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Become a professional Data Scientist with R and learn Machine Learning, Data Analysis + Visualisation, Web Apps + more! Earn 29 CPD credits from this CPD-accredited course.

Course Instructor

1 day, 5 hours

Last updated

July 21, 2023



Original price was: £25.99.Current price is: £12.99. inc. VAT

More about the course

In this practical, hands-on course developed and delivered by Juan Galvan, you will acquire comprehensive knowledge of Data Science & Machine Learning with R. Through interactive learning; you’ll gain proficiency in programming with R and effectively analyse and visualise data. We focus on the practical application of R to help you harness its power for real-world scenarios.
The course begins with setting up a statistical programming environment by installing and configuring the necessary software. We delve into the fundamentals of R programming, covering generic programming language concepts as implemented in this high-level statistical language.
Our primary objective is to equip you with a deep understanding of R and to transform you into a professional Data Scientist proficient in R, paving the way for your first job in this field.

What is R?

R coding is a powerful and versatile programming language widely used for statistical computing, data analysis, and visualisation. Its extensive collection of libraries and packages empowers researchers, data scientists, and analysts to tackle complex data challenges easily. Whether exploring datasets, building predictive models, or creating interactive visualisations, R's flexibility and community support make it an invaluable tool for unlocking data-driven insights. Its open-source nature fosters collaboration, making R a top choice for anyone seeking to harness the true potential of data.
Throughout the program, we address practical issues in statistical computing, including data manipulation, reading data into R, utilising R packages, creating custom functions, and effectively debugging and profiling R code. By blending theoretical knowledge with practical applications, we guide you from R Programming basics to mastery.
We recognise the significance of theory in building a solid foundation, but we also understand that practical skills are equally crucial. Therefore, this course includes numerous hands-on examples that you can follow step by step. Whether you already have coding experience or seek to explore advanced features of the R programming language, this course caters to your needs.
R coding expertise is required or highly recommended in job postings for data scientists, machine learning engineers, BIG data engineers, IT specialists, and database developers, among other data-related roles. Gaining proficiency in R will enhance your resume and open up opportunities in these specialised fields, where mastery of statistical techniques is essential.
Join us to gain the foundational education required for writing code in R and analysing data, turning your newly developed programming skills into a rewarding career. Our comprehensive approach ensures that you not only learn R but also know how to leverage your expertise to secure well-paid positions.

1: Data Sciense  + Machine Learning course  + R Intro

This intro section fully introduces the R programming language, the 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 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  Visualisation in R

This section gives you a full introduction to Data Visualization in R with hands-on, step-by-step training.
  • Aesthetics Mappings
  • Single Variable Plots
  • Two-Variable Plots
  • step-by-step 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 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.

What you will learn

  • 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 a resume and land your first job as a Data Scientist
  • Step-by-step practical knowledge of the R programming language
  • Learn Machine Learning, and its 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

This course is beneficial to:

This course on Data Science & Machine Learning with R is beneficial to a diverse range of individuals across various fields and professions. It caters to the following list of people:
  1. Aspiring Data Scientists: Individuals wishing to pursue a career in data science and develop a strong foundation in R programming for data analysis, visualisation, and machine learning.
  2. Current Data Analysts: Data analysts seeking to enhance their skills and proficiency by incorporating R into their data analysis and visualisation workflows.
  3. Machine Learning Enthusiasts: Those interested in machine learning and its applications, as the course covers practical implementations of machine learning algorithms in R.
  4. Professionals in IT and Software Development: IT specialists and software developers looking to expand their skill set and explore data science and machine learning concepts using R.
  5. Statisticians and Researchers: Statisticians and researchers who want to use R as a statistical programming language for data analysis and conducting statistical research.
  6. Big Data Engineers: Professionals working with big data and interested in integrating R into their data processing and analysis pipelines.
  7. Database Developers: Database developers who want to utilise R for data analysis and reporting tasks to gain valuable insights from databases.
  8. Business Analysts: Business analysts seeking to add advanced data analysis and visualisation capabilities to their toolkits to support informed decision-making.
  9. Students and Academics: Students and academics from various disciplines want to explore R’s power for data analysis, visualisation, and research purposes.
  10. Professionals in Finance and Economics: Individuals in the finance and economics sectors who want to leverage R for data analysis, forecasting, and financial modelling.
  11. Marketing and Market Research Professionals: Marketing and market research professionals aiming to use R for data-driven marketing strategies and market analysis.
  12. Healthcare and Biotechnology Professionals: Professionals in the healthcare and biotechnology fields are interested in using R for data analysis and research applications.
In summary, this course is to benefit anyone seeking to learn R for data science, machine learning, and statistical analysis, regardless of their current background or profession. Whether you are a beginner or an experienced professional, the course provides valuable knowledge and practical skills to succeed in data science.

