This is a list of resources that I am always looking for information, I consider them as essential material for my professional work as a researcher and a data analyst. If you can’t have access to any of these, please, reach me by email so that I can try to help you.
Statistics
Statistical Rethinking (2019)
This book is written by Richard McElreath, it talks about a different way of looking at statistics and inferences. His website is also a great source for talks and posts.
The effect (2022)
The effect is a gentle introduction to causal inference, written by Nick Huntington-Klein. The first part is an introduction to all the intuition that supports causal inference. The second part is an explanation of different statistical models for causal inference. The language used is accessible and there is not too much math, so the reading is easier than other books.
Statistical Inference as Severe Testing (2018)
Modern Statistics with R (2021)
This is a book that covers many aspects of applying statistics in R. There are some chapters with basic material (if you are experienced with R, you are safe to skip them). After chapter 7, there is a wide range of topics on statistics, one of my favorites are the chapters talking about ethics issues with statistical inferences. This is a very applied book, with a lot of code examples in R.
Statistical Inference via Data Science (2022)
Another hands-on book, with a lot a code in R to apply sampling techniques for statistical inferences. What I really like about this book is that it uses a very didactic approach, and it also applies modern packages (infer, from tidymodels) to perform the statistical inferences.
Programming
R for Data Science
This is the book that finally made me start thriving in programming and data analyses. It was written by Hadley Wickham (a big reference in the R community). The book is an introduction to the use of a collection of packages, called the tidyverse
Happy Git and GitHub for the useR
This is an essential book to learn version control with R! It is written by another reference to the R community, Jennifer Bryan. It teaches you how to start integrating your R projects with git and github, such an important resource if you want to produce high quality projects.
Advanced R
Another book by Hadley Wickham, but differently from R for Data Science, it teaches specific skills with R that helps specially with the package development. My favorite section is the Functional Programming.
Mastering Spark with R
Spark helped me a lot when I needed to analyse a data set that could not fit in my computer memory. Even though I just used I tiny part of the whole capability of Spark, it was a great solution for my problem. I expect working more with Spark, and start using connections and applying machine learning algorithms with Spark.
Data Communication
Fundamentals of Data Visualization (2020)
ggplot2: Elegant Graphics for Data Analysis (2016)
Chartability
The author of chartability is Frank Elavsky, which is a reference for me concerning accessibility. He developed the chartability which is a framework to evaluate and develop more accessible data visualizations. There is also a document explaining the creation and usage of chartability: How accessible is my visualization? Evaluating visualization accessibility with Chartability (2022).
Hands-On Data Visualization (2022)
Telling Stories with Data (2023)
Report Writing for Data Science in R (2019)
Modelling
Modelling Mindsets (2022)
This is an amazing overview of different modeling approaches. Written by Christoph Molnar, in a very fluid and pedagogical manner. I strongly recommend reading this book if you are into modeling, and already have familiarity with statistics.
Interpretable Machine Learning (2022)
This is another exceptional book, also written by Christoph Molnar. This book aims to present ways to interpret models. Some chapters deal with models that are naturally easier to interpret. Other chapters deal with methodologies that can be applied to any model, even those that are more difficult to interpret. A must read for anyone working with modeling.
The Art of Data Science (2018)
Modern Data Science with R (2021)
Tidy Modelling with R (2022)
Hands-On Machine Learning with R (2020)
Explanatory Model Analysis (2020)
Feature Engineering and Selection: A Practical Approach for Predictive Models (2019)
Interpretable Machine Learning (2022)
MLU-Explain
Spatial analysis
Geocomputation with R (2023)
Satellite Image Time Series Analysis on Earth Observation Data Cubes (2022)
Spatial Data Science (2022)
This is a book written by Edzer Pebesma. He is a reference for me, concerning spatial data manipulation. I have learned so much from him, he has made a great work developing R packages for spatial analytics, and really helped the development of this area in the R community. This is an amazing book with essential information for anyone interested on spatial data analysis, and even though it is focused on the R language, it can provide precious information to users of any type!