Models, algorithms, recurrences, summations, growth rates. Probabilistic tools, upper and lower bounds; worst-case and average-case analysis, amortized analysis, dynamization. Comparison-based algorithms: search, selection, sorting, hashing. Information extraction algorithms (graphs, databases).
DurationModels, algorithms, recurrences, summations, growth rates. Probabilistic tools, upper and lower bounds; worst-case and average-case analysis, amortized analysis, dynamization.
Comparison-based algorithms: search, selection, sorting, hashing. Information extraction algorithms (graphs, databases). Graphs algorithms: spanning trees, shortest paths, connectivity, depth-first search, breadth-first search.
A leader in interdisciplinary research and education
As the first school of its kind in the United States, the Indiana University Luddy School of Informatics, Computing, and Engineering is an innovator in a fast-paced and dynamic field. Our school on the IUPUI campus integrates computing, social science, and information systems design in unique ways.
More than 4,500 students—including over 1,400 at Luddy IUPUI—study informatics on IU campuses. Our top-notch programs and highly regarded faculty prepare them for the power and possibilities in computing and information technology.
This course is designed to give experienced web developers an advanced understanding of algorithmic thinking, the varieties of common data structures and their applications in databases and modern blockchain applications.
This course covers data structures, recursion, analysis, sorting and searching (sequential and binary), tree and tree algorithms, graphs and graph algorithms, as related to organizational problem solving across industries.
Systematic study of data structures encountered in computing problems, methods of representing structured data, and techniques for operating on data structures.Â
The AP Computer Science Java is a UC and CSU approved college preparatory elective course offered by High Schools.
The analysis and design of computer algorithms and their underlying data structures. Analysis of the timing and efficiency of algorithms. Study of lists, stacks, queues, trees, backtracking, searching, sorting and recursion.
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