1.1 Data Structures1.2 Introduction to Algorithms1.3 Relation between data structures and algorithms1.4 Abstract data types1.5 Applications of ADTs1.6 Algorithm efficiency
A data structure is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to solve a particular problem. Learning data structures and algorithms allow us to write efficient and optimized computer programs.
Data Structures And Algorithm Analysis In C (2nd Edition) Download.zip
Object-oriented programming, recursion, fundamental data structures (including stacks, queues, linked lists, hash tables, trees, and graphs), the basics of algorithmic analysis, and an introduction to the principles of language translation.
This course builds upon the foundations of programming and computation by introducing and studying the data structures and programming language features that support the design and construction of large-scale software systems. It introduces the foundations of object-oriented programming, the design and analysis of efficient algorithms using important data structures, and programming techniques that support reusable and modular program components, including data abstraction, polymorphism, and higher-order functions. Topics from object-oriented programming include classes, objects, inheritance, methods, message passing, static and dynamic type checking. These topics form the core of most object-oriented languages and provide a foundation for learning more advanced language topics. Data structures capture the common organization of many kinds of data arising in the design of efficient solutions to computational problems. Specific data structures covered include stacks, queues, trees, graphs and linked lists. The design and analysis of efficient algorithms using these data structures provide a foundation for the study of computing, where understanding the complexity of a problem and the availability of efficient solutions are essential skills. Finally, topics including higher-order functional programming, data abstraction and parametric polymorphism, as well as principles from object-oriented programming, come together to support the design and implementation of modular, reusable and robust code.
This course introduces basic programming concepts including algorithm development, simple data types, number representation, control structures, functions, plotting, and basic numerical analysis techniques, all in the context of science and engineering. The course enables students to develop computer programs in a scientific programming language to solve simple engineering and science problems. The basic numerical analysis techniques covered in the course include matrix operations, systems of equations, solving equations, roots, curve fitting, interpolation, numerical integration, and numerical solutions of ordinary differential equations. Students analyze engineering and science problems, develop algorithms to solve those problems, implement their algorithms in an appropriate programming language, and produce informative output in both numerical and graphical form. The general programming concepts learned in the course are commonly found in most programming languages. The problem-solving skills learned in the course can then be utilized in upper-level engineering and science courses. This course includes an exploration of the conceptual and syntactical background needed for the successful completion of practical programming assignments. The course also includes an emphasis on the actual design and implementation of computer programs necessary for solving computational problems. This course satisfies University quantification requirements because it assists students in assessing and interpreting quantitative data and information. In addition, the course assists students with recognizing patterns, establishing relations, exercising conceptual thinking, developing problem-solving skills, and thinking logically and critically. Finally, the course supports students in their efforts to draw accurate and useful conclusions, to make informed decisions based on quantitative analysis, and to use mathematical skills to solve conceptual problems. A student may use only one of the following courses to satisfy degree requirements: CMPSC 101, 102, 121, 131, 200, 201.
This course introduces intermediate concepts of programming including object-oriented programming, dynamic data structures, and data visualization and data modeling. The core of this course covers data analysis, efficient usage of data structure, simulations, data visualization, and data structure collections. The topics include, but are not limited to, the study and the use of lists, stacks, queues, and hash tables. Emphasis will revolve around application to scientific computing libraries in conjunction with data abstraction, object-oriented program design, data visualization, and data manipulation. Hands-on programming assignments and usage of scientific libraries are the integral part of this course.
Unix system programming in C; organization of programs and data; program analysis and support tools; software standards; common system functions. CMPSC 311 CMPSC 311 Introduction to Systems Programming (3) System Programming concerns the development of software components and methods for their combination, independent of any particular application. This course will provide information and experience required to understand, design and implement components of large software systems.In general, students should be able to evaluate design alternatives according to standard practice, specifications, performance analysis, robustness, etc. To concentrate attention, we investigate one system and one programming language in detail, through demonstration programs, short- and long-term programming assignments. The specific system is Unix, a family of operating systems forming a complete standardized programming environment based on the idea of software tools. The specific language is C, which is widely used for operating system implementations, and which forms the basis for the C++ and Java languages studied in the prerequisite courses. This will help students understand operating system services available to application programmers, and provide a firm ground for study of operating systems in general.There are several themes of the course:(1) Understand computer systems, especially low-level influences on high-level goals. This includes the machine-level representation of programs and data structures; the memory hierarchy and its impact on performance; access to stored information via file systems, and access to other computer systems via networks.(2) Understand existing system software and software standards, especially the UNIX toolset. This includes preparing a program (editors, static analysis, development environments); running a program (compilers and interpreters, assembler, linker, loader, debugger, profiler, tracer); controlling parts of a program (memory management, threads); communication between programs (within one system using signals, between systems using sockets and communication protocols); and combinations of software tools with scripting languages.(3) Understand "real code", such as selections from the Linux operating system kernel and GNU utilities and libraries, and through comparative selections from Solaris, Linux, and Mac OS X.(4) Understand system performance, including experiments on program performance and optimization techniques.
This course will introduce students from different engineering and sciences disciplines to the data driven problem solving methods using machine learning techniques including clustering, classification, data modeling, and data visualization. The course will focus on processes, techniques, and algorithms for data mining and machine learning. Processes and phases include data preparation, exploration, modeling, and prediction. Specific clustering and classification techniques will be used to emphasize machine learning models using high-level language and their libraries. Students will learn and apply key concepts of modeling, analysis and validation from machine learning and data mining to analyze and develop data-driven predictive models. Upon completion of the course, students will be able to develop machine learning based solutions with applications in different science and engineering domains.
Recommended Preparations: DS 310; CMPSC 448 This course introduces modern programming models and related software stacks for performing scalable data analytics and discovery tasks over massive and/or high dimensional datasets. The learning objectives of the course are that the students are able to choose appropriate programming models for a big data application, understand the tradeoff of such choice, and be able to leverage state-of-the art cyber infrastructures to develop scalable data analytics or discovery tasks. Building on data models covered in DS 220, this course will introduce programming models such as MapReduce, data flow supports for modern cluster computing environment, and programming models for large-scale clustering (either a large number of data samples or a large number of dimensions). Using these frameworks and languages, the students will learn to implement data aggregation algorithms, iterative algorithms, and algorithms for generating statistical information from massive and/or high-dimensional data. The realization of these algorithms will enable the students to develop data analytic models for massive datasets.
This course provides hands-on practice with a wide range of computer science topics that are used to solve programming contest questions. These topics include common data structures; strings; sorting; combinatorics; number theory; graph traversal and other algorithms; dynamic programming. In the course, students will solve a range of programming contest questions, both using an online judge in practice sessions and during actual programming contests. In addition to solving programming problems, the course time will also be used to explore topics mentioned above and the relationship to specific problems, solution techniques, and the analysis of proposed solutions to specific problems. This course is envisioned to be a hands-on lab, with instructor supported, self-guided study. The course topics will be chosen from topics that commonly appear in current programming contests, including but not limited to: Contest Programming Common Data Structures Strings Sorting Combinatorics Number Theory Graph Traversal and Other Algorithms Dynamic Programming 2ff7e9595c
Comments