“Learn Data Science & Machine Learning with R from A-Z” welcomes the students to the R from A to Z course on Learning Data Science and Machine Learning. Students will learn how to program in R and how to utilize R for efficient data analysis, visualization, and practical data use in this hands-on, practical course. Students will learn how to set up the software required for a statistical programming environment, as well as how to define how high-level statistical languages apply common programming language principles. Students will learn how to program in R, load data into R, access R packages, writing R functions, debug R code, profile R code, organize R code, and comment on R code.
This course is for you whether you’ve never programmed before or want to learn more about the advanced features of the R programming language. In job listings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and many other positions, R coding experience is either necessary or encouraged. Together, you will be provided with the fundamental education you need to understand how to write R code, analyze data, and visualize it, as well as how to get paid for your newly acquired programming talents. The training covers six key topics: 1: ML Course + R Intro + DS The R programming language. Currently, udemy is offering the Learn Data Science & Machine Learning with R from A-Z for up to 87 % off i.e. INR 449 (INR 3,499).
Who Can Opt for This Course?
- Students interested in learning about machine learning and data science
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 28 Hours |
| Rating | 4.8/5 |
| Student Enrollment | 94,925 students |
| Instructor | Juan E. Galvan https://www.linkedin.com/in/juane.galvan |
| Topics Covered |
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| Course Level | N.A |
| Total Student Reviews | 1,296 |
Learning Outcomes
- Become a certified consultant, data scientist, data engineer, or data analyst
- How to create sophisticated R programs for real-world business problems
- Learn how to manipulate, clean, process, and wrangle data
- Learn how to plot in R (graphs, charts, plots, histograms, etc)
- Creating a résumé and getting your first job as a data scientist
- Practical understanding of the R programming language
- Learn about the numerous applications of machine learning
- Using R Shiny, you can create online dashboards and web applications
- Learn how to manage files and data in R
- R can be used to organize, examine, and display data
- Discover the Tidyverse
- Learn about operators, vectors, and lists, and how to use them
- Visualization of data (ggplot2)
- Web scraping and data extraction
- Development of a whole data science stack
- Creating unique data solutions
- Automating the creation of dynamic reports
- Data science for commercial use
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Data Science and Machine Learning Course Intro (27 minutes) | Data Science and Machine Learning Intro Section Overview |
| What is Data Science? | ||
| Machine Learning Overview | ||
| Data Science + Machine Learning Marketplace | ||
| Who is This Course For? | ||
| Data Science and Machine Learning Job Opportunities | ||
| 2. | Getting Started with R (44 minutes) | Getting Started with R |
| R Basics | ||
| Working with Files | ||
| R Studio | ||
| Tidyverse Overview | ||
| Additional Resources | ||
| 3. | Data Types and Structures in R (04 hours 56 minutes) | Data Types and Structures in R Section Overview |
| Basic Types | ||
| Vectors Part One | ||
| Vectors Part Two | ||
| Vectors: Missing Values | ||
| Vectors: Coercion | ||
| Vectors: Naming | ||
| Vectors: Misc. | ||
| Working with Matrices | ||
| Working with Lists | ||
| Introduction to Data Frames | ||
| Creating Data Frames | ||
| Data Frames: Helper Functions | ||
| Data Frames: Tibbles | ||
| 4. | Intermediate R (04 hours 14 minutes) | Intermedia R Section Introduction |
| Relational Operators | ||
| Logical Operators | ||
| Conditional Statements | ||
| Working with Loops | ||
| Working with Functions | ||
| Working with Packages | ||
| Working with Factors | ||
| Dates & Times | ||
| Functional Programming | ||
| Data Import/Export | ||
| Working with Databases | ||
| 5. | Data Manipulation in R (05 hours 39 minutes) | Data Manipulation Section Intro |
| 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} | ||
| 6. | Data Visualization in R (02 hours 24 minutes) | Data Visualization in R Section Intro |
| Getting Started with Data Visualization in R | ||
| Aesthetics Mappings | ||
| Single Variable Plots | ||
| Two Variable Plots | ||
| Facets, Layering, and Coordinate Systems | ||
| Styling and Saving | ||
| 7. | Creating Reports with R Markdown (28 minutes) | Introduction to R Markdown |
| 8. | Building Webapps with R Shiny (01 hour 31 minutes) | Introduction to R Shiny |
| Creating A Basic R Shiny App | ||
| Other Examples with R Shiny | ||
| 9. | Introduction to Machine Learning (01 hour 08 minutes) | Introduction to Machine Learning Part One |
| Introduction to Machine Learning Part Two | ||
| 10. | Data Preprocessing (01 hour 04 minutes) | Data Preprocessing Intro |
| Data Preprocessing | ||
| 11. | Linear Regression: A Simple Model (01 hour 18 minutes) | Linear Regression: A Simple Model Intro |
| A Simple Model | ||
| 12. | Exploratory Data Analysis (01 hour 28 minutes) | Exploratory Data Analysis Intro |
| Hands-on Exploratory Data Analysis | ||
| 13. | Linear Regression – A Real Model (01 hour 24 minutes) | Linear Regression – Real Model Section Intro |
| Linear Regression in R – Real Model | ||
| 14. | Logistic Regression (01 hour 17 minutes) | Introduction to Logistic Regression |
| Logistic Regression in R | ||
| 15. | Starting A Career in Data Science (29 minutes) | Starting a Data Science Career Section Overview |
| Creating A Data Science Resume | ||
| Getting Started with Freelancing | ||
| Top Freelance Websites | ||
| Personal Branding | ||
| Networking Do’s and Don’ts | ||
| Setting Up a Website |
Resources Required
- Simple computing abilities
Featured Review
Bharat Ratawa (5/5) : I am learning with this guy and he is just awesome tutor or say mentor to teach Language “R”. He explains simpler than the other people who teach. Thanks sir
Pros
- Peter Stewart (5/5): Great course on the fundamentals of data science and machine learning.
- Muhammad Hasnain (4/5): yes it was good work i have understand each and word of the video.
- Bilal Ahmed (5/5): it’s very exciting moment for me! amazing videos with best concept
- Nirav Patel (5/5): nice explanation what we going to do here so student think to buy or not
Cons
About the Author
The instructor of this course is Juan E. Galvan who is a Digital Entrepreneur Business Coach, with 4.5 Instructor Rating and 18,285 reviews on Udemy. He/She offers 15 Courses and has taught 513,290 students so far.
- Juan E. Galvan background is in technology, including programming, web development, digital marketing, and e-commerce
- Juan E. Galvan supports lifelong learning that offers the benefits of a university degree without the drawbacks of high expenses and ineffective teaching techniques
Comparison Table
| Parameters | Learn Data Science & Machine Learning with R from A-Z | Data Science Real World Projects in Python | Data Analysis Real world use-cases- Hands on Python |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 28.5 hours | 9.5 hours | 7 hours |
| Rating | 4.8 /5 | 4.3 /5 | 4.5 /5 |
| Student Enrollments | 94,925 | 79,617 | 64,324 |
| Instructors | Juan E. Galvan | Shan Singh | Shan Singh |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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