Many professionals in health, education, and community service roles are caught in a particular bind of identity--they live in a complex social borderland of credibility and professional authority while experiencing or having experienced the same discrimination, violence or trauma that they are committed to conquering. For some, the disclosure of their own stories of marginalization has become a tool for advocacy, for telling a larger truth; for others, self-disclosure is a more personal action, intended to assist isolated others in developing trust and connection. Linde Zingaro, a lifelong social service worker and activist, interviewed several colleagues who have chosen to speak out in this way, talking with them about their ethics and intentions, and collaborating to identify some of the risks of negative personal and professional consequences for the practitioner. She uses their voices--and her own--to illustrate some of the ways that these people have learned to safely and effectively use the transformative potential of storytelling as significant social action. This examination of speaking out as a meaningful social practice may help other workers, activists, and community researchers in their efforts to be heard in the interests of a more just society.

Get in the game and learn essential computer algorithms by solving competitive programming problems, in the fully revised second edition of the bestselling original. (Still no math required!) Knowing how to design algorithms will take you from being a good programmer to a great programmer. This completely revised second edition teaches you how to design your own rocket-fast, right-for-the-task algorithms—minus the proofs and complex math. Forget the useless pseudocode and played-out examples you’ve seen in other books. Author and award-winning educator Dan Zingaro draws problems straight from online programming competitions to rigorously teach you all of the heavyweights you need to know, like hash tables, recursion, trees, graphs, and heaps. As he guides you to the perfect algorithmic solution for each unique programming puzzle, you’ll build up a toolkit of go-to algorithms for quickly and correctly solving any problem you come across. The second edition features several entirely new chapters on dynamic programming and randomized algorithms, as well as more effective problems and enhanced explanations. Code examples are provided using the C language. Learn how to: Classify problems, choose data structures, and identify appropriate algorithms Choose between data structures like hash tables, heaps, or trees, based on how they affect runtime and speed Adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems Apply the breadth-first search algorithm to find the optimal way to play a board game, Dijkstra’s algorithm to determine the fastest routes between two locations, and many more

Learn to Program by Solving Problems is a practical introduction to programming using Python, one of the world's most popular programming languages. The book emphasises problem-solving strategies that teach readers not only the mechanics of coding, but how to think like savvy programmers. Teaches readers how to use Python to solve short, situational problems (for example, how to predict when a gambler will run out of money while playing slot machines; how to create a programme to track cell phone data usage; how to set up a system of identifying the popularity of berths in a parking lot).

Algorithms are central to all areas of computer science, from compiler construction to numerical analysis to artificial intelligence. Throughout your academic and professional careers, you may be required to construct new algorithms, analyze existing algorithms, or modify algorithms to suit new purposes. How do we know that such algorithms are correct? One method involves making claims about how we expect our programs to operate, and then constructing code that carries out these tasks. The key component of such reasoning is the invariant, and is the topic of this book. In these pages, you will study how invariants are developed, how they are used to construct correct algorithms, and how they are helpful in analyzing existing programs. Along the way, you'll be introduced to some classic sorting, searching and mathematical algorithms, and even some solutions to games and logic puzzles. These examples, though, are only conduits for the loftier goal: understanding why algorithms work.