Cleaning and Preparing Data

Welcome to the last stage of this course. 0:00 Nice work. 0:02 We've covered a lot of material. 0:03 So let's go over it one more time before you move on. 0:05 We've learned the basics of data cleaning and 0:08 ways to make sure that our data is correct and accurate. 0:11 If you feel like cleaning your data set takes a long time, 0:14 remember that it's normal. 0:17 Properly cleaning your data takes a lot of work, but 0:19 it's vital to the quality of your analysis. 0:22 We covered eight types of data errors or problems. 0:24 Formatting errors, incorrect data type, nonsensical data entries, 0:27 duplicate entries, missing data, saturated data, systematic and 0:32 individual errors, and confidential information. 0:37 You should have an idea of how to start cleaning up these problems when you see 0:41 them in your data. 0:44 We also talked about how important it is to understand your dataset. 0:45 A lot of errors can go unnoticed if you're not sure what your data should look like, 0:49 or what's normal. 0:53 It's also important when deciding which data should be kept in your dataset, and 0:55 what should be removed. 0:59 And since we’re using Python, 1:01 we talked about ways we can automate parts of our data analysis to help it go faster. 1:03 That’s all for this course, but there’s always something new to learn. 1:07 In the teacher’s notes, you can download marked down commented versions 1:11 of the code we used throughout this course. 1:14 You can use these to remind yourself what we’ve covered in the lessons. 1:17 There are also links to resources for learning more about data cleaning and 1:21 data analysis. 1:24 Now there is one more quiz and then you are done with this course. 1:26 Congratulations and good luck. 1:29