Development of mathematical reasoning skills for problems that involve uncertainty. Counting and probability -- basic counting problems, probability definitions, mean, variance, binomial distribution, discrete random variables, continuous random variables, Markov and Chebyshev bounds, Laws of large number, and central limit theorem. Probabilistic reasoning -- conditional probability and odds, Bayes' Law, Markov Chains, Bayesian Network, Markov Decision Processes.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to implement abstract data collections and their associated operations. Specific implementations include linked structures, recursive structures, binary trees, balanced trees, and hash tables. Algorithm analysis and asymptotic bounding of implementations is a major topic throughout the course. The topics covered in this course are fundamental to programming and are essential to further computer science courses.

Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results. On the other hand, many computer systems don't perform nearly as well as Google - hence the now-cliche "the system is down." In this class, we study the scientific principles behind the construction of high-performance, scalable systems.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

This course provides an introduction to the principles and practice of computer and network security. A focus on both fundamentals and practical information will be stressed. The three key topics of this course are cryptography, privacy, and network security. Subtopics include ciphers, hashes, key exchange, security services (integrity, availability, confidentiality, etc.), security attacks, vulnerabilities, anonymous communications, and countermeasures.

Practical experience in the management of software development projects. Managing people, coaching, mentoring, planning, scheduling, evaluation, negotiation, and expectation management are explored. The focus is on the power of communication.

Development of mathematical reasoning skills for problems that involve uncertainty. Counting and probability -- basic counting problems, probability definitions, mean, variance, binomial distribution, discrete random variables, continuous random variables, Markov and Chebyshev bounds, Laws of large number, and central limit theorem. Probabilistic reasoning -- conditional probability and odds, Bayes' Law, Markov Chains, Bayesian Network, Markov Decision Processes.

A complete introduction to computer programming using the Python language. Topics include coverage of all the supported data types and program code structures, functions (up through lambda expressions and recursion), reasoning about and debugging existing code, implementation of custom libraries, selection of data structures, and the fundamentals of object-oriented programming.