Data Science and Machine Learning Masterclass with R will teach students everything they need to know to become a master in data science. Students will learn the advantages of artificial intelligence, machine learning, and data science. The course also teaches the best directions for becoming a Data Scientist, with the tips for preparing for the Data Science Interview.
The course will also teach about data science and how it benefits the contemporary society. Students will be able to use R Programming to address Data Science-related issues and will also be able to plot various types of data & derive conclusions using: Line Chart, Bar Chart, Pie Chart, Histogram, Density Plot, Box Plot, 3D Plot, Mosaic Plot, etc. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get Data Science and Machine Learning Masterclass with R for INR 449.
Who all can opt for this course?
- This course is open to everyone with an interest in data science
- Data scientists in training
- This course should be taken by anyone who wishes to change careers and work in data science, analytics, or machine learning
- Beginners in programming of any kind who are curious about the fascinating fields of artificial intelligence, machine learning, and data science
- Students with an interest in data analysis and statistics
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 12.5 Hours |
| Rating | 4.5/5 |
| Student Enrollment | 9,431 students |
| Instructor | Up Degree https://www.linkedin.com/in/updegree |
| Topics Covered | Data Science, Data Visualization, Data Manipulation in R, Data Analytics, R Programming |
| Course Level | Beginner |
| Total Student Reviews | 259 |
Learning Outcomes
- Find out what data science is and how it benefits the contemporary society
- What are the advantages of machine learning and data science
- R programming is able to solve problems related to data science
- Why R is a Must Have for Machine Learning, AI, and Data Science!
- How to Choose the Best Route to Become a Data Scientist + Interview preparation for data scientists
- How can I change my Data Science career?
- Work with the loops, functions, and conditional statements provided by R
- Explore data in R systematically
- Data science toolkit: GGPlot 2 with dplyr
- Data by index, slice, and subset
- Use CSV, Excel, databases, the web, and text data to enter and exit data into R
- Plotting various forms of data to gain insights using tools including line charts, bar graphs, pie charts, histograms, density plots, box plots, 3D plots, and mosaic plots is known as data visualisation
- Apply the mutate(), filter(), arrange (), summarise(), groupby(), and date functions in R to manipulate data
- Enjoy using actual Life Data Sets
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | *************Section Zero ********* (02 minutes) | Meet Your Instructor |
| 2. | **********************Introduction to Data Science *************************** (01 hour 19 minutes) | Introduction to Business Analytics |
| Introduction to Business Analytics | ||
| Introduction to Machine Learning | ||
| Introduction to Machine Learning | ||
| Introduction To Data Scientist | ||
| Introduction To Data Scientist | ||
| How to switch your career into ML | ||
| How to switch your career into ML | ||
| Data Science Career Part #2 | ||
| How to switch your career into ML 2 | ||
| 3. | Course Curriculum Overview (10 minutes) | Course Curriculum Overview |
| 4. | INTRODUCTION TO R (31 minutes) | Introduction to R |
| Introduction to R | ||
| Setting up R | ||
| 5. | R Programming (01 hour 14 minutes) | R Operator |
| R Conditional Statement & Loop | ||
| R Conditional Statement & Loop Study Note | ||
| R Function | ||
| R Programming – Function in R Study Note | ||
| R Function Part #2 | ||
| 11-R Programming – R Function Study Note | ||
| R Function Part #3 | ||
| 12-R Programming-Writing Function Study Note | ||
| All Codes : R Programming Study Note | ||
| 6. | R Data Structure (01 hour 09 minutes) | R Data Structure – Vector |
| Vector Study Note | ||
| Codes – Vector | ||
| Matrix, Array and Data Frame | ||
| Matrix, Array , Data Frame Study Note | ||
| Codes – Matrix, Array and Data Frame | ||
