Cleaning Data In R with Tidyverse and Data.table (Udemy.com)

Getting great surface level information on all topics but was hoping for more of a deep dive - particularly on building r code and what different pieces of the functions/script mean. added bonus would be some view on how they may be tweaked for other purposes/questions.

It's the best course on data cleaning with R. Please create some courses on statistics also. It will help learners like me a lot. Thanks a lot!

Have taken half this course. And, have a strong feeling the rest is as great as the first-half. It clearly articulates the cleaning of data. I recommend this to anyone serious about analyzing data (involved in ETL.) I hope the author(s) takes it to the next level or does the equivalent in python (something like this course is badly needed for python.)

This course covers a critical aspect common to any data science assignment - Processing raw data for analysis. The topics included in the course are all relevant and a majority is sufficiently detailed to enable a new user to begin applying the functions independently. I would however like to recommend Martin to consider enhancements for the following topics: 1. Missing value imputation using mice – The explanation on the pooling concept is currently too brief 2. Outlier treatment – Only detection is covered in the learning section, and treatment is just about touched upon in the final assignment 3. group_by/ summarise()- much used functions under dlpyr and should be covered in detail with examples.

Very helpful course on applications of tidyverse. The slides were very good that put the applications into perspective. In particular, the presentation of the mice package is very interesting, however, I felt that it was not clear how to correctly apply this powerful package (will do that by myself; thank for the hint though). The summaries at the end of each section are helpful - will use the code snippets in my personal notebook.

I find it a bit difficult that R script is already written in advance. I would prefer having it written during the video or at least having more detailed explanation of how to it was done. In this course more focus is on the content (which is quite good) rather than technical staff that I was expecting. Otherwise, it is useful course.

This is the information/skill that I need and it's been ok so far. I like the explanations of what each function does but I would benefit more if there were comments within the code explaining what each command meant or what it does.

I greatly enjoyed the course. The instructor has an accent (German, Austrian, Dutch?) but his English is great! I would have liked to have been able to actually see rather than just hear him, at least at times. The most important thing I learned about is the data.table package and the data.table class. I already new about data.frames and tibbles, but was unaware of the powerful tools provided by data.table. My only (small) complaint is that the directions for the data-cleaning project that ends the course states that students should use the tidyverse for most of the operations, but the instructor himself did not do that in the solutions he provided. Although he used the unite() function in tidyverse, all of the queries were done in either base R or data.table. Also, he never once used the pipe operator in his code except for when he introduced it in the tidyverse module.

The lecturer is very knowledgeable. The contents are very helpful and interesting too. I wish I have taken this course earlier. Strongly recommend everyone R beginner to take this course. It will save you a lot of time to do online search when you have problems in the future.

I love how mice and outliers packages can be used to impute missing values and outliers. I have also learnt how a boxplot can be so useful to quickly see irregularities in data. Other than that, most of the slicing & dicing of data, calculations, rename statements, creating new columns, joining tables etc can also be done using sqldf package. Overall, it was a good class where I learnt how to cleanse data using some of R's popular data manipulation packages.