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Become a professional Data Scientist with R and learn Machine Learning, Data Analysis + Visualization, Web Apps + more!

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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.

**Audience**: Consultants, Developers, End users, IT/Business Analysts, Project team members, system administrators**Prerequisites**: Basic computer skills

**What you'll learn/Goals:**

- 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)
- How to create resume and land your first job as a Data Scientist
- Step by step practical knowledge of R programming language
- Learn Machine Learning and it's various practical applications
- Building web apps and online, interactive dashboards with R Shiny
- Learn Data and File Management in R
- Use R to clean, analyze, and visualize data
- Learn the Tidyverse
- Learn Operators, Vectors, Lists and their application
- Data visualization (ggplot2)
- Data extraction and web scraping
- Full-stack data science development
- Building custom data solutions
- Automating dynamic report generation
- Data science for business

Data Science ML Course Intro - 02mins

What is data science - 09mins

Machine Learning Overview - 05mins

Who's this course is for - 02mins

DL and ML Marketplace - 04mins

Data Science and ML Job opportunities - 02mins

Data Science Job Roles - 04mins

Getting Started - 10mins

Basics - 06mins

Files - 11mins

RStudio - 06mins

Tidyverse - 05mins

Resources - 04mins

Section Introduction - 30mins

Basic Types - 08mins

Vectors Part One - 19mins

Vectors Part Two - 24mins

Vectors - Missing Values - 15mins

Vectors - Coercion - 14mins

Vectors - Naming - 10mins

Vectors - Misc - 05mins

Creating Matrices - 31mins

Lists - 31mins

Introduction to Data Frames - 19mins

Creating Data Frames - 19mins

Data Frames and Helper Functions - 31mins

Data Frames - Tibbles - 39mins

Section Introduction Intermediate R - 46mins

Relational Operations - 11mins

Logical Operators - 07mins

Conditional Statements - 11mins

Loops - 07mins

Functions - 14mins

Packages - 11mins

Factors - 28mins

Dates and Times - 30mins

Functional Programming - 36mins

Data Import or Export - 22mins

Database - 27mins

Data Manipulation in R Section Introduction - 36mins

Tidy Data - 10mins

The Pipe Operator - 14mins

The Filter Verb - 21mins

The Select Verb - 46mins

The Mutate Verb - 31mins

The Arrange Verb - 10mins

The Summarize Verb - 23mins

Data Pivoting - 42mins

JSON Parsing - 10mins

String Manipulation - 32mins

Web Scraping - 58mins

Data Visualization in R Section Introduction - 17mins

Getting Started - 15mins

Aesthetics Mappings - 24mins

Single Variables Plot - 36mins

Two Variable Plots - 20mins

Facets Layering and Coordinate System - 17mins

Styling and Saving - 11mins

Creating-Reports-with-R-Markdown - 28mins

Section-Introduction-With-R-Shiny - 26mins

A Basic App - 31mins

Other Examples - 34mins

Intro to Machine Learning - Part 1 - 21mins

Intro to Machine Learning - Part 2 - 46mins

Introduction to Data Preprocessing - 27mins

Data Preprocessing - 37mins

LR Section Introduction - 25mins

Linear Regression A Simple Model - 53mins

Section Introduction EDA - 25mins

Hands-on Exploratory Data Analysis - 01hrs 02mins

Linear Regression - Real Model Section Intro - 32mins

Linear Regression in R - real model - 52mins

Introduction to Logistic Regression - 37mins

Logistic Regression in R - 39mins

Starting a Career in Data Science1 - 02mins

Data Science Resume - 03mins

Getting Started with Freelancing - 04mins

Top Freelancing Websites - 05mins

Personal Branding - 05mins

Importance of Website and Blog - 03mins

Networking do's and dont's - 03mins

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.

Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skillsets.

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