| A Deep Drive to R Data Frame | ||
| A deep drive into data frames Study Notes | ||
| CODES – A Deep Drive to R Data Frame | ||
| R Data Structure – Factor | ||
| 16.R Data Structure – Factor | ||
| CODES – Factor | ||
| R Data Structure – List | ||
| List – Study Note | ||
| Code – List | ||
| All Code : R Data Structure | ||
| 7. | Import and Export in R (36 minutes) | Import CSV Data in R |
| 18-Import in R-CSV Study Note | ||
| CODES – Import CSV Data in R | ||
| Import Text Data in R | ||
| Import Text file Study Note | ||
| CODES – Import Text Data in R | ||
| Import Excel, Database and Web Data in R | ||
| Import Excel, Database and Web Data – Study Note | ||
| CODE – Import Excel, Web Data in R | ||
| Export Data in R – Text | ||
| Export Data in R – Text,CSV,Excel – Text Study Note | ||
| CODE – Export Data in R – Text | ||
| Export Data in R – CSV & Excel | ||
| CODE – CSV & Excel | ||
| All Code: Import and Export in R | ||
| 8. | Data Manipulation in R (01 hour 50 minutes) | Data Manipulation – Apply Function |
| Data Manipulation – select | ||
| Data Manipulation STUDY NOTE | ||
| Dplyr Package | ||
| Dplyr Package Study Note | ||
| Dplyr Package part #2 – mutate(),filter() | ||
| mutate(),filter(),arrange() Function Study Note | ||
| Dplyr Package #3 – summarise() | ||
| summarise() Function and Pipe operator Study Note | ||
| Dplyr Package #4 -groupby() | ||
| Groupby() Part 2 – Get the Flight data from hflight dataset that departed too | ||
| Group By Function Study Note | ||
| Different format of Date | ||
| Data Manipulation – Date with R | ||
| All Code: Data Manipulation | ||
| 9. | Data Visualization (02 hours 34 minutes) | Data Visualization – Scatter Plot |
| Data Visualization – Scatter Plot Study Note | ||
| Data Visualization – mfrow | ||
| Data Visualization – mfrow Study Note | ||
| Data Visualization – pch | ||
| Data Visualization – pch Study Note | ||
| Data Visualization – Color | ||
| Data Visualization – Color Study Note | ||
| Data Visualization – Line Chart | ||
| Data Visualization – Line Chart Study Note | ||
| Data Visualization – Bar Plot | ||
| Data Visualization – Bar Plot STUDY NOTE | ||
| Data Visualization – Pie Chart | ||
| Data Visualization – Pie Chart STUDY NOTE | ||
| Data Visualization – Histogram | ||
| Data Visualization – Histogram STUDY NOTE | ||
| Data Visualization – Density Plot | ||
| Data Visualization – Density Plot STUDY NOTE | ||
| Data Visualization – Box Plot | ||
| Data Visualization – Box Plot STUDY NOTE | ||
| Data Visualization – Mosaic Plot and Heat Map | ||
| Data Visualization – Mosaic Plot and Heat Map SYUDY NOTE | ||
| Data Visualization – 3D Plot | ||
| Data Visualization – 3D Plot STUDY NOTE | ||
| Data Visualization – Word Cloud | ||
| Data Visualization – Word Cloud STUDY NOTE | ||
| Data Visualization – ggplot2 Part 1 | ||
| #PART 1 Data Visualization – ggplot2 | ||
| Data Visualization-ggplot2 Part #2 | ||
| #PART 2 Data Visualization – ggplot2 | ||
| Data Visualization – ggplot2 -Part #3 | ||
| #PART 3 Data Visualization – ggplot2 | ||
| All Code: Data Visualization Code | ||
| Par Function Code | ||
| 10. | Introduction to Statistics (02 hours 28 minutes) | Introduction To Statistic – Part 1 |
| Introduction To Statistic – Part 1 STUDY NOTE | ||
| Introduction To Statistic – Part 2 | ||
| Introduction To Statistic – Part 2 STUDY NOTE | ||
| Introduction To Statistic – Part 3 | ||
| Introduction To Statistic – Part 3 STUDY NOTE | ||
| Introduction To Statistic – Part 4 | ||
| # Part 4 Introduction To Statistic STUDY NOTE | ||
| Introduction To Statistic – Part 5 | ||
| Introduction To Statistic – Part 5 STUDY NOTE | ||
| Introduction To Statistic – Part 6 | ||
| Introduction To Statistic – Part 7 | ||
| Introduction To Statistic – Part 7 STUDY NOTE | ||
| Introduction To Statistic – Part 8 | ||
| Introduction To Statistic – Part 8 STUDY NOTE | ||
| Introduction To Statistic – Part 9 | ||
| Introduction To Statistic – Part 9 STUDY NOTE | ||
| Introduction To Statistic – Part 10 | ||
| Introduction To Statistic – Part 10 STUDY NOTE | ||
| Introduction To Statistic – Part 11 | ||
| Add Codes : Introduction to Statistics | ||
