Video 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.
What You Will Learn
- Learn data cleaning, processing, wrangling, and manipulation
- Learn plotting in R (graphs, charts, plots, histograms, and more)
- How to create a resume and land your first job as a data scientist
- Learn machine learning and its various practical applications
- Learn data and file management in R
- Use R to clean, analyze, and visualize data
Audience
This course is designed for beginners who want to learn about data science and machine learning. No prior knowledge of R is required.
About The Author
Juan E. Galvan: 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.
Table of contents
- Chapter 1 : Data Science and Machine Leaning Course Introduction
- Chapter 2 : Getting Started with R
-
Chapter 3 : Data Types and Structures in R
- Data Types and Structures in R Section Overview
- Basic Types
- Vectors - Part One
- Vectors - Part Two
- Vectors: Missing Values
- Vectors: Coercion
- Vectors: Naming
- Vectors: Miscellaneous
- Working with Matrices
- Working with Lists
- Introduction to Data Frames
- Creating Data Frames
- Data Frames: Helper Functions
- Data Frames: Tibbles
- Chapter 4 : Intermediate R
- Chapter 5 : Data Manipulation in R
- Chapter 6 : Data Visualization in R
- Chapter 7 : Creating Reports with R Markdown
- Chapter 8 : Building Webapps with R Shiny
- Chapter 9 : Introduction to Machine Learning
- Chapter 10 : Data Preprocessing
- Chapter 11 : Linear Regression: A Simple Model
- Chapter 12 : Exploratory Data Analysis
- Chapter 13 : Linear Regression - a Real Model
- Chapter 14 : Logistic Regression
- Chapter 15 : Starting a Career in Data Science
Product information
- Title: Data Science and Machine Learning with R from A-Z Course [Updated for 2021]
- Author(s):
- Release date: April 2021
- Publisher(s): Packt Publishing
- ISBN: 9781801075282
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