Data Science and Machine Learning with R - Level 3
4.5( 4 REVIEWS )
166 STUDENTS
Overview Data science is a field of study that combines programming skills with the knowledge of mathematics and statistics to extract insights …
Overview
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
- About the Tutor
- Learning Outcome
- Who is this qualification for?
- Prerequisites to take the course
- Method of assessment
- Certification
- Awarding Body
- Prospective careers
Juan Galvan is a visionary, marketer and digital entrepreneur. He has been effective in enabling digital businesses to reach the next level of success. He believes in continued education and wants to share his extensive knowledge and experience as a coach, consultant and strategist with others. He aims to enable learners to expand their skill set in digital marketing, web development, programming and e-commerce. Juan Galvan will guide you to make critical business decisions, develop unique ways to deliver products in the marketplace and have clarity and confidence in your business.
- 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
- Individuals who want to make a career in data science.
- Individuals who want to learn about data science.
- There are no pre-requisites to follow this course
- Learners should be over the age of 16
This is a knowledge-based course, and thus, will contain no method of assessment.
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.
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.
On successful completion of this course, learners will have the knowledge and skills to enter the relevant job market, with the confidence to explore a wide range of industry-related professions. You can study related courses that will open the door to new and exciting opportunities and enhance your expertise in this subject, and add this as a skillset on your resume. Your skills will be recognised by top employers and organisations that will enable you to land a generous-paying job, gain plenty of benefits, and a wide array of opportunities. Given below are job titles you can compete for.
- Data Scientist
- Data Engineer
- Data Analyst
- Consultant
Course Curriculum
| 1. Introduction to Data Science +ML with R from A-Z | |||
| 1.1. Intro To DS+ML Section Overview | FREE | 00:03:00 | |
| 1.2. What is Data Science? | FREE | 00:10:00 | |
| 1.3. Machine Learning Overview | FREE | 00:05:00 | |
| 1.4. Who is this course for? | FREE | 00:03:00 | |
| 1.5. Data Science + Machine Learning Marketplace | FREE | 00:05:00 | |
| 1.6. DS+ ML Job Opportunities | FREE | 00:03:00 | |
| 1.7. Data Science Job Roles | 00:04:00 | ||
| 2. Getting Started with R | |||
| 2.1. Getting Started | 00:11:00 | ||
| 2.2. Basics | 00:06:00 | ||
| 2.3. Files | 00:11:00 | ||
| 2.4. R Studio | 00:07:00 | ||
| 2.5. Tidyverse | 00:05:00 | ||
| 2.6. Resources | 00:04:00 | ||
| 3. Data Types and Structures in R | |||
| 3.1. Section Introduction | 00:30:00 | ||
| 3.2. Basic Types | 00:09:00 | ||
| 3.3. Vectors Part One | 00:20:00 | ||
| 3.4. Vectors Part Two | 00:25:00 | ||
| 3.5. Vectors: Missing Values | 00:16:00 | ||
| 3.6. Vectors: Coercion | 00:14:00 | ||
| 3.7. Vectors: Naming | 00:10:00 | ||
| 3.8. Vectors: Misc. | 00:06:00 | ||
| 3.9. Matrices | 00:31:00 | ||
| 3.10. Lists | 00:32:00 | ||
| 3.11. Introduction to Data Frames | 00:19:00 | ||
| 3.12. Creating Data Frames | 00:20:00 | ||
