This course is designed for those interested to learn the basics of data science and machine learning, become a professional data scientist, write complex programs, learn data wrangling, and plotting in R. Read more.
Hi I'm Juan. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming.
Access all courses in our library for only $9/month with All Access Pass
Get Started with All Access PassBuy Only This CourseAbout This Course
Who this course is for:
- Students who want to learn about Data Science and Machine Learning
- Data analysts
- Data engineers
- Data scientists
What you’ll learn:Â
- Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
- How to write complex R programs for practical industry scenarios
- Learn data cleaning, processing, wrangling and manipulation
- Learn Plotting in R (graphs, charts, plots, histograms etc)
Requirements:Â
- No prior knowledge is required to take this course
In this practical, hands-on course, you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
The course covers practical issues in statistical computing which 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.
Our Promise to You
By the end of this course, you will have learned data science and machine learning in R.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today and learn more about data science.
Course Curriculum
| Section 1 - Data Science and Machine Learning Course Intro | |||
| Data Science And Machine Learning Intro Section Overview | 00:00:00 | ||
| What Is Data Science? | 00:00:00 | ||
| Machine Learning Overview | 00:00:00 | ||
| Data Science Plus Machine Learning Marketplace | 00:00:00 | ||
| Who Is This Course For? | 00:00:00 | ||
| Data Science And Machine Learning Job Opportunities | 00:00:00 | ||
| Section 2 - Getting Started with R | |||
| Getting Started With R | 00:00:00 | ||
| R Basics | 00:00:00 | ||
| Working With Files | 00:00:00 | ||
| The R Studio | 00:00:00 | ||
| Tidyverse Overview | 00:00:00 | ||
| Additional Resources | 00:00:00 | ||
| Section 3 - Data Types And Structures In R | |||
| Data Types And Structures in R Section Overview | 00:00:00 | ||
| Basic Types | 00:00:00 | ||
| Vectors Part One | 00:00:00 | ||
| Vectors Part Two | 00:00:00 | ||
| Vectors: Missing Values | 00:00:00 | ||
| Vectors: Coercion | 00:00:00 | ||
| Vectors: Naming | 00:00:00 | ||
| Vectors: Misc. | 00:00:00 | ||
| Working With Matrices | 00:00:00 | ||
| Working With Lists | 00:00:00 | ||
| Introduction To Data Frames | 00:00:00 | ||
| Creating Data Frames | 00:00:00 | ||
| Data Frames: Helper Functions | 00:00:00 | ||
| Data Frames: Tibbles | 00:00:00 | ||
| Section 4 - Intermediate R | |||
| Data Manipulation Section Intro | 00:00:00 | ||
| Relational Operators | 00:00:00 | ||
| Logical Operators | 00:00:00 | ||
| Conditional Statements | 00:00:00 | ||
| Working With Loops | 00:00:00 | ||
| Working With Functions | 00:00:00 | ||
| Working With Packages | 00:00:00 | ||
| Working With Factors | 00:00:00 | ||
| Dates And Times | 00:00:00 | ||
| Functional Programming | 00:00:00 | ||
| Data Import And Export | 00:00:00 | ||
| Working With Databases | 00:00:00 | ||
| Section 5 - Data Manipulation In R | |||
| Data Manipulation Section Intro | 00:00:00 | ||
| Tidy Data | 00:00:00 | ||
| The Pipe Operator | 00:00:00 | ||
| The Filter Verb: {dplyr} | 00:00:00 | ||
| The Select Verb: {dplyr} | 00:00:00 | ||
| The Mutate Verb: {dplyr} | 00:00:00 | ||
| The Arrange Verb: {dplyr} | 00:00:00 | ||
| The Summarize Verb: {dplyr} | 00:00:00 | ||
| Data Pivoting: {tidyr} | 00:00:00 | ||
| String Manipulation: {stringr} | 00:00:00 | ||
| Web Scraping: {rvest} | 00:00:00 | ||
| JSON Parsing: {jsonlite} | 00:00:00 | ||
| Section 6 - Data Visualization In R | |||
| Data Visualization In R Section Intro | 00:00:00 | ||
| Getting Started With Data Visualization In R | 00:00:00 | ||
| Aesthetics Mappings | 00:00:00 | ||
| Single Variable Plots | 00:00:00 | ||
| Two Variable Plots | 00:00:00 | ||
| Facets, Layering, And Coordinate Systems | 00:00:00 | ||
| Styling And Saving | 00:00:00 | ||
| Section 7 - Creating Reports With R Markdown | |||
| Introduction To R Markdown | 00:00:00 | ||
| Section 8 - Building Webapps With R Shiny | |||
| Introduction To R Shiny | 00:00:00 | ||
| Creating A Basic R Shiny App | 00:00:00 | ||
| Other Examples With R Shiny | 00:00:00 | ||
| Section 9 - Introduction To Machine Learning | |||
| Machine Learning Part One | 00:00:00 | ||
| Machine Learning Part Two | 00:00:00 | ||
| Section 10 - Starting A Career in Data Science | |||
| Starting A Data Science Career Section Overview | 00:00:00 | ||
| Creating A Data Science Resume | 00:00:00 | ||
| Getting Started With Freelancing | 00:00:00 | ||
| Top Freelance Websites | 00:00:00 | ||
| Personal Branding | 00:00:00 | ||
| Networking Do’s and Don’ts | 00:00:00 | ||
| Setting Up A Website | 00:00:00 | ||
About This Course
Who this course is for:
- Students who want to learn about Data Science and Machine Learning
- Data analysts
- Data engineers
- Data scientists
What you’ll learn:Â
- Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
- How to write complex R programs for practical industry scenarios
- Learn data cleaning, processing, wrangling and manipulation
- Learn Plotting in R (graphs, charts, plots, histograms etc)
Requirements:Â
- No prior knowledge is required to take this course
In this practical, hands-on course, you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
The course covers practical issues in statistical computing which 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.
