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Learn Data Science & Machine Learning with R from A-Z Course
Training Overview
Description of Training
Video Overview (3:44)
Section 1: Introduction to Data Science +ML with R from A-Z
1. Intro To DS+ML Section Overview (2:30)
2. What is Data Science? (9:47)
3. Machine Learning Overview (5:26)
4. Who is this course for? (2:57)
5. Data Science + Machine Learning Marketplace (4:38)
6. DS+ ML Job Opportunities (2:37)
7. Data Science Job Roles (4:04)
Section 2: Getting Started with R
1. Getting Started (10:58)
2. Basics (6:24)
3. Files (11:08)
4. R Studio (6:58)
5. Tidyverse (5:19)
6. Resources (4:02)
Section 3: Data Types and Structures in R
1. Section Introduction (30:03)
2. Basic Types (8:46)
3. Vectors Part One (19:40)
4. Vectors Part Two (24:51)
5. Vectors: Missing Values (15:36)
6. Vectors: Coercion (14:07)
7. Vectors: Naming (10:16)
8. Vectors: Misc. (5:59)
9. Matrices (31:27)
10. Lists (31:41)
11. Introduction to Data Frames (19:20)
12. Creating Data Frames (19:50)
13. Data Frames: Helper Functions (31:12)
14. Data Frames: Tibbles (39:03)
Section 4: Intermediate R
1. Section Introduction (46:31)
2. Relational Operators (11:06)
3. Logical Operators (7:04)
4. Conditional Statements (11:20)
5. Loops (7:56)
6. Functions (14:19)
7. Packages (11:29)
8. Factors (28:14)
9. Dates & Times (30:10)
10. Functional Programming (36:41)
11. Data Import/Export (22:06)
12. Databases (27:08)
Section 5: Data Manipulation in R
1. Section Introduction (36:29)
2. Tidy Data (10:53)
3. The Pipe Operator (14:50)
4. {dplyr}: The Filter Verb (21:34)
5. {dplyr}: The Select Verb (46:03)
6. {dplyr}: The Mutate Verb (31:57)
7. {dplyr}: The Arrange Verb (10:03)
8. {dplyr}: The Summarize Verb (23:05)
9. Data Pivoting: {tidyr} (42:41)
10. String Manipulation: {stringr} (32:38)
11. Web Scraping: {rvest} (58:53)
12. JSON Parsing: {jsonlite} (10:46)
Section 6: Data Visualization in R
1. Section Introduction (17:13)
2. Getting Started (15:37)
3. Aesthetics Mappings (24:45)
4. Single Variable Plots (36:50)
5. Two-Variable Plots (20:34)
6. Facets, Layering, and Coordinate Systems (17:56)
7. Styling and Saving (11:33)
Section 7: Creating Reports with R Markdown
1. Intro To R Markdown (28:54)
Section 8: Building Webapps with R Shiny
1. Intro to R Shiny (26:05)
2. A Basic Webapp (31:18)
3. Other Examples (34:05)
Section 9: Introduction to Machine Learning
1. Intro to ML Part 1 (21:48)
2. Intro to ML Part 2 (46:45)
Section 10: Data Preprocessing
1. Section Overview (27:03)
2. Data Preprocessing (37:47)
Section 11: Linear Regression: A Simple Model
1. Section Introduction (25:09)
2. A Simple Model (53:05)
Section 12: Exploratory Data Analysis
1. Section Introduction (25:03)
2. Hands-on Exploratory Data Analysis (62:57)
Section 13: Linear Regression: A Real Model
1. Section Introduction (32:04)
2. Linear Regression in R (52:48)
Section 14: Logistic Regression
1. Logistic Regression Intro (37:48)
2. Logistic Regression in R (39:37)
Section 15: Starting a Career in Data Science
1. Section Overview (2:54)
2. Creating A Data Science Resume (3:43)
3. Getting Started with Freelancing (4:44)
4. Top Freelance Websites (5:18)
5. Personal Branding (5:27)
6. Networking (3:50)
7. Setting Up a Website (3:42)
Audio Version of the Training
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4. Who is this course for?
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