2021 Data Science & Machine Learning with R from A-Z Course

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2021 Data Science & Machine Learning with R from A-Z Course [TutsNode.com] - 2021 Data Science & Machine Learning with R from A-Z Course 5. Data Manipulation in R
  • 11. Web Scraping {rvest}.mp4 (545.1 MB)
  • 11. Web Scraping {rvest}.srt (75.8 KB)
  • 5. {dplyr} The Select Verb.srt (63.8 KB)
  • 9. Data Pivoting {tidyr}.srt (55.4 KB)
  • 1. Data Manipulation Section Intro.srt (45.2 KB)
  • 10. String Manipulation {stringr}.srt (41.6 KB)
  • 6. {dplyr} The Mutate Verb.srt (41.0 KB)
  • 8. {dplyr} The Summarize Verb.srt (29.8 KB)
  • 4. {dplyr} The Filter Verb.srt (29.2 KB)
  • 3. The Pipe Operator.srt (17.9 KB)
  • 2. Tidy Data.srt (13.5 KB)
  • 12. JSON Parsing {jsonlite}.srt (13.5 KB)
  • 7. {dplyr} The Arrange Verb.srt (12.3 KB)
  • 5. {dplyr} The Select Verb.mp4 (355.0 MB)
  • 1. Data Manipulation Section Intro.mp4 (315.4 MB)
  • 9. Data Pivoting {tidyr}.mp4 (293.4 MB)
  • 10. String Manipulation {stringr}.mp4 (257.0 MB)
  • 6. {dplyr} The Mutate Verb.mp4 (243.3 MB)
  • 4. {dplyr} The Filter Verb.mp4 (153.6 MB)
  • 8. {dplyr} The Summarize Verb.mp4 (142.0 MB)
  • 3. The Pipe Operator.mp4 (81.9 MB)
  • 12. JSON Parsing {jsonlite}.mp4 (81.2 MB)
  • 7. {dplyr} The Arrange Verb.mp4 (63.6 MB)
  • 2. Tidy Data.mp4 (54.6 MB)
12. Exploratory Data Analysis
  • 2. Hands-on Exploratory Data Analysis.srt (81.5 KB)
  • 2. Hands-on Exploratory Data Analysis.mp4 (467.7 MB)
  • 1. Exploratory Data Analysis Intro.srt (39.5 KB)
  • 1. Exploratory Data Analysis Intro.mp4 (208.0 MB)
11. Linear Regression A Simple Model
  • 2. A Simple Model.srt (71.8 KB)
  • 2. A Simple Model.mp4 (436.8 MB)
  • 1. Linear Regression A Simple Model Intro.srt (37.2 KB)
  • 1. Linear Regression A Simple Model Intro.mp4 (231.0 MB)
9. Introduction to Machine Learning
  • 2. Introduction to Machine Learning Part Two.srt (69.1 KB)
  • 1. Introduction to Machine Learning Part One.srt (33.2 KB)
  • 2. Introduction to Machine Learning Part Two.mp4 (427.1 MB)
  • 1. Introduction to Machine Learning Part One.mp4 (195.6 MB)
13. Linear Regression - A Real Model
  • 2. Linear Regression in R - Real Model.srt (68.8 KB)
  • 1. Linear Regression - Real Model Section Intro.srt (50.5 KB)
  • 2. Linear Regression in R - Real Model.mp4 (420.3 MB)
  • 1. Linear Regression - Real Model Section Intro.mp4 (295.0 MB)
4. Intermediate R
  • 1. Intermedia R Section Introduction.srt (63.8 KB)
  • 10. Functional Programming.srt (41.0 KB)
  • 9. Dates & Times.srt (38.5 KB)
  • 12. Working with Databases.srt (37.0 KB)
  • 8. Working with Factors.srt (35.9 KB)
  • 11. Data ImportExport.srt (25.9 KB)
  • 6. Working with Functions.srt (17.4 KB)
  • 7. Working with Packages.srt (15.4 KB)
  • 4. Conditional Statements.srt (14.0 KB)
  • 2. Relational Operators.srt (13.9 KB)
  • 5. Working with Loops.srt (9.0 KB)
  • 3. Logical Operators.srt (9.0 KB)
  • 1. Intermedia R Section Introduction.mp4 (420.9 MB)
  • 10. Functional Programming.mp4 (281.3 MB)
  • 12. Working with Databases.mp4 (208.0 MB)
  • 9. Dates & Times.mp4 (173.3 MB)
  • 8. Working with Factors.mp4 (160.4 MB)
  • 11. Data ImportExport.mp4 (130.5 MB)
  • 6. Working with Functions.mp4 (79.9 MB)
  • 7. Working with Packages.mp4 (76.5 MB)
  • 2. Relational Operators.mp4 (66.3 MB)
  • 4. Conditional Statements.mp4 (58.3 MB)
  • 3. Logical Operators.mp4 (43.4 MB)
  • 5. Working with Loops.mp4 (30.8 MB)
14. Logistic Regression
  • 2. Logistic Regression in R.srt (57.0 KB)
  • 1. Introduction to Logistic Regression.srt (56.5 KB)
  • 1. Introduction to Logistic Regression.mp4 (339.9 MB)
  • 2. Logistic Regression in R.mp4 (309.6 MB)
6. Data Visualization in R
  • 4. Single Variable Plots.srt (50.2 KB)
  • 3. Aesthetics Mappings.srt (35.2 KB)
  • 5. Two Variable Plots.srt (28.6 KB)
  • 1. Data Visualization in R Section Intro.srt (25.3 KB)
  • 6. Facets, Layering, and Coordinate Systems.srt (23.9 KB)
  • 2. Getting Started with Data Visualization in R.srt (21.7 KB)
  • 7. Styling and Saving.srt (15.7 KB)
  • 4. Single Variable Plots.mp4 (276.0 MB)
  • 3. Aesthetics Mappings.mp4 (176.9 MB)
  • 1. Data Visualization in R Section Intro.mp4 (146.4 MB)
  • 2. Getting Started with Data Visualization in R.mp4 (144.9 MB)
  • 6. Facets, Layering, and Coordinate Systems.mp4 (142.6 MB)
  • 5. Two Variable Plots.mp4 (137.0 MB)
  • 7. Styling and Saving.mp4 (69.5 MB)
  • 2.1 Comprehensive Guide to Data Visualization_R-11.pdf (6.0 MB)
3. Data Types and Structures in R
  • 14. Data Frames Tibbles.srt (47.2 KB)
  • 13. Data Frames Helper Functions.srt (39.5 KB)
  • 9. Working with Matrices.srt (38.9 KB)
  • 10. Working with Lists.srt (33.9 KB)
  • 4. Vectors Part Two.srt (30.8 KB)
  • 1. Data Types and Structures in R Section Overview.srt (26.7 KB)
  • 11. Introduction to Data Frames.srt (24.3 KB)
  • 12. Creating Data Frames.srt (23.8 KB)
  • 3. Vectors Part One.srt (20.3 KB)
  • 5. Vectors Missing Values.srt (19.0 KB)
  • 6. Vectors Coercion.srt (17.6 KB)
  • 2. Basic Types.srt (13.0 KB)
  • 7. Vectors Naming.srt (11.6 KB)
  • 8. Vectors Misc..srt (7.2 KB)
  • 14. Data Frames Tibbles.mp4 (288.6 MB)
  • 13. Data Frames Helper Functions.mp4 (197.4 MB)
  • 1. Data Types and Structures in R Section Overview.mp4 (187.7 MB)
  • 4. Vectors Part Two.mp4 (163.6 MB)
  • 10. Working with Lists.mp4 (153.6 MB)
  • 11. Introduction to Data Frames.mp4 (150.5 MB)
  • 9. Working with Matrices.mp4 (132.4 MB)
  • 12. Creating Data Frame

