Below are several books on such issues as writing, research, and teaching by Darrin Thomas. There is also an Amazon Author page.
Introductory Statistics with Python teaches basic statistics within the context of using Python. Specifically, the reader will learn to do statistical analysis using Jupyter Notebook. This book prepares the reader with a foundation for growing in Python for conducting complex analysis.
Easily provides even more insights into the use of machine learning algorithms using Python. This book provides explanation of supervised and unsupervised algorithms along with more complex approaches such as combining algorithms for classification performance.
Simply is a book that focuses on the use of Python for machine learning purposes. The emphasis in this book is on supervised learning involving both regression and classification models. This is a great starter text for people new to Python for data science.
PlaineR provides a basic overview of common supervised and unsupervised algorithms in the R language. This book is great for taking a look in particular at the finer points of unsupervised machine learning algorithms.
Looking for a book that covers the basics of statistics and also serves as a guide for learning simple concepts in R? If so BeginneR Introductory Statistics Using R is what you are looking for. This text covers everything that is normally learned in a 1st-semester statistics course with applications in R.
For those who are familiar with R and machine learning, EasieR: Practical Applications of Machine Learning Algorithms in R provides insights into the use of several machine learning algorithms. The text includes discussion on the use of decision trees, classification rules, support vector machines, artificial neural networks as well as ways to evaluate and improve a model.
For the machine learning lover. SimpleR: Using Machine Learning Algorithms in R provides practical examples and applications of machine learning algorithms using the R programming language. Such concepts as numeric and classification models as well as feature selection and how to assess the quality of a model are discussed. Topics include best subset, ridge, lasso, and elastic net regression, discriminant analysis, and logistic regression.
For those new to writing at an academic level Introduction to Secondary Research Writing is for you. This book does not address the details of grammar but rather provides insight into shaping the ideas and flow of a paper by sharing practical advice. This book will guide you through the experience of developing a topic, problem, purpose, objective, significance, of a paper as well as how to approach writing the body and conclusion.
Research is not necessarily difficult. What is needed is a basic explanation of common terms and how they apply to completing a project. In the book Research as a Language, Darrin Thomas provides practical insights into how research can be done primarily in an educational setting.
In the Thai Classroom provides insights into teaching young adults in the context of Thailand. Practical tips and dealing specifically with Thai students are given as well as general approaches to teaching. This book is especially helpful for those new to teaching and or do not have formal training in teaching.