Promoted by John Tukey, exploratory data analysis focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with more formal statistical methods. Link to Course Back to table.
The R data.table package is rapidly making its name as the number one choice for handling large datasets in R. This online data.table tutorial will bring you from data.table novice to expert in no time. Once you are introduced to the general form of a data.table query, you will learn the techniques to subset your data.table, how to update by reference and how you can use data.table’s set-family in your workflow. The course finishes with more complex concepts such as indexing, keys and fast ordered joins.
Upon completion of the course, you will be able to use data.table in R for a more efficient manipulation and analysis process.
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Welcome to Data Analysis in Python!Python is an increasingly popular tool for data analysis. Components. Core Skill Sequence: A collection of four numbered tutorials that cover core skills everyone needs to work in Python in social science.
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I recommend you visit these in sequence – a site for setting up Python on your computer using the Anaconda distribution, an intro to Python for those not familiar with the language, an introduction to the pandas library for working with tabular data (analogous to data.frames in R, or everything you ever did in Stata), and a guide to installing libraries to expand Python. Specific Resources for Different Research Topics: “topic” pages, which you should feel free to jump through as appropriate for your purposes:, for graphing,. The topic pages also include two topics that are a little unusual, but I think potentially quite useful: guide to, and resources on evidence-based research on for anyone teaching this material. Resources for Other Software Tools: Resources on tools and programs you may come across while using Python with descriptions of the tool, guidance on what you need to know most, and links to other tutorials. These include pages on the, and.Ready to get started? Head on over to!Question or comments?
Feedback of all sorts is greatly appreciated, and if you have any experience with github, Contents.
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