| 3.13. Data Frames: Helper Functions | 00:31:00 | ||
| 3.14. Data Frames: Tibbles | 00:39:00 | ||
| 4. Intermediate R | |||
| 4.1. Section Introduction | 00:47:00 | ||
| 4.2. Relational Operators | 00:11:00 | ||
| 4.3. Logical Operators | 00:07:00 | ||
| 4.4. Conditional Statements | 00:11:00 | ||
| 4.5. Loops | 00:08:00 | ||
| 4.6. Functions | 00:14:00 | ||
| 4.7. Packages | 00:11:00 | ||
| 4.8. Factors | 00:28:00 | ||
| 4.9. Dates & Times | 00:30:00 | ||
| 4.10. Functional Programming | 00:37:00 | ||
| 4.11. Data Import/Export | 00:22:00 | ||
| 4.12. Databases | 00:27:00 | ||
| 5. Data Manipulation in R | |||
| 5.1. Section Introduction | 00:36:00 | ||
| 5.2. Tidy Data | 00:11:00 | ||
| 5.3. The Pipe Operator | 00:15:00 | ||
| 5.4. {dplyr}: The Filter Verb | 00:22:00 | ||
| 5.5. {dplyr}: The Select Verb | 00:46:00 | ||
| 5.6. {dplyr}: The Mutate Verb | 00:32:00 | ||
| 5.7. {dplyr}: The Arrange Verb | 00:10:00 | ||
| 5.8. {dplyr}: The Summarize Verb | 00:23:00 | ||
| 5.9. Data Pivoting: {tidyr} | 00:43:00 | ||
| 5.10. String Manipulation: {stringr} | 00:33:00 | ||
| 5.11. Web Scraping: {rvest} | 00:59:00 | ||
| 5.12. JSON Parsing: {jsonlite} | 00:11:00 | ||
| 6. Data Visualization in R | |||
| 6.1. Section Introduction | 00:17:00 | ||
| 6.2. Getting Started | 00:16:00 | ||
| 6.3. Aesthetics Mappings | 00:25:00 | ||
| 6.4. Single Variable Plots | 00:37:00 | ||
| 6.5. Two-Variable Plots | 00:21:00 | ||
| 6.6. Facets, Layering, and Coordinate Systems | 00:18:00 | ||
| 6.7. Styling and Saving | 00:12:00 | ||
| 7. Creating Reports with R Markdown | |||
| 7.1. Intro To R Markdown | 00:29:00 | ||
| 8. Building Webapps with R Shiny | |||
| 8.1. Intro to R Shiny | 00:26:00 | ||
| 8.2. A Basic Webapp | 00:31:00 | ||
| 8.3. Other Examples | 00:34:00 | ||
| 9. Introduction to Machine Learning | |||
| 9.1. Intro to ML Part 1 | 00:22:00 | ||
| 9.2. Intro to ML Part 2 | 00:47:00 | ||
| 10. Data Preprocessing | |||
| 10.1. Section Overview | 00:27:00 | ||
| 10.2. Data Preprocessing | 00:38:00 | ||
| 11. Linear Regression: A Simple Model | |||
| 11.1. Section Introduction | 00:25:00 | ||
| 11.2. A Simple Model | 00:53:00 | ||
| 12. Exploratory Data Analysis | |||
| 12.1. Section Introduction | 00:25:00 | ||
| 12.2. Hands-on Exploratory Data Analysis | 01:03:00 | ||
| 13. Linear Regression: A Real Model | |||
| 13.1. Section Introduction | 00:32:00 | ||
| 13.2. Linear Regression in R | 00:53:00 | ||
| 14. Logistic Regression | |||
| 14.1. Logistic Regression Intro | 00:38:00 | ||
| 14.2. Logistic Regression in R | 00:40:00 | ||
| 15. Starting a Career in Data Science | |||
| 15.1. Section Overview | 00:03:00 | ||
| 15.2. Creating A Data Science Resume | 00:04:00 | ||
| 15.3. Getting Started with Freelancing | 00:05:00 | ||
| 15.4. Top Freelance Websites | 00:05:00 | ||
| 15.5. Personal Branding | 00:05:00 | ||
| 15.6. Networking | 00:04:00 | ||
| 15.7. Setting Up a Website | 00:04:00 | ||
| Completion Certificate Request | |||
| Completion Certificate Request | |||
Students feedback
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Blake Elliott
I had zero knowledge of machine learning. Now I feel stronger with this course and certificate.
Mason Taylor
A well-designed platform to learn data science.
Taylor Berry
Good understanding of machine learning with examples and lectures.
Carol Robertson
The information is up-to-date and deep.