Syllabus for the course: CIS 356 - Fundamentals of Cybersecurity and Intelligence Gathering. Delivered at Lehman College in Spring 2020 by Fahad Chowdhury as part of the Tech-in-Residence Corps program.

Syllabus for the course: CSC 511 - Special Topics in Advanced Web Development. Delivered at the College of Staten Island in Fall 2019 by Shane Afsar as part of the Tech-in-Residence Corps program.

This is the story of a web-based information system rebuild. The system in question is www.teachengineering.org, a digital library of K-12 engineering curriculum that was built from the ground up with established technology and which for 13 years enjoyed lasting support from its growing user community and its sponsors. These 13 years, however, cover the period during which smart phones and tablets became commonplace, during which the Internet of Things started replacing the Semantic Web, during which NoSQL databases made their way out of the research labs and into everyday development shops, during which we collectively started moving IT functions and services into ‘the cloud,’ and during which computing performance doubled a few times, yet again. During this same period, TeachEngineering’s user base grew from a few hundred to more than 3 million users annually, its collection size quadrupled, it went through several user interface renewals, and significant functionality was added while having an exemplary service record, and it enjoyed continued financial support from its sponsors.

In this monograph we provide a side-by-side of this rebuild. We lay out the choices made in the old architecture —we refer to it as TE 1.0— and compare and contrast them with the choices made for TE 2.0. We explain why both the 1.0 and 2.0 choices were made and discuss the advantages and disadvantages associated with them.

This book offers an open guide to online discussions, media arts production, blended learning, and active learning from L&S Learning Support Services @ UW-Madison. It was first published in 2015.

Technical Project Management in Living and Geometric Order demonstrates that even the best-laid project plans can be undone by new technologies, financial upheavals, or resource scarcity, to name just a few disruptors. It encourages project managers to focus on learning throughout a project, with the understanding that what they learn could necessitate major changes in midstream. This adaptive, flexible, living-order approach is inspired by Lean in construction projects and Agile in software development. Technical Project Management in Living and Geometric Order explains how today’s projects unfold in dynamic environments in response to unexpected events. With its practical tips, detailed graphics, links to additional resources, and interviews with engineering professionals, it’s an accessible introduction to the living order for aspiring project managers.

El nivel académico que pretende abordar el libro es en las diferentes licenciaturas e ingenierías que consideren tópicos en el área de interacción humano computadora, así como los posgrados relacionados con la temática. El libro describe diferentes tipos de interacción que pueden ofrecer las aplicaciones interactivas donde a través de la interfaz gráfica es posible guiar y hacer fácil la tarea del usuario. Así pues, el lector puede encontrar diferentes trabajos que especifican desde los requerimientos del usuario hasta el diseño, programación y evaluación de distintas aplicaciones interactivas.

Tennessee State University Open Education Resource - Affordable Learning Solutions Webpage Portal for post-secondary content across higher education academic disciplines in partnership with www.MERLOT.org.

The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods.

This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science. This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system.

The examples and supporting code for this book are in Python. You should know core Python and you should be familiar with object-oriented features, at least using objects if not defining your own. If you are not already familiar with Python, you might want to start with my other book, Think Python, which is an introduction to Python for people who have never programmed, or Mark Lutz’s Learning Python, which might be better for people with programming experience.

Data structures and algorithms are among the most important inventions of the last 50 years, and they are fundamental tools software engineers need to know. But in my opinion, most of the books on these topics are too theoretical, too big, and too bottom-up:

*Too theoretical: Mathematical analysis of algorithms is based on simplifying assumptions that limit its usefulness in practice. Many presentations of this topic gloss over the simplifications and focus on the math. In this book I present the most practical subset of this material and eliminate the rest.

*Too big: Most books on these topics are at least 500 pages, and some are more than 1000. By focusing on the topics I think are most useful for software engineers, I kept this book under 250 pages.

*Too bottom-up: Many data structures books focus on how data structures work (the implementations), with less about how to use them (the interfaces). In this book, I go “top down”, starting with the interfaces. Readers learn to use the structures in the Java Collections Framework before getting into the details of how they work.

Finally, many present this material out of context and without motivation: it’s just one damn data structure after another!

I try to alleviate the boredom by organizing the topics around an application—web search—that uses data structures extensively, and is an interesting and important topic in its own right.

This application also motivates some topics that are not usually covered in an introductory data structures class, including persistent data structures, with Redis, and streaming algorithms.

