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RubyConf 2016 - Computer Science: The Good Parts by Jeffrey Cohen You don't need a 4-year degree to learn the best parts of computer science. Maybe you've seen jargon like "Big O Notation," "binary trees", "graph theory", "map-reduce", or the "Turing test", but have been afraid to ask. But did you know that that these concepts are responsible for Mars rovers, self-driving cars, mobile databases, our air traffic control system, and and even how we elect presidents? In this beginner-focused talk, I will present some simple and very practical ways apply the "good parts" of computer science to your next app.
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In "Computer Science: The Good Parts," Jeffrey Cohen presents a beginner-friendly overview of fundamental computer science concepts, emphasizing their practical applications in modern technology. Drawing from his own non-traditional journey into the field, Cohen highlights that one doesn't need a formal computer science degree to grasp important principles that are pivotal in developing software today. **Key Points:** - **Personal Journey**: Cohen shares his background as a self-taught developer who initially faced imposter syndrome and developed an aversion to traditional computer science education, later becomming a master’s instructor in the subject. - **Data Structures**: He discusses the importance of data structures for data storage, emphasizing that programming revolves around data transformation. Concepts such as linked lists and binary trees are introduced to illustrate how data can be organized efficiently for retrieval and processing. - **Binary Trees and Algorithms**: Cohen explains how binary trees facilitate quick data management and decision-making processes, drawing parallels to structures applied in systems like air traffic control or social networks. - **Graphs and Connections**: He elaborates on graph theory, showing how these models help in finding optimal paths, such as routing in navigation systems, underscoring their relevance through real-world applications. - **Historical Context**: Through anecdotes about influential figures in computing, like Alan Turing and Grace Hopper, Cohen connects foundational computer science concepts to historical events, emphasizing the impact of their work on modern computing. - **Complexity Analysis with Big O Notation**: He introduces concepts like Big O notation to assess algorithm efficiency, explaining variations such as O(n) and O(log n), which indicate how execution time can scale with input size. - **The Broader Impact of Computer Science**: He concludes by affirming that computer science extends beyond data structures and algorithms; it involves using programming skills to make meaningful contributions to society, encouraging developers to engage with their communities and foster innovation. In summary, Cohen's talk serves as an empowering call to embrace the foundational aspects of computer science, demonstrating that any developer can harness these
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