Tidy Data: Updated Data Processing With tidyr and dplyr in R
Tidy Data: Updated Data Processing With tidyr and dplyr in R, available at $59.99, has an average rating of 4.65, with 42 lectures, based on 234 reviews, and has 1580 subscribers.
You will learn about Read In Data Into The R Environment From Different Sources Carry Out Basic Data Pre-processing & Wrangling In R Studio How To Use Some Of The MOST IMPORTANT R Data Wrangling & Visualisation Packages Such As Dplyr and Ggplot2 Gain PROFICIENCY In Data Preprocessing, Data Wrangling & Data Visualization In R By Putting Your Soon-To-Be-Acquired Knowledge Into IMMEDIATE Application This course is ideal for individuals who are Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment or Students Interested in Learning About the Common Pre-processing Data Tasks or Those Interested in Gaining Tangible Insights From Their Data or Those Interested in Learning to Create Publication Quality Visualisations It is particularly useful for Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment or Students Interested in Learning About the Common Pre-processing Data Tasks or Those Interested in Gaining Tangible Insights From Their Data or Those Interested in Learning to Create Publication Quality Visualisations.
Enroll now: Tidy Data: Updated Data Processing With tidyr and dplyr in R
Summary
Title: Tidy Data: Updated Data Processing With tidyr and dplyr in R
Price: $59.99
Average Rating: 4.65
Number of Lectures: 42
Number of Published Lectures: 42
Number of Curriculum Items: 42
Number of Published Curriculum Objects: 42
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Read In Data Into The R Environment From Different Sources
- Carry Out Basic Data Pre-processing & Wrangling In R Studio
- How To Use Some Of The MOST IMPORTANT R Data Wrangling & Visualisation Packages Such As Dplyr and Ggplot2
- Gain PROFICIENCY In Data Preprocessing, Data Wrangling & Data Visualization In R By Putting Your Soon-To-Be-Acquired Knowledge Into IMMEDIATE Application
Who Should Attend
- Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
- Students Interested in Learning About the Common Pre-processing Data Tasks
- Those Interested in Gaining Tangible Insights From Their Data
- Those Interested in Learning to Create Publication Quality Visualisations
Target Audiences
- Students Interested In Getting Started With Data Science Applications In The R & R Studio Environment
- Students Interested in Learning About the Common Pre-processing Data Tasks
- Those Interested in Gaining Tangible Insights From Their Data
- Those Interested in Learning to Create Publication Quality Visualisations
THIS IS YOUR ROADMAP TO LEARNING & BECOMING HIGHLY PROFICIENT IN DATA PREPROCESSING, DATA WRANGLING, & DATA VISUALIZATION USING TWO OF THE MOST IN-DEMAND R DATA SCIENCE PACKAGES!
Hello, My name is Minerva Singh. I am an Oxford University MPhil graduate in Geography & Environment & I finished a PhD at Cambridge University in Tropical Ecology & Conservation.
I have +5 of experience in analysing real-life data from different sources using statistical modelling and producing publications for international peer-reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!
I created this course to take you by hand and teach you all the concepts, and tackle the most fundamental building block on practical data science – data wrangling and visualisation.
THIS COURSE WILL TEACH YOU ALL YOU NEED AND PUT YOUR KNOWLEDGE TO PRACTICE NOW!
This course is your sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualisation skills that I acquired from the rigorous training I received at 2 of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in the renowned international journal like PLOS One.
HERE IS WHAT THIS COURSE WILL DO FOR YOU:
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It will take you (even if you have no prior statistical modelling/analysis background) from a basic level of performing some of the most common data wrangling tasks in R- with two of the most happening R data science packages tidyverse and dplyr.
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It will equip you to use some of the most important R data wrangling and visualisation packages such as dplyr and ggplot2.
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It will Introduce some of the most important data visualisation concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.
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You will also be able to decide which wrangling and visualisation techniques are best suited to answer your research questions and applicable to your data and interpret the results..
The course will mostly focus on helping you implement different techniques on real-life data such as Olympic medal winners
After each video, you will learn a new concept or technique which you may apply to your own projects immediately! Reinforce your knowledge through practical quizzes and assignments.
ON TOP OF THE COURSE, I’M ALSO OFFERING YOU:
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Practice Activities To Reinforce Your Learning
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My Continuous Support To Make Sure You Gain Complete Understanding & Proficiency
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Access To Future Course Updates Free Of Charge
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I’ll Even Go The Extra Mile & Cover Any Topics That Are Related To The Subject That You Need Help With (This is something you can’t get anywhere else).
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& Access To A Community Of 25,000 Data Scientists (& growing) All Learning Together & Helping Each Other!
Now, go ahead & enrol in the course. I’m certain you’ll love it, but in case you don’t, you can always request a refund within 30 days. No hard feelings whatsoever. I look forward to seeing you inside!
Course Curriculum
Chapter 1: Welcome To The Course
Lecture 1: Introduction to the Course
Lecture 2: Data and Scripts
Lecture 3: Install R and RStudio
Lecture 4: Common data types
Lecture 5: Quick Pointers
Chapter 2: Read in Data From Different Sources
Lecture 1: Read in CSV and Excel Data
Lecture 2: Read in Data from Online HTML Tables-Part 1
Lecture 3: Read in Data from Online HTML Tables-Part 2
Lecture 4: Read in Data from Databases
Lecture 5: Read in Data from JSON
Chapter 3: Data Processing With dplyr
Lecture 1: Introduction to Pipe Operators
Lecture 2: Get acquainted with our data using "dplyr"
Lecture 3: More selections with dplyr
Lecture 4: Row filtering
Lecture 5: More row filtering
Lecture 6: Select desired rows and columns
Lecture 7: Add new variables/columns
Lecture 8: Making sense of data by grouping different categories
Lecture 9: Grouping Data-Part 2
Lecture 10: Introduction to dplyr for Data Summarizing-Part 1
Lecture 11: Introduction to dplyr for Data Summarizing-Part 2
Chapter 4: Data Processing the Tidy Way: The "tidyr" Package
Lecture 1: Start with Tidyverse
Lecture 2: Column Renaming
Lecture 3: Tidy Data: Long and Wide
Lecture 4: Joining Tables
Lecture 5: Nesting
Lecture 6: Brief Reminder: Hypothesis Testing
Lecture 7: Implement t-test On Different Categories
Chapter 5: Dealing With Missing Values
Lecture 1: Removing NAs- the ordinary way
Lecture 2: Remove NAs- using "dplyr"
Lecture 3: Data imputation with dplyr
Lecture 4: More data imputation
Chapter 6: Data Visualisation and Explorations
Lecture 1: What is Data Visualisation?
Lecture 2: Some Principles of Data Visualisation
Lecture 3: Data Visualisation With dplyr and ggplot2
Lecture 4: Mining and Visualising Information About the Olympic Games
Lecture 5: Of Winter and Summer Olympic Games
Lecture 6: Of Men and Women
Lecture 7: Theory of Ordinary Least Square (OLS) Regression
Lecture 8: Implement OLS on Different Categories
Chapter 7: Miscellaneous Lectures
Lecture 1: Github Intro
Lecture 2: Group By Time
Instructors
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Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)
Rating Distribution
- 1 stars: 7 votes
- 2 stars: 18 votes
- 3 stars: 21 votes
- 4 stars: 44 votes
- 5 stars: 144 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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