I have made difficult decisions about what to leave out, but I have made some compromises. I include a few topics that most readers will never use, but that they might be expected to know, possibly in a technical interview. For these topics, I present both the conventional wisdom as well as my reasons to be skeptical.

This book also presents basic aspects of software engineering practice, including version control and unit testing. Each chapter ends with an exercise that allows readers to apply what they have learned. Each exercise includes automated tests that check the solution. And for most exercises, I present my solution at the beginning of the next chapter.

This book is intended for college students in computer science and related fields, as well as professional software engineers, people training in software engineering, and people preparing for technical interviews.

I assume that the reader knows Java at an intermediate level, but I explain some Java features along the way, and provide pointers to supplementary material.

People who have read Think Java or Head First Java are prepared for this book.

Think Java is an introduction to computer science and programming intended for people with little or no experience. We start with the most basic concepts and are careful to define all terms when they are first used. The book presents each new idea in a logical progression. Larger topics, like recursion and object-oriented programming, are divided into smaller examples and introduced over the course of several chapters.

Table of Contents:
Preface
Chapter 1: Computer Programming
Chapter 2: Variables and Operators
Chapter 3: Input and Output
Chapter 4: Methods and Testing
Chapter 5: Conditionals and Logic
Chapter 6: Loops and Strings
Chapter 7: Arrays and References
Chapter 8: Recursive Methods
Chapter 9: Immutable Objects
Chapter 10: Mutable Objects
Chapter 11: Designing Classes
Chapter 12: Arrays of Objects
Chapter 13: Objects of Arrays
Chapter 14: Extending Classes
Chapter 15: Arrays of Arrays
Chapter 16: Reusing Classes
Chapter 17: Advanced Topics
Appendix A: Tools
Appendix B: Javadoc
Appendix C: Graphics
Appendix D: Debugging
Index

Think Java is an introduction to Java programming for beginners. It is tailored for students preparing for the Computer Science Advanced Placement (AP) Exam, but it is for anyone who wants to learn Java.

The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.

Think Raku is an introduction to computer science and programming intended for people with little or no experience.

This aim of this book is not primarily to teach Raku, but instead to teach the art of programming, using the Raku language. After having completed this book, you should hopefully be able to write programs to solve relatively difficult problems in Raku, but my main aim is to teach computer science, software programming, and problem solving rather than solely to teach the Raku language itself.

Think Raku is a free book available under a Creative Commons license. Readers are free to copy and distribute the text; they are also free to modify it, which allows them to adapt the book to different needs, and to help develop new material.

Think Stats is an introduction to Probability and Statistics for Python programmers.

*Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.
*If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.

With much of the world gone digital, learning to create documents that are accessible to everyone is becoming a necessary skill. Intended for a general audience, this free resource reviews a wide range of document authoring applications, including the tools they contain for creating accessible documents, and tests them to ensure they do not contain potential barriers. Learn how to create accessible word processed documents, spreadsheets, presentation slides, and PDF documents, among others, so they are accessible to everyone.

This text is a practical guide for linguists, and programmers, who work with data in multilingual computational environments. We introduce the basic concepts needed to understand how writing systems and character encodings function, and how they work together at the intersection between the Unicode Standard and the International Phonetic Alphabet. Although these standards are often met with frustration by users, they nevertheless provide language researchers and programmers with a consistent computational architecture needed to process, publish and analyze lexical data from the world's languages. Thus we bring to light common, but not always transparent, pitfalls which researchers face when working with Unicode and IPA. Having identified and overcome these pitfalls involved in making writing systems and character encodings syntactically and semantically interoperable (to the extent that they can be), we created a suite of open-source Python and R tools to work with languages using orthography profiles that describe author- or document-specific orthographic conventions. In this cookbook we describe a formal specification of orthography profiles and provide recipes using open source tools to show how users can segment text, analyze it, identify errors, and to transform it into different written forms for comparative linguistics research.

This lesson is connected to but can be used independently of "Using Big Data to Identify and Understand Educational Inequality in America (1)"

Students will develop CS skills and behaviors including but not limited to: learning what an API is, learning how to access and utilize data on an API, and developing their R coding skills and knowledge. Students will also learn basic, but important, sociological principles such as how poverty is related to educational opportunities in America (and how this relationship varies between and among states). Although prior knowledge of CS and sociology is helpful, neither is necessary for student (or instructor) success on this project. Three instructional hours.