29

00041 Tidy Data.mp4

10:53

00042 The Pipe Operator.mp4

14:50

00043 dplyr - The Filter Verb.mp4

21:34

00044 dplyr - The Select Verb.mp4

46:03

00045 dplyr - The Mutate Verb.mp4

31:57

00046 dplyr - The Arrange Verb.mp4

10:03

00047 dplyr - The Summarize Verb.mp4

23:05

00048 Data Pivoting - tidyr.mp4

42:41

00049 String Manipulation - stringr.mp4

32:38

00050 Web Scraping - rvest.mp4

58:53

00051 JSON Parsing - jsonlite.mp4

10:56

00052 Data Visualization in R Section Introduction.mp4

17:13

00053 Getting Started with Data Visualization in R.mp4

15:37

00054 Aesthetics Mappings.mp4

24:45

00055 Single Variable Plots.mp4

36:50

00056 Two Variable Plots.mp4

20:33

00057 Facets Layering and Coordinate Systems.mp4

17:56

00058 Styling and Saving.mp4

11:33

00059 Introduction to R Markdown.mp4

29:04

00060 Introduction to R Shiny.mp4

26:05

00061 Creating a Basic R Shiny App.mp4

31:18

00062 Other Examples with R Shiny.mp4

34:05

00063 Introduction to Machine Learning Part One.mp4

21:48

00064 Introduction to Machine Learning Part Two.mp4

46:55

00065 Data Preprocessing Introduction.mp4

27:03

00066 Data Preprocessing.mp4

37:47

00067 Linear Regression - A Simple Model Introduction.mp4

25:09

00068 A Simple Model.mp4

53:15

00069 Exploratory Data Analysis Introduction.mp4

25:03

00070 Hands-on Exploratory Data Analysis.mp4

01:02:57

00071 Linear Regression - Real Model Section Introduction.mp4

32:04

00072 Linear Regression in R - Real Model.mp4

52:58

00073 Introduction to Logistic Regression.mp4

37:48

00074 Logistic Regression in R.mp4

39:37

00075 Starting a Data Science Career Section Overview.mp4

02:54

00076 Creating a Data Science Resume.mp4

03:43

00077 Getting Started with Freelancing.mp4

04:44

00078 Top Freelance Websites.mp4

05:18

00079 Personal Branding.mp4

05:27

00080 Networking Do s and Don ts.mp4

03:50

00081 Setting Up a Website.mp4

03:52

Description

The course covers practical issues in statistical computing that include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R programming to mastery. We understand that theory is important to build a solid foundation, we also understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you! R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques. By the end of the course, you’ll be a professional data scientist with R and confidently apply for jobs and will feel good knowing that you have the skills and knowledge to back it up. All resources are placed here: https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-

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Machine Learning

Data Science

Juan E. Galvan

Instructor's Courses

Juan E. Galvan has been an entrepreneur since grade school. His background is in the tech space from digital marketing, e-commerce, web development to programming. He believes in continuous education with the best of a university degree without all the downsides of burdensome costs and inefficient methods. He looks forward to helping people expand their skillsets.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.