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Graduate Admissions and Tests
How to Apply:
Applicants are expected to have academic backgrounds and records that clearly indicate their potential to excel in a rigorous graduate program in IT. GRE scores are required. Admissions applications and detailed instructions are available from Graduate Admissions.
IT applicants also complete the IT Background Evaluation Form (Word file, 94k) and send it to the Admissions Office with other materials or to the IT office via e-mail to gereg@rpi.edu.
Program Prerequisites:
A three course prerequisite sequence in Computer Science that is equivalent to the Rensselaer courses listed below is required prior to enrolling for the Master’s in IT. We welcome applications from students with a wide variety of backgrounds. Students without the full computer science sequence may be able to complete the required courses via on-line course work or at colleges near their homes as preparation to undertake the IT master’s.
Rensselaer Prerequisite Sequence:
- CSCI-1100 Computer Science I: Fundamentals of Computer Science
An introduction to algorithm design and analysis, programming, and use of the World Wide Web for information dissemination and retrieval. Additional topics include basic computer organization; internal representation of scalar and array data; use of top-down design and subprograms to tackle complex problems; abstract data types. Enrichment material as time allows. Interdisciplinary case studies, numerical and nonnumerical applications.
- CSCI-1200 Computer Science II: Data Structures, Introductory Algorithm Analysis
Programming concepts: functions, parameter passing, pointers, arrays, strings, structs, classes, templates. Mathematical tools: sets, functions, and relations, O-notation, complexity of algorithms, proof by induction. Data structures and their representations: data abstraction and internal representation, sequences, trees, binary search trees, associative structures. Algorithms: searching and sorting, generic algorithms, iterative and recursive algorithms. Methods of testing correctness and measuring performance.
- CSCI-2300 “Advanced” Data Structures and Algorithms
Data structures and algorithms, and the mathematical techniques necessary to design and analyze them. Basic data structures: lists, associative structures, trees. Mathematical techniques for designing algorithms and analyzing worst-case and expected-case algorithm efficiency. Advanced data structures: balanced trees, tries, heaps, priority queues, graphs. Searching, sorting. Algorithm design techniques: dynamic programming, greedy algorithms, divide-and-conquer, backtracking. Example graph, string, geometric, and numeric algorithms.
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