Data Science Machine Learning with R هذه الدورة تدرس باللغة الإنجليزية

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Overview

Data Science and Machine Learning with R - Level 3

Data science is a field of study that combines programming skills with the knowledge of mathematics and statistics to extract insights from data. It’s an interdisciplinary field that uses scientific methods, processes, algorithms and systems to convert insights from unstructured data to structured knowledge, that can help them make decisions and take action across a broad range of application domains.

Data science can be defined as a blend of mathematics; business acumen, tools, algorithms and machine learning techniques. All of these help us give meaning to raw data which can be of major use in the formation of big business decisions.

This course will facilitate your growth of data skills and enhance your career. In this non-technical course, you’ll be introduced to everything you need to know about this fast-growing and exciting field. Learn real-world applications and gain understanding through quick non-coding exercises.

Why You Should Consider Taking this Course at Study365?

The Data Science and Machine Learning with R Course is brought to you by Study365. We are a leading online education provider for several accredited organisations, and provide learners the opportunity to take this IAP course. At Study365, we give our fullest attention to the learners’ needs and ensure they have the necessary information required to proceed with the training.

Our priority is on the advancement of knowledge and skills, we are bound to facilitate everything required to confirm a top-notch education service. We vouch for our learners an exclusive learning experience by paying close attention to each of their unique requirements and coming up with solutions every time. We provide them with state-of-the-art facilities using the latest technology.

The benefits of studying with Study365 are ample. Here are just a few,

• Unlimited 12 months access from anywhere, anytime
• Save time and money on travel
• Learn at your own pace
• Versatile and motivated learning
• A culture of continuous improvement

Description

Learning Outcome

• Understand data cleaning, processing and manipulation
• Learn Data and File Management in R
• Give a head start for individuals who want to start a career in the field
• Learn how to write complex R programmes for practical industry scenarios

Curriculum

[Module 01]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

[Module 02]Getting Started with R

• Getting Started
• Basics
• Files
• R Studio
• Tidyverse
• Resources

[Module 03]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

[Module 04]Intermediate R

• Section Introduction
• Relational Operators
• Logical Operators
• Conditional Statements
• Loops
• Functions
• Packages
• Factors
• Dates & Times
• Functional Programming
• Data Import/Export
• Databases

[Module 05]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}

[Module 06]Data Visualization in R

• Section Introduction
• Getting Started
• Aesthetics Mappings
• Single Variable Plots
• Two-Variable Plots
• Facets, Layering, and Coordinate Systems
• Styling and Saving

[Module 07]Creating Reports with R Markdown

• Intro To R Markdown

[Module 08]Building Webapps with R Shiny

• Intro to R Shiny
• A Basic Webapp
• Other Examples

[Module 09]Introduction to Machine Learning

• Intro to ML Part 1
• Intro to ML Part 2

[Module 10]Data Preprocessing

• Section Overview
• Data Preprocessing

[Module 11]Linear Regression: A Simple Model

• Section Introduction
• A Simple Model

[Module 12]Exploratory Data Analysis

• Section Introduction
• Hands-on Exploratory Data Analysis

[Module 13]Linear Regression: A Real Model

• Section Introduction
• Linear Regression in R

[Module 14]Logistic Regression

• Logistic Regression Intro
• Logistic Regression in R

[Module 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

Method of assessment
This is a knowledge-based course, and thus, will contain no method of assessment.

Certification
Once the course is completed, the learners get awarded with a certificate of completion for ‘Data Science and Machine Learning with R - Level 3' by iAP.

Awarding Body
The International Awards for Professionals (iAP) is an awarding body established in 1999 that aims to promote a high educational standard. They hope to create online education that is trustworthy and credible. They are focused on raising the standards of online education, and ensuring it is accessible to all. The iAP provides accreditation for a range of educational establishments, and monitors and continually develops the educational standards of such institutions. Their globally recognised certifications allow learners to acquire the skills and knowledge needed to gain employment in the chosen fields. تحديث بتاريخ 29 February, 2024