The ‘Data Science and Machine Learning Bootcamp with R Course’ is created by one of the top Udemy instructors Jose Portilla. In this course, students will learn how to use the R programming language for Data Science, Data Visualization, and Machine Learning. Compared to other Data Science courses on Udemy, it offers over 100 HD video lectures and detailed code notebooks for every lesson.
Originally, this course is priced between INR 2,000 to INR 3,000. Students can Enroll Now and get an exclusive discount of up to 90% off the regular price by clicking on the link.
Course Highlights
| Key Highlights | Details |
|---|---|
| Course Name | Data Science and Machine Learning Bootcamp with R |
| Github | https://github.com/alpha-nero1/2021-Python-for-Machine-Learning-Data-Science-Masterclass |
| Duration | 17 Hours |
| Rating | 4.7/5 |
| Student Enrollment | 84,592 students |
| Instructor | Jose Portilla |
| Topics Covered | R programming, advanced R features, using R data frames to solve complex tasks, using R to handle Excel files |
| Course Level | Intermediate |
| Total Student Reviews | 15,352 |
Learning Outcomes
- R Programming
- Use R for Data Analysis
- Create Data Visualizations
- Use R to handle CSV, Excel, SQL files, or web scraping
- Programming with R to manipulate data easily
- Utilizing R for Machine Learning Algorithms
- Make use of R for Data Science
Who all can opt for this course?
Anyone interested in becoming a Data Scientist
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Course Introduction (09 minutes) | Introduction to Course |
| Course Curriculum | ||
| What is Data Science? | ||
| Course FAQ | ||
| 2. | Course Best Practices (01 minute) | How to Get Help in the Course! |
| Welcome to the Course. | ||
| Installation and Set-Up | ||
| 3. | Windows Installation Set-Up (06 minutes) | Windows Installation Procedure |
| 4. | Mac OS Installation Set-Up (05 minutes) | Mac OS Installation Procedure |
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| 5. | Linux Installation | Linux/Unbuntu Installation Procedure |
| 6. | Development Environment Overview (21 minutes) | Development Environment Overview |
| Course Notes | ||
| Guide to RStudio | ||
| 7. | Introduction to R Basics (57 minutes) | Introduction to R Basics |
| Arithmetic in R | ||
| Variables | ||
| R Basic Data Types | ||
| Vector Basics | ||
| Vector Operations | ||
| Comparison Operators | ||
| Vector Indexing and Slicing | ||
| Getting Help with R and RStudio | ||
| R Basics Training Exercise | ||
| R Basics Training Exercise – Solutions Walkthrough | ||
| 8. | R Matrices (49 minutes) | Introduction to R Matrices |
| Creating a Matrix | ||
| Matrix Arithmetic | ||
| Matrix Operations | ||
| Matrix Selection and Indexing | ||
| Factor and Categorical Matrices | ||
| Matrix Training Exercise | ||
| Matrix Training Exercises – Solutions Walkthrough | ||
| 9. | R Data Frames (01 hour 09 minutes) | Introduction to R Data Frames |
| Data Frame Basics | ||
| Data Frame Indexing and Selection | ||
| Overview of Data Frame Operations – Part 1 | ||
| Overview of Data Frame Operations – Part 2 | ||
| Data Frame Training Exercise | ||
| Data Frame Training Exercises – Solutions Walkthrough | ||
| 10. | R Lists (09 minutes) | List Basics |
| 11. | Data Input and Output with R (35 minutes) | Introduction to Data Input and Output with R |
| CSV Files with R | ||
| Note on R with Excel Download | ||
| Excel Files with R | ||
| SQL with R | ||
| Web Scraping with R | ||
| 12. | R Programming Basics (01 hour 38 minutes) | Introduction to Programming Basics |
| Logical Operators | ||
| if, else, and else if Statements | ||
| Conditional Statements Training Exercise | ||
| Conditional Statements Training Exercise – Solutions Walkthrough | ||
| While Loops | ||
| For Loops | ||
| Functions | ||
| Functions Training Exercise | ||
| Functions Training Exercise – Solutions | ||
| 13. | Advanced R Programming (46 minutes) | Introduction to Advanced R Programming |
| Built-in R Features | ||
| Apply | ||
| Math Functions with R | ||
| Regular Expressions | ||
| Dates and Timestamps | ||
| 14. | Data Manipulation with R (57 minutes) | Data Manipulation Overview |
| Guide to Using Dplyr | ||
| Guide to Using Dplyr – Part 2 | ||
| Pipe Operator | ||
| Quick note on Dpylr exercise | ||
| Dplyr Training Exercise | ||
