Complete Data Science and Machine Learning with R

The Complete Data Science and Machine Learning with R course is a practical learning course in which you will learn how to program in R, R studio, data types and structures in R, data manipulation in R, data visualization in R, linear regression in R, logistic regression in R and more use of R in data science and machine learning wholly and how to use that data practically.

In this Complete Data Science and Machine Learning with R course, we will not only provide you the knowledge of data science and machine learning with R but also show you the opportunities and ways to build a professional career in the related job fields.

This Complete Data Science and Machine Learning with R course covers real-life issues in creating data frames, functional programming, the pipe operator, data preprocessing, linear regression, and various problems regarding machine learning. This Complete Data Science and Machine Learning with R course is a proper mix-up of practical work with appropriate theoretical knowledge which provides you with the best learning experience and will help you to build a great career in the fields of data science and machine learning with R.

Practical experience along with theoretical knowledge can make you the master of data science and machine learning with R but you may not find your desired job or may not prosper in your related existing career if you are not aware of the exact job field related to data science and machine learning with R. This Complete Data Science and Machine Learning with R course will also provide you with information about related job fields, how to network, how to do personal branding and other job opportunities and the way of starting a successful career in data science and machine learning with R.

What you will learn from this course:

• Learn skills to become a professional data scientist, data analyst, and data engineer.
• Learn to write complex R programs in real-life circumstances.
• Learn data framing, data export/import, and data visualization.
• Learn R studio, data types and structures in R, data manipulation in R, data visualization in R, linear regression in R, and logistic regression in R.

• Section 1 22:02

  • 1: 1 - Beginners Guide To Juicing 05:40
  • 2: 2 - The Science Of Juicing 09:26
  • 3: 3 - Juice Cleanes 06:56

• Section 2 17:12

  • 4: 4 - Juicing _ Anti-Aging 03:21
  • 5: 5 - Juicing _ Energy 04:28
  • 6: 6 - Store Bought Or Homemade - Pros _ Cons 09:23

• Section 3 13:02

  • 7: 7 - The Many Benefits Of Juicing 09:09
  • 8: 8 - Great Recipes For Juicing 03:53

• Section 4 18:51

  • 9: 9 - Going Beyond Juicing 07:46
  • 10: 10 - Why Your Healthy Lifestyle Is Important 06:34
  • 11: Promo - Juicing For Health _ Longevity-1 04:31

Program content:

• Section 1: Introduction to Data Science +ML with R from A-Z
  
  • Intro To DS+ML Section Overview
  • What is Data Science?
  • Machine Learning Overview
  • Who is this course for?
  • Data Science + Machine Learning Marketplace
  • DS+ ML Job Opportunities
  • Data Science Job Roles
• Section 2: Getting Started with R
  
  • Getting Started
  • Basics
  • Files
  • R Studio
  • Tidyverse
  • Resources
• Section 3: Data Types and Structures in R
  
  • Section Introduction
  • Basic Types
  • Vectors Part One
  • Vectors Part Two
  • Vectors: Missing Values
  • Vectors: Coercion
  • Vectors: Naming
  • Vectors: Misc.
  • Matrices
  • Lists
  • Introduction to Data Frames
  • Creating Data Frames
  • Data Frames: Helper Functions
  • Data Frames: Tibbles
• Section 4: Intermediate R
  
  • Section Introduction
  • Relational Operators
  • Logical Operators
  • Conditional Statements
  • Loops
  • Functions
  • Packages
  • Factors
  • Dates & Times
  • Functional Programming
  • Data Import/Export
  • Databases
• Section 5: Data Manipulation in R
  
  • Section Introduction
  • Tidy Data
  • The Pipe Operator
  • {dplyr}: The Filter Verb
  • {dplyr}: The Select Verb
  • {dplyr}: The Mutate Verb
  • {dplyr}: The Arrange Verb
  • {dplyr}: The Summarize Verb
  • Data Pivoting: {tidyr}
  • String Manipulation: {stringr}
  • Web Scraping: {rvest}
  • JSON Parsing: {jsonlite}
• Section 6: Data Visualization in R
  
  • Section Introduction
  • Getting Started
  • Aesthetics Mappings
  • Single Variable Plots
  • Two-Variable Plots
  • Facets, Layering, and Coordinate Systems
  • Styling and Saving
• Section 7: Creating Reports with R Markdown
  
  • Intro to R Markdown
• Section 8: Building Web Apps with R Shiny
  
  • Intro to R Shiny
  • A Basic Webapp
  • Other Examples
• Section 9: Introduction to Machine Learning
  
  • Intro to ML Part 1
  • Intro to ML Part 2
• Section 10: Data Preprocessing
  
  • Section Overview
  • Data Preprocessing
• Section 11: Linear Regression: A Simple Model
  
  • Section Introduction
  • A Simple Model
• Section 12: Exploratory Data Analysis
  
  • Section Introduction
  • Hands-on Exploratory Data Analysis
• Section 13: Linear Regression: A Real Model
  
  • Section Introduction
  • Linear Regression in R
• Section 14: Logistic Regression
  
  • Logistic Regression Intro
  • Logistic Regression in R
• Section 15: Starting a Career in Data Science
  
  • Section Overview
  • Creating A Data Science Resume
  • Getting Started with Freelancing
  • Top Freelance Websites
  • Personal Branding
  • Networking
  • Setting Up a Website

Why Blackboard learning:

Blackboard Learning is an online learning platform by which students from any corner of the world can learn his/her desired course. Using online learning, we assist students in realizing their full potential and advancing their careers. Today, our goal is to be the world's leading provider of online learning experiences with a global impact. By leveraging online learning, we assist students in preparing for bright futures in world-changing jobs. We provide a wide range of categories including Accounting & IT, Programming, Creative, and more. Our courses are designed to stretch students intellectually through state-of-the-art online learning.

• Anyone who has a desire to build a career as a data analyst, data engineer, or data scientist.
• Anyone who is already in a job with a data science background but wants to add value to his/her career by learning machine learning.
• Anyone who can work with small data but wants to work with big data.
• Anyone who wants to learn programming with R.

No prior knowledge or experience required

• Learn about data science job roles and job opportunities.
• Learn machine learning and its various practical applications.
• Learn the Tidyverse.
• Learn Operators, Vectors, Lists, and their applications.

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Certificate of completion

Digital certificate - Included

Reed Courses Certificate of Completion

Digital certificate - Included