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Building bioinformatics solutions : with Perl, R, and MySQL by 
Environmental and Ecological Statistics with R by 
Computing skills for biologists : a toolbox by 
A Handbook of Statistical Analyses Using R, Third Edition by 
Beginner's guide to spatial, temporal, and spatial-temporal ecological data analysis with R-INLA by 
Using R at the Bench: Step-By-Step Data Analytics for Biologists by 
A Practical Guide to Ecological Modelling by 
Using R for Modelling and Quantitative Methods in Fisheries by __________eBooks__________
EBOOK: Statistical modeling with R : a dual frequentist and Bayesian approach for life scientists by 
EBOOK: XML and web technologies for data sciences with R by 
EBOOK: Probability, statistics, and data : a fresh approach using R. by 
EBOOK: R for data analysis in easy steps : R programming essentials by 
EBOOK: Mathematical Statistics with Resampling and R by 
EBOOK: A primer in biological data analysis and visualization using R by
R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of correctly entering and analyzing data and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normally distributed data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter outlining algorithms and the art of programming using R. This second edition has been revised to be current with the versions of R software released since the book's original publication. It features updated terminology, sources, and examples throughout.
EBOOK: Statistical analysis and data display : an intermediate course with examples in R by __________eBooks__________
EBOOK: Bayesian networks : advances and novel applications by EBOOK: Statistical modeling with R : a dual frequentist and Bayesian approach for life scientists by 
EBOOK: Bayesian modeling and computation in Python by 
Introduction to the Physical and Biological Oceanography of Shelf Seas by 
Time Series Data Analysis in Oceanography by 
Sliding Mode Control Using MATLAB by 
Numerical linear algebra : a concise introduction with MATLAB and Julia by 
Computational mathematics : models, methods, and analysis with MATLAB® and MPI by 
Nonlinear Control Systems Using MATLAB by 
Neural data science : a primer with MATLAB® and Python by __________eBooks__________
EBOOK: Neural data science : a primer with MATLAB® and Python by EBOOK: Nonlinear Control Systems Using MATLAB by Neural data science : a primer with MATLAB® and Python by 
Advanced Python Scripting for ArcGIS Pro by Computing skills for biologists : a toolbox by 
An Introduction to Python Programming for Scientists and Engineers by __________eBooks__________
EBOOK: Bayesian modeling and computation in Python by 
EBOOK: Python scripting for ArcGIS Pro by 
EBOOK: Introduction to Python in earth science data analysis : from descriptive statistics to machine learning by EBOOK: Neural data science : a primer with MATLAB® and Python by 
Discovering SQL : a hands-on guide for beginners by Building bioinformatics solutions : with Perl, R, and MySQL by __________eBooks__________
EBOOK: Discovering SQL : a hands-on guide for beginners by 
EBOOK: SQL for data scientists : a beginner's guide for building datasets for analysis by 
EBOOK: SQL in easy steps : ideal for those who want to master SQL essentials by 
EBOOK: SQL for data science : data cleaning, wrangling and analytics with relational databases by 
C++ Primer by 
The C++ Programming Language by ❤ See an Item You Love? ❤
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Advanced Python Scripting for ArcGIS Pro by __________eBooks__________
EBOOK: Python scripting for ArcGIS Pro by 
Core Java Volume I--Fundamentals by 
Scientific Data Analysis Using Jython Scripting and Java by __________eBooks__________
EBOOK: Scientific Data Analysis Using Jython Scripting and Java by 
EBOOK: Mastering Java : A Beginner's Guide. by __________eBooks__________
EBOOK: SAS essentials : mastering SAS for data analytics by 
EBOOK: Text as data : computational methods of understanding written expression using SAS by 
EBOOK: Microsimulation population projections with SAS : a reference guide by 
EBOOK: SAEBOOK: S for R users : a book for budding data scientists by 
EBOOK: Multilevel modeling : applications in STATA®, IBM® SPSS®, SAS®, R, & HLM™ by 
EBOOK: SAS and R : data management, statistical analysis, and graphics by 
Integrated Population Biology and Modeling, Part A by 
Integrated Population Biology and Modeling Part B by 
Modelling and Monitoring of Coastal Marine Processes by __________eBooks__________
EBOOK: Data mining : concepts and applications by 
EBOOK: Occupancy estimation and modeling : inferring patterns and dynamics of species occurrence by 
EBOOK: Introduction to quantitative ecology : mathematical and statistical modelling for beginners by
Environmental science (ecology, conservation, and resource management) is an increasingly quantitative field. A well-trained ecologist now needs to evaluate evidence generated from complex quantitative methods, and to apply these methods in their own research. Yet the existing books and academic coursework are not adequately serving most of the potential audience - instead they cater to the specialists who wish to focus on either mathematical or statistical aspects, and overwhelmingly appeal to those who already have confidence in their quantitative skills. At the same time, many texts lack an explicit emphasis on the epistemology of quantitative techniques. That is, how do we gain understanding about the real world from models that are so vastly simplified? This accessible textbook introduces quantitative ecology in a manner that aims to confront these limitations and thereby appeal to a far wider audience. It presents material in an informal, approachable, and encouraging manner that welcomes readers with any degree of confidence and prior training. It covers foundational topics in both mathematical and statistical ecology before describing how to implement these concepts to choose, use, and analyse models, providing guidance and worked examples in both spreadsheet format and R. The emphasis throughout is on the skillful interpretation of models to answer questions about the natural world. Introduction to Quantitative Ecology is suitable for advanced undergraduate students and incoming graduate students, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world ecology, conservation, and resource management scenarios.
EBOOK: Modelling and Monitoring of Coastal Marine Processes by 
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