This course focuses on the fundamentals of computer algorithms, emphasizing methods useful in practice. Upon successful completion of this course, the student will be able to: explain and identify the importance of algorithms in modern computing systems and their place as a technology in the computing industry; indentify algorithms as a pseudo-code to solve some common problems; describe asymptotic notations for bounding algorithm running times from above and below; explain methods for solving recurrences useful in describing running times of recursive algorithms; explain the use of Master Theorem in describing running times of recursive algorithms; describe the divide-and-conquer recursive technique for solving a class of problems; describe sorting algorithms and their runtime complexity analysis; describe the dynamic programming technique for solving a class of problems; describe greedy algorithms and their applications; describe concepts in graph theory, graph-based algorithms, and their analysis; describe tree-based algorithms and their analysis; explain the classification of difficult computer science problems as belonging to P, NP, and NP-hard classes. (Computer Science 303)

American Government is designed to meet the scope and sequence requirements of the single-semester American government course. This title includes innovative features designed to enhance student learning, including Insider Perspective features and a Get Connected Module that shows students how they can get engaged in the political process. The book provides an important opportunity for students to learn the core concepts of American government and understand how those concepts apply to their lives and the world around them. American Government includes updated information on the 2016 presidential election.Senior Contributing AuthorsGlen Krutz (Content Lead), University of OklahomaSylvie Waskiewicz, PhD (Lead Editor)

This course is a continuation of the first-semester course titled Introduction to Computer Science I. It will introduce the student to a number of more advanced Computer Science topics, laying a strong foundation for future academic study in the discipline. The student will begin with a comparison between Java--the programming language utilized last semester--and C++, another popular, industry-standard programming language. The student will then discuss the fundamental building blocks of Object-Oriented Programming, reviewing what they have learned learned last semester and familiarizing themselves with some more advanced programming concepts. The remaining course units will be devoted to various advanced topics, including the Standard Template Library, Exceptions, Recursion, Searching and Sorting, and Template Classes. By the end of the class, the student will have a solid understanding of Java and C++ programming, as well as a familiarity with the major issues that programmers routinely address in a professional setting. Upon successful completion of this course, the student will be able to: Demonstrate an understanding of the concepts of Java and C++ and how they are used in Object-Oriented Programming; Demonstrate an understanding of the history and development of Object-Oriented Programming; Explain the importance of the C++ Standard Template Library and how basic components are used; Demonstrate a basic understanding of the importance of run-time analysis in programming; Demonstrate an understanding of important sorting and search routines in programming; Demonstrate an understanding of the generic usage of templates in programming for C++ and Java; Compare and contrast the features of Java and C++. (Computer Science 102; See also: Mathematics 303)

This course presents the fundamentals of object-oriented software design and development, computational methods and sensing for engineering, and scientific and managerial applications. It cover topics, including design of classes, inheritance, graphical user interfaces, numerical methods, streams, threads, sensors, and data structures. Students use Java programming language to complete weekly software assignments. How is 1.00 different from other intro programming courses offered at MIT? 1.00 is a first course in programming. It assumes no prior experience, and it focuses on the use of computation to solve problems in engineering, science and management. The audience for 1.00 is non-computer science majors. 1.00 does not focus on writing compilers or parsers or computing tools where the computer is the system; it focuses on engineering problems where the computer is part of the system, or is used to model a physical or logical system. 1.00 teaches the Java programming language, and it focuses on the design and development of object-oriented software for technical problems. 1.00 is taught in an active learning style. Lecture segments alternating with laboratory exercises are used in every class to allow students to put concepts into practice immediately; this teaching style generates questions and feedback, and allows the teaching staff and students to interact when concepts are first introduced to ensure that core ideas are understood. Like many MIT classes, 1.00 has weekly assignments, which are programs based on actual engineering, science or management applications. The weekly assignments build on the class material from the previous week, and require students to put the concepts taught in the small in-class labs into a larger program that uses multiple elements of Java together.

Introduction to Sociology 2e adheres to the scope and sequence of a typical, one-semester introductory sociology course. It offers comprehensive coverage of core concepts, foundational scholars, and emerging theories, which are supported by a wealth of engaging learning materials. The textbook presents detailed section reviews with rich questions, discussions that help students apply their knowledge, and features that draw learners into the discipline in meaningful ways. The second edition retains the book’s conceptual organization, aligning to most courses, and has been significantly updated to reflect the latest research and provide examples most relevant to today’s students. In order to help instructors transition to the revised version, the 2e changes are described within the preface.

Define manifest and latent functions of education
Explain and discuss how functionalism, conflict theory, feminism, and interactionism view issues of education

Studies how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Models of randomized computation. Data structures: hash tables, and skip lists. Graph algorithms: minimum spanning trees, shortest paths, and minimum cuts. Geometric algorithms: convex hulls, linear programming in fixed or arbitrary dimension. Approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.