Our Promise to You
By the end of this course, you will have learned data science and machine learning in R.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today and learn more about data science.
Course Curriculum
| Section 1 - Data Science and Machine Learning Course Intro | |||
| Data Science And Machine Learning Intro Section Overview | 00:00:00 | ||
| What Is Data Science? | 00:00:00 | ||
| Machine Learning Overview | 00:00:00 | ||
| Data Science Plus Machine Learning Marketplace | 00:00:00 | ||
| Who Is This Course For? | 00:00:00 | ||
| Data Science And Machine Learning Job Opportunities | 00:00:00 | ||
| Section 2 - Getting Started with R | |||
| Getting Started With R | 00:00:00 | ||
| R Basics | 00:00:00 | ||
| Working With Files | 00:00:00 | ||
| The R Studio | 00:00:00 | ||
| Tidyverse Overview | 00:00:00 | ||
| Additional Resources | 00:00:00 | ||
| Section 3 - Data Types And Structures In R | |||
| Data Types And Structures in R Section Overview | 00:00:00 | ||
| Basic Types | 00:00:00 | ||
| Vectors Part One | 00:00:00 | ||
| Vectors Part Two | 00:00:00 | ||
| Vectors: Missing Values | 00:00:00 | ||
| Vectors: Coercion | 00:00:00 | ||
| Vectors: Naming | 00:00:00 | ||
| Vectors: Misc. | 00:00:00 | ||
| Working With Matrices | 00:00:00 | ||
| Working With Lists | 00:00:00 | ||
| Introduction To Data Frames | 00:00:00 | ||
| Creating Data Frames | 00:00:00 | ||
| Data Frames: Helper Functions | 00:00:00 | ||
| Data Frames: Tibbles | 00:00:00 | ||
| Section 4 - Intermediate R | |||
| Data Manipulation Section Intro | 00:00:00 | ||
| Relational Operators | 00:00:00 | ||
| Logical Operators | 00:00:00 | ||
| Conditional Statements | 00:00:00 | ||
| Working With Loops | 00:00:00 | ||
| Working With Functions | 00:00:00 | ||
| Working With Packages | 00:00:00 | ||
| Working With Factors | 00:00:00 | ||
| Dates And Times | 00:00:00 | ||
| Functional Programming | 00:00:00 | ||
| Data Import And Export | 00:00:00 | ||
| Working With Databases | 00:00:00 | ||
| Section 5 - Data Manipulation In R | |||
| Data Manipulation Section Intro | 00:00:00 | ||
| Tidy Data | 00:00:00 | ||
| The Pipe Operator | 00:00:00 | ||
| The Filter Verb: {dplyr} | 00:00:00 | ||
| The Select Verb: {dplyr} | 00:00:00 | ||
| The Mutate Verb: {dplyr} | 00:00:00 | ||
| The Arrange Verb: {dplyr} | 00:00:00 | ||
| The Summarize Verb: {dplyr} | 00:00:00 | ||
| Data Pivoting: {tidyr} | 00:00:00 | ||
| String Manipulation: {stringr} | 00:00:00 | ||
| Web Scraping: {rvest} | 00:00:00 | ||
| JSON Parsing: {jsonlite} | 00:00:00 | ||
| Section 6 - Data Visualization In R | |||
| Data Visualization In R Section Intro | 00:00:00 | ||
| Getting Started With Data Visualization In R | 00:00:00 | ||
| Aesthetics Mappings | 00:00:00 | ||
| Single Variable Plots | 00:00:00 | ||
| Two Variable Plots | 00:00:00 | ||
| Facets, Layering, And Coordinate Systems | 00:00:00 | ||
| Styling And Saving | 00:00:00 | ||
| Section 7 - Creating Reports With R Markdown | |||
| Introduction To R Markdown | 00:00:00 | ||
| Section 8 - Building Webapps With R Shiny | |||
| Introduction To R Shiny | 00:00:00 | ||
| Creating A Basic R Shiny App | 00:00:00 | ||
| Other Examples With R Shiny | 00:00:00 | ||
| Section 9 - Introduction To Machine Learning | |||
| Machine Learning Part One | 00:00:00 | ||
| Machine Learning Part Two | 00:00:00 | ||
| Section 10 - Starting A Career in Data Science | |||
| Starting A Data Science Career Section Overview | 00:00:00 | ||
| Creating A Data Science Resume | 00:00:00 | ||
| Getting Started With Freelancing | 00:00:00 | ||
| Top Freelance Websites | 00:00:00 | ||
| Personal Branding | 00:00:00 | ||
| Networking Do’s and Don’ts | 00:00:00 | ||
| Setting Up A Website | 00:00:00 | ||
Related Courses
Are you interested in higher education?
All Rights Reserved