Why Choose Data Science &  Machine Learning with R course?

  • Developed and delivered by Qualified industry expert Juan Galvan
  • Instant E-certificate of Completion
  • Entirely online
  • Self-paced learning and laptop, tablet, and smartphone-friendly
  • 24/7 Support

Career Prospect of this course:

Completing the Data Science & Machine Learning with R course opens up many promising career prospects for individuals. As the demand for data-driven insights and machine learning expertise grows across industries, those proficient in R programming and data science concepts are highly sought after. Here is a list of potential career prospects for individuals who complete this course:
  1. Data Scientist: As a data scientist, you will be responsible for collecting, analysing, and interpreting complex data to derive valuable insights and make data-driven decisions. You will use R for data manipulation, statistical analysis, and machine learning modelling.
  2. Machine Learning Engineer: Machine learning engineers design and develop machine learning algorithms and models to automate processes and improve efficiency. Proficiency in R allows you to implement and evaluate machine learning algorithms effectively.
  3. Data Analyst: Data analysts use R to clean, process, and visualise data to help businesses make informed decisions. They identify trends, patterns, and correlations in data sets to support various organisational departments.
  4. Business Intelligence Analyst: BI analysts use R to analyse data and create reports and dashboards that provide critical business insights to stakeholders. They contribute to strategic planning and help optimise business operations.
  5. Statistical Analyst: Statistical analysts apply statistical techniques and methods to interpret data and solve complex problems. R is a valuable tool for conducting statistical analysis and hypothesis testing.
  6. Market Research Analyst: Analysts use R to analyse market trends, consumer behaviour, and competitive landscapes. They provide valuable insights to businesses to make informed marketing and sales decisions.
  7. Financial Analyst: Financial analysts leverage R to analyse financial data, perform risk assessments, and create economic models for forecasting and investment decisions.
  8. Data Engineer: Data engineers use R for data processing, transformation, and integration tasks. They ensure data pipelines are robust and efficient to support data analysis and machine learning workflows.
  9. Healthcare Data Analyst: In the healthcare industry, data analysts use R to analyse patient data, medical records, and clinical trial results to support medical research and improve patient outcomes.
  10. Research Scientist: Research scientists in various fields, such as social sciences, environmental sciences, and biological sciences, use R for data analysis and statistical modelling to draw meaningful conclusions from research data.
  11. Marketing Analyst: Analysts use R to analyse marketing campaign data, customer behaviour, and market trends to optimise marketing strategies and increase ROI.
  12. Data Visualisation Specialist: Data visualisation specialists create compelling visual representations of data using R to convey complex insights effectively to stakeholders.
  13. Business Analyst: Business analysts use R to analyse data and create data-driven reports to help organisations make strategic decisions.
These career prospects highlight the versatility and demand for individuals skilled in R programming and data science. With this course’s completion, you can position yourself for success in various industries and secure a rewarding career in the dynamic field of data science and machine learning.

