Course Curriculum
- 18 sections
- 84 lectures
- 1 day, 4 hours total length
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Data Science and Machine Learning Introduction00:03:00
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What is Data Science00:10:00
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Machine Learning Overview00:05:00
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Who is This Course for00:03:00
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Data Science and Machine Learning Marketplace00:05:00
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Data Science and Machine Learning Job Opportunities00:03:00
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Data Science Job Roles00:04:00
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Getting Started00:11:00
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Basics00:06:00
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Files00:11:00
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RStudio00:07:00
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Tidyverse00:05:00
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Resources00:04:00
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Unit Introduction00:30:00
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Basic Type00:09:00
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Vector Part One00:20:00
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Vectors Part Two00:25:00
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Vectors – Missing Values00:16:00
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Vectors – Coercion00:14:00
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Vectors – Naming00:10:00
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Vectors – Misc00:06:00
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Creating Matrics00:31:00
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List00:32:00
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Introduction to Data Frames00:19:00
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Creating Data Frames00:20:00
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Data Frames: Helper Functions00:31:00
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Data Frames Tibbles00:39:00
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Intermediate Introduction00:47:00
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Relational Operations00:11:00
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Logical Operators00:07:00
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Conditional Statements00:11:00
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Loops00:08:00
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Functions00:14:00
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Packages00:11:00
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Factors00:28:00
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Dates and Times00:30:00
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Functional Programming00:37:00
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Data Import or Export00:22:00
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Database00:27:00
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Data Manipulation in R Introduction00:36:00
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Tidy Data00:11:00
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The Pipe Operator00:15:00
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The Filter Verb00:22:00
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The Select Verb00:46:00
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The Mutate Verb00:32:00
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The Arrange Verb00:10:00
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The Summarize Verb00:23:00
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Data Pivoting00:43:00
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JSON Parsing00:11:00
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String Manipulation00:33:00
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Web Scraping00:59:00
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Data Visualization in R Section Intro00:17:00
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Getting Started00:16:00
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Aesthetics Mappings00:25:00
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Single Variable Plots00:37:00
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Two Variable Plots00:21:00
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Facets, Layering, and Coordinate Systems00:18:00
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Styling and Saving00:12:00
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Creating with R Markdown00:29:00
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Introduction to R Shiny00:26:00
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A Basic R Shiny App00:31:00
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Other Examples with R Shiny00:34:00
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Machine Learning Part 100:22:00
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Machine Learning Part 200:47:00
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Section Overview(Section 10: Data Preprocessing)00:27:00
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Data Preprocessing00:38:00
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Section Introduction(Section 11: Linear Regression: A Simple Model)00:25:00
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A Simple Model00:53:00
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Section Introduction(Section 12: Exploratory Data Analysis)00:25:00
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Hands-on Exploratory Data Analysis01:03:00
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Section Introduction (Section 13: Linear Regression: A Real Model)00:32:00
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Linear Regression in R00:53:00
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Logistic Regression Intro00:38:00
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Logistic Regression in R00:40:00
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Starting a Data Science Career Section Overview00:03:00
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Data Science Resume00:04:00
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Getting Started with Freelancing00:05:00
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Top Freelance Websites00:05:00
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Personal Branding00:05:00
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Importance of Website and Blo00:04:00
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Networking Do’s and Don’ts00:04:00
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Resources – Data Science & Machine Learning with R
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Assignment – Data Science & Machine Learning with R
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