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Tidy Data: Updated Data Processing With tidyr and dplyr in R (Udemy.com)

Learn Data Preprocessing, Data Wrangling and Data Visualisation With the Two Most Happening R Data Science Packages

Created by: Minerva Singh

Last updated November 2025

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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

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

  • Rating4.4 out of 5higher than 28% of them
  • Reviews257#308 of 462 by number of reviews
  • Length4.6 hoursshorter than 69% of them
  • Last updatedNovember 2025more recently than 53% of them

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Course Description

     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:

  • 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.

  • It will equip you to use some of the most important R data wrangling and visualisation packages such as dplyr and ggplot2.

  • 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.

  • 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:

  • Practice Activities To Reinforce Your Learning

  • My Continuous Support To Make Sure You Gain Complete Understanding & Proficiency

  • Access To Future Course Updates Free Of Charge

  • 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).

  • & 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!



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Instructor Details

Minerva Singh

I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.

I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly

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Reviews

4.4

257 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Sangeeta Rani on 11/28/2021

The course containing utility of data processing tool is ideally suited for my work. The instructor is simply brilliant in explaining finer points of the subject.

By Raveena Sarkar on 11/26/2021

The course contains valuable information and utility about data processing tools in R. The course has vast potential for practical applications. The instructor has total command over the subject and her teaching techniques are appealing and interesting.

By Rishilal Yadav on 8/11/2021

The course discusses in details the important tools for data processing, data wrangling and data visualization. The concept has immense possibilities for practical applications. The instructor is well-versed with the finer nuances of the concepts and her delivery is impressive.

By Raj Kumar Majumdar on 8/10/2021

The course contains useful and important information. I will be able to apply the concepts to my work which will definitely enhance the quality of my work by few notches. The instructor is thorough about the subject and her delivery is effective.

By Rajesh Kumar on 7/20/2021

The instructor has complete knowledge of the subject and has the expertise and ability to explain the concept in a simple manner. This is extremely valuable course which has unlimited avenues of usage.

By Tabu Zerin on 8/3/2020

Was my first and best experience of learning online. I think it's the best way of learning python because you get to understand the fundamentals from really experienced instructors.

By Jahid Jakaria on 8/2/2020

There are a number of online courses on tidy data available , but I found this to be one of the best. Lectures were based on real life problems. Course content delivery by the Instructor was very good.

By Joseph Barber on 5/11/2020

Overall, course did a good job of explain dyplr and tidyverse. Some of the videos in Section 6 were blurry, so I was unable to practice the code in those videos. I tried to adjust the aspect ratio, but it did not help with video clarity.

By Sa Gri on 12/27/2019

Helpful because I know a little bit about R already and needed practice with data sets. However, the lectures could be more clear. Right now, it's a little hard to follow the logic sometimes. Perhaps an outline or summary would help frame each lesson. There is a lot of code that is just given (typed or on the screen during lecture) but not really explained or taught. But I have learned some new skills, so I give it 3 stars because of that.

By Charles Knell on 9/17/2019

The instructor was inconsistent in her use of assignment operators. The "preferred" R assignment operator (

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Overall Score : 88 / 100