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Data Science and Machine Learning with R - Level 3

4.5( 4 REVIEWS )
165 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

4.5

Average rating (4)
4.5
5 Star
4 Star
3 Star
2 Star
1 Star
    B E

    Blake Elliott

    October 14, 2021
    Zero to advance

    I had zero knowledge of machine learning. Now I feel stronger with this course and certificate.

    M T

    Mason Taylor

    September 10, 2021
    Thanks

    A well-designed platform to learn data science.

    T B

    Taylor Berry

    August 18, 2021
    Good level 3 course.

    Good understanding of machine learning with examples and lectures.

    C R

    Carol Robertson

    August 14, 2021
    Deep

    The information is up-to-date and deep.

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