| Dplyr Training Exercise – Solutions Walkthrough | ||
| Guide to Using Tidyr | ||
| 15. | Data Visualization with R (01 hour 36 minutes) | Overview of ggplot2 |
| Histograms | ||
| Scatterplots | ||
| Barplots | ||
| Boxplots | ||
| 2 Variable Plotting | ||
| Coordinates and Faceting | ||
| Themes | ||
| ggplot2 Exercises | ||
| ggplot2 Exercise Solutions | ||
| 16. | Data Visualization Project (24 minutes) | Data Visualization Project |
| Data Visualization Project – Solutions Walkthrough – Part 1 | ||
| Data Visualization Project Solutions Walkthrough – Part 2 | ||
| 17. | Interactive Visualizations with Plotly (09 minutes) | Overview of Plotly and Interactive Visualizations |
| Resources for Plotly and ggplot2 | ||
| 18. | Capstone Data Project (29 minutes) | Introduction to Capstone Project |
| Capstone Project Solutions Walkthrough | ||
| 19. | Introduction to Machine Learning with R (17 minutes) | ISLR PDF |
| Introduction to Machine Learning | ||
| 20. | Machine Learning with R – Linear Regression (57 minutes) | Introduction to Linear Regression |
| Linear Regression with R – Part 1 | ||
| Linear Regression with R – Part 2 | ||
| Linear Regression with R – Part 3 | ||
| 21. | Machine Learning Project – Linear Regression (40 minutes) | Introduction to Linear Regression Project |
| ML – Linear Regression Project – Solutions Part 1 | ||
| ML – Linear Regression Project – Solutions Part 2 | ||
| 22. | Machine Learning with R – Logistic Regression (50 minutes) | Introduction to Logistic Regression |
| Logistic Regression with R – Part 1 | ||
| Logistic Regression with R – Part 2 | ||
| 23. | Machine Learning Project – Logistic Regression (49 minutes) | Introduction to Logistic Regression Project |
| Logistic Regression Project Solutions – Part 1 | ||
| Logistic Regression Project Solutions – Part 2 | ||
| Logistic Regression Project – Solutions Part 3 | ||
| 24. | Machine Learning with R – K Nearest Neighbors (24 minutes) | Introduction to K Nearest Neighbors |
| K Nearest Neighbors with R | ||
| 25. | Machine Learning Project – K Nearest Neighbors (14 minutes) | Introduction K Nearest Neighbors Project |
| K Nearest Neighbors Project Solutions | ||
| 26. | Machine Learning with R – Decision Trees and Random Forests (18 minutes) | Introduction to Tree Methods |
| Decision Trees and Random Forests with R | ||
| 27. | Machine Learning Project – Decision Trees and Random Forests (23 minutes) | Introduction to Decision Trees and Random Forests Project |
| Tree Methods Project Solutions – Part 1 | ||
| Tree Methods Project Solutions – Part 2 | ||
| 28. | Machine Learning with R – Support Vector Machines (19 minutes) | Introduction to Support Vector Machines |
| Support Vector Machines with R | ||
| 29. | Machine Learning Project – Support Vector Machines (23 minutes) | Introduction to SVM Project |
| Support Vector Machines Project – Solutions Part 1 | ||
| Support Vector Machines Project – Solutions Part 2 | ||
| 30. | Machine Learning with R – K-means Clustering (14 minutes) | Introduction to K-Means Clustering |
| K Means Clustering with R | ||
| 31. | Machine Learning Project – K-means Clustering (19 minutes) | Introduction to K Means Clustering Project |
| K Means Clustering Project – Solutions Walkthrough | ||
| 32. | Machine Learning with R – Natural Language Processing (25 minutes) | Introduction to Natural Language Processing |
| Natural Language Processing with R – Part 1 | ||
| Natural Language Processing with R – Part 2 | ||
| 33. | Machine Learning with R – Neural Nets (28 minutes) | Introduction to Neural Nets |
| Neural Nets with R | ||
| 34. | Machine Learning Project – Neural Nets (11 minutes) | Introduction to Neural Nets Project |
| Neural Nets Project – Solutions | ||
| 35. | Bonus Section | Bonus Lecture: |
Resources Required
- Computer Access with download privileges
- Basic Math Skills
Featured Review
Avijit D: By far the best course available on Udemy on R programming. A true gem for beginners. It has been a great leaning experience for me. The course starts with absolute basics and builds your skill gradually. All the way from Data Structures, Visualizations to advance Machine Learning concepts. It also has exercises and projects to test your newly acquired knowledge. Thank you Jose, for such an awesome course. Highly Recommended!!
