"Although many of the techniques are relevant to molecular bioinformatics, the motivation for the text is much broader, focusing on topics and techniques that are applicable to a range of scientific endeavors."
"This methodology will not find every bug in every program, but it is highly effective for the sort of short programs that beginner programmers are assigned as homework. These techniques then scale up to finding bugs in non-trivial programs."
"The United States Indigenous Data Sovereignty Network (USIDSN) helps ensure that data for and about Indigenous nations and peoples in the US (American Indians, Alaska Natives, and Native Hawaiians) are utilized to advance Indigenous aspirations for collective and individual wellbeing. USIDSN’s primary function is to provide research information and policy advocacy to safeguard the rights and promote the interests of Indigenous nations and peoples in relation to data."
"an open source textbook aimed at introducing undergraduate students to data science. [...] In this book, we define data science as the study and development of reproducible, auditable processes to obtain value (i.e., insight) from data." Uses R's tidyverse packages and Jupyter notebooks.
Call Number: QA76.73.P98 V365 2016 (Youngblood Energy Library)
Publication Date: 2016
"For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. "
"Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. "
Based on their extensive experience with teaching R & statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R & statistics at the same time, statistical methods are kept to a minimum.