<iframe width="600" height="400"
src="https://www.youtube.com/embed/kgicuytCkoY">
</iframe>05. Additional resources
Additional resources and building your skills
Try using the estimationstats.com web app to analyze your own grouped data.
Open and have a look at the sample multivariate data. Go through the introductory notebook that demonstrates data analysis.
We recommend the following texts to strengthen your data-analysis and presentation skills. They can be dipped into over the coming months or years, and used as references. Being familiar with some or all of this material will help you write your first-author paper/s and doctoral thesis.
Key resources
Estimation: Our estimationstats.com site has introductory information on estimation and specific types of analyses and effect sizes.
Datavis: Claus Wilke’s free online book is a great introduction to data visualization, and a style guide. It is written in R, which is the best language for statistics.
Coding: There are many online resources to learn coding. Published in 2021, A Data-Centric Introduction to Computing uses a Python-like teaching language (Pyret) to introduce key concepts in computer science.
Additional resources
Some are free, some you will need to buy or borrow from the library.
Estimation: If you want to learn about estimation statistics in greater depth, there is Calin-Jageman and Cumming’s textbook that is well-written, funny, and clear. The authors also run a blog.
Estimation: Christoph Bernard’s account of the pioneering experience of a major journal (eNeuro) recommending estimation as standard: the initial announcement, author feedback, and after one year.
Coding: The paid coding tutorial Learn Python The Hard Way has a good reputation, but there are also many free options (see DCIC above) with great reviews.
Coding: It will help to learn to use your computer’s Unix-style command-line shell. This interface will allow you to use package managers like conda and homebrew, version-control tools like git, and other important tools. There are many books about the shell, with only minor differences between MacOS, Windows, and Linux.
Datavis: A brief guide to oral–visual data presentations (talks).
Datavis: A reader-funded textbook on typography, including for slides. Since so much communication relies on text, typography is an important part of the data interface.
Datavis: For historical perspectives, Edward Tufte’s books are classic texts to develop your design skills, and there is Friendly and Wainer’s History of Data Visualization.
As you progress, you will want to develop your skills in areas like bioinformatics, image processing, and/or machine learning. The iris dataset is widely used for training in multivariate data analysis, with many online tutorials.
The social reasons to learn programming also apply to programming for research.