Pros
- Anonymous User: Best classes for reviewing how to use R for data analysis.
- Andrey Cesar Vilchã: Excellent course for beginners to learn R and the basics of machine learning.
- Nitin Sharma: 4.5 stars for an excellent course in R and an introduction to ML concepts.
- Donald Bleyl: It would be excellent if there were optional lectures to bridge the gap between R commands and the underlying math.
Cons
- Aditya Oad: This is worst R programming bootcamp course one can take! Instructors doesn’t\nrespond to your questions and even the lectures aren’t updated.
- Josh Cubero: I watched 20%, hated it, now they won’t refund my money.
- Xander Crofts: All in all, the course isn’t bad, and I appreciate Jose’s work (I learned SQL from him too).
- Artashes Aleksanyan: The example and the data for making some analysis are very bad, which makes to hardly understand the points.
About the Author
- The instructor of this course is Jose Portilla who is the Head of Data Science at Pierian Training.
- With 4.6 Instructor Rating and 954,437 Reviews on Udemy, he offers 54 courses and has taught 3,052,886 students so far.
- Jose Portilla holds a BS and MS degree in mechanical engineering from Santa Clara University, and he has years of experience working as a qualified instructor and trainer for Python programming, machine learning, and data science.
- He has written articles and received patents in a number of disciplines, including data science, material science, and microfluidics.
- He has acquired a set of skills for data analysis throughout the course of his career, and he wants to combine both his teaching and data science knowledge to educate others the power of programming, how to analyse data, and how to display the data in attractive visualisations.
Comparison Table
| Parameters | Data Science and Machine Learning Bootcamp with R | R Programming: Advanced Analytics In R For Data Science | Python A-Z™: Python For Data Science With Real Exercises! |
|---|---|---|---|
| Offers | Students can join the course now and get an exclusive discount of up to 90% off the regular price by clicking on the link. | ||
| Registration Link | Apply Now! | Apply Now! | Apply Now! |
| Duration | 18 hours | 6 hours | 11 hours |
| Rating | 4.74/5 | 4.57/5 | 4.62/5 |
| Student Enrollments | 84,592 | 57,192 | 1,47,419 |
| Instructors | Jose Portilla | Kirill Eremenko | Kirill Eremenko |
Data Science and Machine Learning Bootcamp with R: FAQs
Ques. Can I learn machine learning with R?
Ans. The finest prototype for working with machine learning models is provided by the R language. The greatest tools and library packages for machine learning applications are available in the R language. These packages can be used by developers to build the optimal pre-model and post-models for machine learning applications.
Ques. Is bootcamp worth it for data science?
Ans. Yes, data science bootcamps and courses are becoming a more wise financial decision. Enrollment at data science bootcamps, with their emphasis on focused, hands-on, immersive learning, has increased as a result of the fact that many firms now prioritise demonstrable skills and expertise over merely credentialism.
Ques. Which bootcamp is best for data science?
Ans. Data Science and Machine Learning Bootcamp with R on Udemy is the best bootcamp for Data Science. The course is designed by one of the Udemy’s finest instructors Jose Portilla.
Ques. Is learning R necessary for data science?
Ans. R is crucial to data science. R is an interpreted language, thus you can run your code without using a compiler. As a result, code can be run without a compiler. R interprets the code and facilitates code development.
Ques. How to get a certificate from Udemy?
Ans. You can get a certificate of completion from Udemy after you complete a paid course. Once all of the course modules are completed, the trophy icon on the top right corner of the course preview window will change it’s color. You can click on the trophy icon and click on download icon to download the certificate in .pdf or .jpg format.
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