| 11. | Hypothesis Testing #1 (44 minutes) | Hypothesys Testing – Part 1 |
| Hypothesys Testing in Practice – Part 1 STUDY NOTE | ||
| Hypothesys Testing – Part 2 | ||
| Hypothesys Testing – Part 2 STUDY NOTES | ||
| Hypothesys Testing – Part 3 | ||
| Hypothesys Testing – Part 4 | ||
| Hypothesys Testing – Part 4 STUDY NOTES | ||
| 12. | Hypothesis Testing in Practice (02 hours 16 minutes) | Hypothesys Testing in Practice – Part 1 |
| Hypothesys Testing in Practice – Part 1 STUDY NOTE | ||
| Hypothesys Testing in Practice – Part 2 | ||
| Hypothesys Testing in Practice – Part 3 | ||
| 10.Hypothesys Testing in Practice – Part 3 STUDY NOTE | ||
| Hypothesys Testing in Practice – Part 4 | ||
| Hypothesys Testing in Practice – Part 5 | ||
| 10. Hypothesys Testing in Practice – Part 5 STUDY NOTE | ||
| Hypothesys Testing in Practice – Part 6 | ||
| Chi Square -Part 1 | ||
| Chi Square -Part 1 STUDY NOTE | ||
| Chi Square -Part 2 | ||
| Chi Square -Part 2 STUDY NOTE | ||
| ANOVA – Part 1 | ||
| ANOVA 1 STUDY NOTE | ||
| ANOVA – Part 2 | ||
| All Codes : Hypothesis Testing in Practice | ||
| 13. | Bonus Lecture – Data Science Masterclass $9 Only (02 minutes) | Join Our Private Facebook and Telegram Group ($497 Value) |
| What Next? – Become a Master in Data Science – [30 Hours Masterclass Course $10] |
Resources Required
- To understand the Data Science & Machine Learning Course, no prior knowledge is necessary
- The course will make use of R software
- The course will cover R installation and use
- Installation of R software is necessary
Featured Review
Chandra Bhushan (5/5) : Excellent course for learning data analysis, trainer has explained everything with using examples and also explained what to use when. I will certainly recommend this course to other.
Pros
- Sandeep K H (5/5) : once i have seen the video “course roadmap” and “how to start/switch a career into data science”, i got perfect idea about what to do?
- Shree Kanth (5/5) : To be honest, this is one of the best courses I’ve ever watched on Data Science & Machine Learning.
- Rohit Kumar Shaw (5/5) : As advertised, the lessons built on one another and I feel Updegree did an excellent job in building a strong foundation of knowledge.
- Rohit Kumar Shaw (5/5) : presented the course with great energy and enthusiasm (the music was a nice touch).
Cons
- Aditya S. (2/5) : Poor grammar and pronunciation makes it difficult to understand some of what the speaker is saying. Waiting to see what the ultimate information value of this course will be.
- Ritesh T. (2.5/5) : not expected, the title says: Data Science and Machine Learning with R, however, the video ends with only Statistics and how to use R, why hasn’t the tutor mentioned this would be no course on Machine Learning before enrolment!!
- Sopio G. (3/5) : Trainer sounds active and seems to have worked hard on the course. So far I like it. Only thing is accent which sometimes was hard for me to understand, but because he uses informative slides, its fine.
About the Author
The instructor of this course is Up Degree who is a New Skills Everyday. With 3.8 Instructor Rating and 4,750 Reviews on Udemy, he/she offers 29 Courses and has taught 84,677 Students so far.
- UpDegree is a team of knowledgeable IT professionals with experience in a variety of IT fields
- UpDegree work for numerous startups in addition to various MNCs like Microsoft, IBM, Cisco, eBay, Amazon, and Flipkart
- UpDegree give students the hands-on, practical computer training they need to land a job in the IT industry
- Course is of less theory and students can learn by doing and by following examples
Comparison Table
| Parameters | Data Science and Machine Learning Masterclass with R | Learn Python For Data Science W/ Search & Recommender Algos! | Want to be a Big Data Scientist? |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 15 hours | 4.5 hours | 1.5 hours |
| Rating | 4.5 /5 | 4.0 /5 | 4.7 /5 |
| Student Enrollments | 9,431 | 5,378 | 12,539 |
| Instructors | Up Degree | Larry Wai | V2 Maestros, LLC |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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