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Data Science & Machine Learning with R Complete Training
Highlights
On-Demand course
22 hours 8 minutes
All levels
Description
Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Data Science & Machine Learning with R Complete Training has been specially designed to help learners gain a good command of Data Science & Machine Learning with R Complete Training, providing them with a solid foundation of knowledge to become a qualified professional.
Through this Data Science & Machine Learning with R Complete Training, you will gain both practical and theoretical understanding of Data Science & Machine Learning with R Complete Training that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.
Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.
Learning outcome
Familiar yourself with the recent development and updates of the relevant industry
Know how to use your theoretical knowledge to adapt in any working environment
Get help from our expert tutors anytime you need
Access to course contents that are designed and prepared by industry professionals
Study at your convenient time and from wherever you want
Why should I take this course?
Affordable premium-quality E-learning content, you can learn at your own pace.
You will receive a completion certificate upon completing the course.
Internationally recognized Accredited Qualification will boost up your resume.
You will learn the researched and proven approach adopted by successful people to transform their careers.
You will be able to incorporate various techniques successfully and understand your customers better.
Requirements
No formal qualifications required, anyone from any academic background can take this course.
Access to a computer or digital device with internet connectivity.
Course Curriculum
Data Science and Machine Learning Course Intro | |||
▶ | Data Science and Machine Learning Introduction | 🕐 00:03:00 | |
▶ | What is Data Science | 🕐 00:10:00 | |
▶ | Machine Learning Overview | 🕐 00:05:00 | |
▶ | Who is This Course for | 🕐 00:03:00 | |
▶ | Data Science and Machine Learning Marketplace | 🕐 00:05:00 | |
▶ | Data Science and Machine Learning Job Opportunities | 🕐 00:03:00 | |
Getting Started with R | |||
▶ | Getting Started | 🕐 00:11:00 | |
▶ | Basics | 🕐 00:06:00 | |
▶ | Files | 🕐 00:11:00 | |
▶ | RStudio | 🕐 00:07:00 | |
▶ | Tidyverse | 🕐 00:05:00 | |
▶ | Resources | 🕐 00:04:00 | |
Data Types and Structures in R | |||
▶ | Unit Introduction | 🕐 00:30:00 | |
▶ | Basic Type | 🕐 00:09:00 | |
▶ | Vector Part One | 🕐 00:20:00 | |
▶ | Vectors Part Two | 🕐 00:25:00 | |
▶ | Vectors - Missing Values | 🕐 00:16:00 | |
▶ | Vectors - Coercion | 🕐 00:14:00 | |
▶ | Vectors - Naming | 🕐 00:10:00 | |
▶ | Vectors - Misc | 🕐 00:06:00 | |
▶ | Creating Matrics | 🕐 00:31:00 | |
▶ | List | 🕐 00:32:00 | |
▶ | Introduction to Data Frames | 🕐 00:19:00 | |
▶ | Creating Data Frames | 🕐 00:20:00 | |
▶ | Data Frames: Helper Functions | 🕐 00:31:00 | |
▶ | Data Frames Tibbles | 🕐 00:39:00 | |
Intermediate R | |||
▶ | Intermediate Introduction | 🕐 00:47:00 | |
▶ | Relational Operations | 🕐 00:11:00 | |
▶ | Conditional Statements | 🕐 00:11:00 | |
▶ | Loops | 🕐 00:08:00 | |
▶ | Functions | 🕐 00:14:00 | |
▶ | Packages | 🕐 00:11:00 | |
▶ | Factors | 🕐 00:28:00 | |
▶ | Dates and Times | 🕐 00:30:00 | |
▶ | Functional Programming | 🕐 00:37:00 | |
▶ | Data Import or Export | 🕐 00:22:00 | |
▶ | Database | 🕐 00:27:00 | |
Data Manipulation in R | |||
▶ | Data Manipulation in R Introduction | 🕐 00:36:00 | |
▶ | Tidy Data | 🕐 00:11:00 | |
▶ | The Pipe Operator | 🕐 00:15:00 | |
▶ | The Filter Verb | 🕐 00:22:00 | |
▶ | The Select Verb | 🕐 00:46:00 | |
▶ | The Mutate Verb | 🕐 00:32:00 | |
▶ | The Arrange Verb | 🕐 00:10:00 | |
▶ | The Summarize Verb | 🕐 00:23:00 | |
▶ | Data Pivoting | 🕐 00:43:00 | |
▶ | JSON Parsing | 🕐 00:11:00 | |
▶ | String Manipulation | 🕐 00:33:00 | |
▶ | Web Scraping | 🕐 00:59:00 | |
Data Visualization in R | |||
▶ | Data Visualization in R Section Intro | 🕐 00:17:00 | |
▶ | Getting Started | 🕐 00:16:00 | |
▶ | Aesthetics Mappings | 🕐 00:25:00 | |
▶ | Single Variable Plots | 🕐 00:37:00 | |
▶ | Two Variable Plots | 🕐 00:21:00 | |
▶ | Facets, Layering, and Coordinate Systems | 🕐 00:18:00 | |
▶ | Styling and Saving | 🕐 00:12:00 | |
Creating Reports with R Markdown | |||
▶ | Creating with R Markdown | 🕐 00:29:00 | |
Building Webapps with R Shiny | |||
▶ | Introduction to R Shiny | 🕐 00:26:00 | |
▶ | A Basic R Shiny App | 🕐 00:31:00 | |
▶ | Other Examples with R Shiny | 🕐 00:34:00 | |
Introduction to Machine Learning | |||
▶ | Machine Learning Part 1 | 🕐 00:22:00 | |
▶ | Machine Learning Part 2 | 🕐 00:47:00 | |
Starting A Career in Data Science | |||
▶ | Starting a Data Science Career Section Overview | 🕐 00:03:00 | |
▶ | Data Science Resume | 🕐 00:04:00 | |
▶ | Getting Started with Freelancing | 🕐 00:05:00 | |
▶ | Top Freelance Websites | 🕐 00:05:00 | |
▶ | Personal Branding | 🕐 00:05:00 | |
▶ | Importance of Website and Blo | 🕐 00:04:00 | |
▶ | Networking Do's and Don'ts | 🕐 00:04:00 |
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