Description


Description

Welcome to the Learn Data Science and Machine Learning with R from A-Z 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.

Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.

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.

We understand that theory is important to build a solid foundation, we 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.

Together we’re going to give you the foundational education that you need to know not just on how to write code in R, analyze and visualize data but also how to get paid for your newly developed programming skills.

The course covers 6 main areas:

1: DS + ML COURSE + R INTRO

This intro section gives you a full introduction to the R programming language, data science industry and marketplace, job opportunities and salaries, and the various data science job roles.

Intro to Data Science + Machine Learning
Data Science Industry and Marketplace
Data Science Job Opportunities
R Introduction
Getting Started with R

2: DATA TYPES/STRUCTURES IN R

This section gives you a full introduction to the data types and structures in R with hands-on step by step training.

Vectors
Matrices
Lists
Data Frames
Operators
Loops
Functions
Databases + more!

3: DATA MANIPULATION IN R

This section gives you a full introduction to the Data Manipulation in R with hands-on step by step training.

Tidy Data
Pipe Operator
dplyr verbs: Filter, Select, Mutate, Arrange + more!
String Manipulation
Web Scraping

4: DATA VISUALIZATION IN R

This section gives you a full introduction to the Data Visualization in R with hands-on step by step training.

Aesthetics Mappings
Single Variable Plots
Two-Variable Plots
Facets, Layering, and Coordinate System

5: MACHINE LEARNING

This section gives you a full introduction to Machine Learning with hands-on step by step training.

Intro to Machine Learning
Data Preprocessing
Linear Regression
Logistic Regression
Support Vector Machines
K-Means Clustering
Ensemble Learning
Natural Language Processing
Neural Nets

6: STARTING A DATA SCIENCE CAREER

This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

Creating a Resume
Personal Branding
Freelancing + Freelance websites
Importance of Having a Website
Networking

By the end of the course you’ll be a professional Data Scientist with R and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.
Who this course is for:

Students who want to learn about Data Science and Machine Learning

Requirements

Basic computer skills

Last Updated 1/2021



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12.6 GB
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2021 Data Science & Machine Learning with R from A-Z Course


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