Course Highlights
Data Science and Machine Learning are transforming industries worldwide. In the UK, the demand for data professionals has surged by over 231% in the last five years, with salaries ranging from £45,000 to £75,000 annually. Globally, companies rely on Data Science to drive innovation, optimize operations, and enhance decision-making. This course, Learn Data Science & Machine Learning with R from A-Z, introduces you to R Programming, a powerful tool used for statistical analysis and predictive modeling.
The World Economic Forum ranks Machine Learning as one of the top future-proof skills. With data-driven decision-making becoming essential, businesses seek professionals who understand data structures, visualization, and automation. R Programming provides the flexibility to manipulate data, build models, and create dynamic reports. Learning Data Science enhances job prospects in finance, healthcare, and e-commerce, with positions like Data Analyst and AI Engineer seeing exponential growth.
By enroling in this course, you will gain expertise in Machine Learning, data manipulation, and visualization using R Programming. Whether you’re starting your journey or looking to upskill, this course provides the essential knowledge to thrive in the evolving tech landscape.
Learning outcome
- Understand the fundamentals of Data Science and Machine Learning.
- Gain proficiency in R Programming for data analysis and visualization.
- Build interactive web applications using R Shiny.
- Create professional reports with R Markdown.
- Apply Machine Learning techniques to solve real-world problems.
- Implement data preprocessing techniques for accurate predictions.
Course media
Why should I take this course?
- Learn R Programming, a powerful language for data analysis.
- Master key Machine Learning techniques used by top industries.
- Enhance your ability to manipulate and visualize data effectively.
- Develop projects those applicable to various business domains.
- Gain industry-relevant skills for a career in Data Science.
Career Path
- Data Scientist
- Machine Learning Engineer
- AI Engineer
- Predictive Modeler
- Research Scientist
Requirements
- Basic understanding of mathematics and statistics.
- Willingness to learn Data Science concepts and techniques.
- A computer with internet access.
Course Curriculum
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Intro To DS+ML Section Overview
00:03:00 -
What is Data Science?
00:10:00
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Machine Learning Overview
00:05:00
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Who is this course for?
00:03:00
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Data Science + Machine Learning Marketplace
00:05:00
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DS+ ML Job Opportunities
00:03:00
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Data Science Job Roles
00:04:00
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Getting Started
00:11:00
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Basics
00:06:00
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Files
00:11:00
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R Studio
00:07:00
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Tidyverse
00:05:00
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Resources
00:04:00
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Section Introduction
00:30:00
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Basic Types
00:09:00
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Vectors Part One
00:20:00
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Vectors Part Two
00:25:00
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Vectors: Missing Values
00:16:00
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Vectors: Coercion
00:14:00
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Vectors: Naming
00:10:00
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Vectors: Misc.
00:06:00
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Matrices
00:31:00
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Lists
00:32:00
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Introduction to Data Frames
00:19:00
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Creating Data Frames
00:20:00
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Data Frames: Helper Functions
00:31:00
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Data Frames: Tibbles
00:39:00
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Section Introduction
00:47:00
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Relational Operators
00:11:00
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Logical Operators
00:07:00
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Conditional Statements
00:11:00
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Loops
00:08:00
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Functions
00:14:00
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Packages
00:11:00
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Factors
00:28:00
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Dates & Times
00:30:00
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Functional Programming
00:37:00
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Data Import/Export
00:22:00
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Databases
00:27:00
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Section Introduction
00:36:00
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Tidy Data
00:11:00
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The Pipe Operator
00:15:00
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{dplyr}: The Filter Verb
00:22:00
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{dplyr}: The Select Verb
00:46:00
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{dplyr}: The Mutate Verb
00:32:00
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{dplyr}: The Arrange Verb
00:10:00
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{dplyr}: The Summarize Verb
00:23:00
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Data Pivoting: {tidyr}
00:43:00
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String Manipulation: {stringr}
00:33:00
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Web Scraping: {rvest}
00:59:00
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JSON Parsing: {jsonlite}
00:11:00
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Getting Started
00:16:00
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Section Introduction
00:17:00
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Aesthetics Mappings
00:25:00
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Single Variable Plots
00:37:00
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Two-Variable Plots
00:21:00
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Facets, Layering, and Coordinate Systems
00:18:00
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Styling and Saving
00:12:00
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Intro To R Markdown
00:29:00
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Intro to R Shiny
00:26:00
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A Basic Webapp
00:31:00
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Other Examples
00:34:00
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Intro to ML Part 1
00:22:00
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Intro to ML Part 2
00:47:00
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Section Overview
00:27:00
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Data Preprocessing
00:38:00
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Section Introduction
00:25:00
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A Simple Model
00:53:00
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Section Introduction
00:25:00
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Hands-on Exploratory Data Analysis
01:03:00
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Section Introduction
00:32:00
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Linear Regression in R
00:53:00
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Logistic Regression Intro
00:38:00
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Logistic Regression in R
00:40:00
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Starting a Data Science Career Section Overview
00:03:00
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Creating A Data Science Resume
00:04:00
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Getting Started with Freelancing
00:05:00
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Top Freelance Websites
00:05:00
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Personal Branding
00:05:00
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Networking
00:04:00
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Setting Up a Website
00:04:00
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Duration:1 day, 4 hours
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Access:1 Year
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Units:82
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