Hello, and welcome to Ta-Da! the social scientist's guide to Tools for Applied Data Analysis!
As social scientists, we often find ourselves thrust into technical areas in which we have not been trained, and we often don't even know the terms we need to google to get started. The purpose of this website is to act as a guide for researchers in this position by providing an overview of the options available for different tasks, and the considerations users should make before investing in learning a specific tool.
With that in mind, our mission is to provide a place where social scientists can go to:
- Figure out what tools are out there so they know what to google to learn more;
- Highlight concepts and quirks that are likely to be most most importance to social scientists, and which may be overlooked by other resources not tailored to this audience;
- And point users to a few especially helpful tutorials.
Ta-Da! covers a great many topics, but articles on each are organized in a parallel structure:
- Topic Pages: Overviews of tools available for specific problems, like web-scraping, or working with network data.
- Level 1 Pages: Overview of available tools, but for a specific language like Stata, R, or Python.
- Level 2 Pages: More in-depth language-specific resources.
You will see the level of each page displayed in the title as a parenthetical after the title.
OK, enough introduction -- take a look at a few topic pages to see what Ta-Da! is all about!
- Statistical Environments - R, Stata, Python / Pandas, and more (Topic): which should I use?
- Big Data (Topic): What is "Big Data," and what tools should I use if I have it?
- Geo-Spatial Tools (GIS) (Topic): Tools and Concepts
- Programming Languages (Topic): Python and Java and C++, oh my! Which one to learn?
- Getting Text and Table Data from Printed Materials (Topic): Tools for OCRing both text and tables.
- Programs for Algebra (Topic): Programs that will simplify and solve symbolic algebraic expressions
- Exporting Results To LaTeX (Topic): Because transcribing results from statistical programs to LaTeX documents by hand is a recipe for disaster.
- Making Code Faster (Topic): Guide to speeding up R and Python code, written with social scientists in mind.
Level 1 Pages
Want to see some Level 1 pages? Here are a few!
- Python (Level 1): Python tools for social scientists!
Feedback / Want to Contribute?
Things you like? Can't find something you think we should cover? Things that aren't clear? Please let us know at firstname.lastname@example.org !
We've done our best to populate Ta-Da! with an initial set of articles on topics that (a) we think are of common concern to social scientists, (b) we struggled to figure out initially, and (c) on which we feel qualified to speak. But we're always looking for more people who want to contribute to the Ta-Da! project, so if you're interested in joining our team, send us an email!