This course provides an introduction of some important concepts and tools on a very important aspect of data science: cleaning and organizing data before any analysis. A must for any data scientist.
This course is really a challenging and compulsory for any one who wants to be a data scientist or working in any sort of data. It teaches you how to make very palatable data-set fro ma messy data.
von Antonios D•
This course it's a great job! There is too much information in here and a great amount of knowledge. I would like to say that in my point of view the current lesson should be updated in more different data sets examples that gives the students the opportunity of learning different kind of ways to manypulate some data. There are some standard ways so it would be great if you expand this.
von Chris B•
It is sometimes daunting and difficult, but now I do understand so much more about downloading files from remote sites and getting them ready for analysis. What I should have done is look to the final project so as get a better understanding of what the project entailed. I also should have done more work replicating the code used in the lessons so as to appreciate how it worked.
von Debayan D•
The Course Project was daunting at first, but I reviewed my notes over and over again, tried reading from the site where the raw data was made available and constructed images of how the TIDY data should look like. This is a very important course in this specialization. The course has given me an abstract sense of what to expect and what to do while cleaning data.
von Li G•
Very helpful and pragmatic.
This course gives a general idea on how to get and clean data in r, and specifically taught me how to use "dplyr" and "tidyr".
The assignment is very helpful, too. It forced me to use the knowledge I learned in this course, might be a little bit of hard for a beginner though. Nevertheless, you can still achieve a 100% score!
von Tai C M•
I am very happy to go through this subject not because of the certification but I learned the steps to import and clean the data. Although this subject is no rocket science, a lot of the data available on the web will require the knowledge that I learned in this subject to enhance the integrity of the data that anyone can download from the web.
von Anthony S•
Learned a lot! I have now dedicated more time to becoming a data analyst, and eventually a data scientist. The materials used in the videos were helpful and current (for me at least, 30 years young). I have started doing more learning on the kaggle platform as well as doing some hands-on Hadoop related training. Thanks to the professors!
von Carlos M•
This course is fantastic! Through it was possible concretely to apply the concepts of BigData through the tool proposed for the course. Due to various difficulties I had to leave. But I'm coming back with all my might. Congratulations to all teachers who make no effort to pass on knowledge in a substantial and substantial way.
von Rodney A J•
This is a terrific course on obtaining data from various sources and then cleaning the raw data obtained to form useful tidy data sets. The course material learned is reinforced using a very interesting peer-reviewed project based on accelerometer and gyroscopic data from collected from typical human activity.
von Murat Z•
Great course for data mining and cleaning. If you planning to take Reproducible Research course, I'd recommend to at least audit that course's second week for markdown and knitr skills prior to taking Getting and Cleaning Data course, coz you're going to face need for those skills during the course project.
von Sachi B•
Good intro to several commands needed for cleaning and preparing data. Final assignment was challenging enough that made me dig deeper into commands. Since there are several ways of accomplishing the same task in R, grading the other students helped see what others have done - some of them were slick!
von Aki T•
This course was excellent and fundamental in order to even start a data analysis. It sets the foundation for how to read and treat the data, which is as the instructor mentioned, often overlooked. Thank you very much for taking the time to break the cleaning process into each comprehensive pieces.
von Nino P•
A bit tough course with topics of getting the data since I don't know much about file types, but cleaning part is a must do for every data scientist. dplyr and tidyverse is the base of R and nowadays I only use dplyr for my data wrangling. Highly recommendable course and specialization.
von Sudheergouda P•
The course project was really helpfull in understanding how the data is presented to datascientists. Now to get the jist of the data we have to go through assembling, cleaning and cutting the data.. It was a challenged to understand the data.. assembling the data was a lot of fun in R..
von Fernando V•
A great course. I mean, It has not been easy, I have spent a lot of time in front of the PC practising and doing exercises, but this time and the tools that I have learned make me much more agile and confortable with R, and I have seen the big possibilities that this language has.
von Christopher L•
great course, I am fairly familiar with R in my line of work but this was a great opportunity to practice web-scraping. I might even switch from a dplyr-centric wrangling workflow to one centered on data.table in my personal and professional work. more compact and faster!
von Carlos M•
Difficult but valuable. You will be watching the videos repeatedly and become a regular at StockOverflow but it was completely worth it. Getting, cleaning, and processing data is pretty much 80%+ of the job, this course's information is vital to any future data worker.
von Gilvan S•
Excellent course. It gets through the "dirty job" of obtaining data from diverse sources (including API, web, and others), cleaning it, and transforming it into a "tidy" dataset. Highly recommended, along with the R programming course (which you should take first).
von Scott C•
Good overview of what it means to get and clean your own data. Really enjoyed the final project as it challenged you to, with minimal guidance, think through what a tidy dataset really means, and figure out how to make that happen with the dataset you are provided.
von Tim S•
For someone with no programming background and limited experience working with data, this was a challenging, sometimes frustrating, course. But perseverance through the struggle can end in a deep sense of satisfaction. Happily, this is how it was - quite rewarding.
Wonderful course. gets you through the basics and beyond in getting and cleaning data from diverse sources. Very well thought and explained. There is a lot to be learnt from this course, and it requires devoting a good amount of time to let the material sink in.
von Diego A S R•
Good course, but needs an update. Week 2 was really difficult compared to what was explained in the lectures and regex expressions should be explained using R, it was a little hard to learn to use them directly in R. I feel that I learned a lot in this course.
von Renzzo S S•
Excellent course! i learned a lot with the packages mentioned dplyr, tidyr, readr, lubridate. the swirl package is perfect to learn by doing and the assignment is very challenging and it is good because it incentivates you to research deeply and learn more.
von Randal N•
Very enlightening course. It is the first course where I felt like I was actually doing something data sciency. Would recommend even as a stand alone course because I have now come to appreciate the importance of tidy data in performing successful analyses.
von Keat C C•
Really can learn practical skills! I like that each sub course of data science specialisation just focus on a certain areas and takes only 4 weeks, this way I won't be overburden between work and learning, and also easier for me to absorb the new skills.
von Waleed A•
Another brilliant course from Johns Hopkins University in the data science specialisation. Data preparation is a step where an analyst may spend considerable time before beginning any analysis task. I found this course useful and practical. It provided