Industry Expert Instructor

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Course Content

    • Data Science and Machine Learning Intro Section Overview 00:03:00
    • What is Data Science? 00:10:00
    • Machine Learning Overview 00:06:00
    • Data Science + Machine Learning Marketplace 00:05:00
    • Who is This Course For? 00:03:00
    • Data Science and Machine Learning Job Opportunities 00:03:00
    • Getting Started with R 00:11:00
    • R Basics 00:07:00
    • Working with Files 00:12:00
    • R Studio 00:07:00
    • Tidyverse Overview 00:06:00
    • Additional Resources 00:05:00
    • Data Types and Structures in R Section Overview 00:31:00
    • Basic Types 00:09:00
    • Vectors Part One 00:20:00
    • Vectors Part Two 00:25:00
    • Vectors: Missing Values 00:16:00
    • Vectors: Coercion 00:15:00
    • Vectors: Naming 00:11:00
    • Vectors: Misc. 00:06:00
    • Working with Matrices 00:32:00
    • Working with Lists 00:32:00
    • Introduction to Data Frames 00:20:00
    • Creating Data Frames 00:20:00
    • Data Frames: Helper Functions 00:32:00
    • Data Frames: Tibbles 00:40:00
    • Intermedia R Section Introduction 00:47:00
    • Relational Operators 00:12:00
    • Logical Operators 00:08:00
    • Conditional Statements 00:12:00
    • Working with Loops 00:08:00
    • Working with Functions 00:15:00
    • Working with Packages 00:12:00
    • Working with Factors 00:29:00
    • Dates & Times 00:31:00
    • Functional Programming 00:37:00
    • Data Import/Export 00:23:00
    • Working with Databases 00:28:00
    • Data Manipulation Section Intro 00:37:00
    • Tidy Data 00:11:00
    • The Pipe Operator 00:15:00
    • {dplyr}: The Filter Verb 00:22:00
    • {dplyr}: The Select Verb 00:47:00
    • {dplyr}: The Mutate Verb 00:32:00
    • {dplyr}: The Arrange Verb 00:11:00
    • {dplyr}: The Summarize Verb 00:24:00
    • Data Pivoting: {tidyr} 00:43:00
    • String Manipulation: {stringr} 00:33:00
    • Web Scraping: {rvest} 00:59:00
    • JSON Parsing: {jsonlite} 00:11:00
    • Data Visualization in R Section Intro 00:25:00
    • Getting Started with Data Visualization in R 00:16:00
    • Aesthetics Mappings 00:25:00
    • Single Variable Plots 00:37:00
    • Two Variable Plots 00:21:00
    • Facets, Layering, and Coordinate Systems 00:18:00
    • Styling and Saving 00:12:00
    • Introduction to R Markdown 00:29:00
    • Introduction to R Shiny 00:27:00
    • Creating A Basic R Shiny App 00:32:00
    • Other Examples with R Shiny 00:35:00
    • Introduction to Machine Learning Part One 00:22:00
    • Introduction to Machine Learning Part Two 00:47:00
    • Data Preprocessing Intro 00:28:00
    • Data Preprocessing 00:38:00
    • Linear Regression: A Simple Model Intro 00:54:00
    • A Simple Model 00:54:00
    • Exploratory Data Analysis Intro 00:26:00
    • Hands-on Exploratory Data Analysis 01:03:00
    • Linear Regression – Real Model Section Intro 00:33:00
    • Linear Regression in R – Real Model 00:53:00
    • Introduction to Logistic Regression 00:38:00
    • Logistic Regression in R 00:40:00
    • Starting a Data Science Career Section Overview 00:03:00
    • Creating A Data Science Resume 00:04:00
    • Getting Started with Freelancing 00:05:00
    • Top Freelance Websites 00:06:00
    • Personal Branding 00:06:00
    • Networking Do’s and Don’ts 00:04:00
    • Setting Up a Website 00:04:00
    • Get Your Certificate & Transcript 00:00:00

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