This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
Below are audio, video and lecture sides for 1997 and 2007. Since the lectures are 10 years apart some of the topics covered by the course have changed. The 1997 lectures have a better quality video and audio than the 2007, although the 2007 covers the newer material and has better lecture notes.
Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course covers major results and current research directions in data structures:
Traditional algorithms works well when the input data fits entirely within memory. In many modern application contexts, however, the size of the input data is too large to fit within memory. In some cases, data is stored in large data centres or clouds and specific parts of it can be accessed via queries. In some other application contexts, very large volume of data may stream through a computer one item at a time.
Everything has a beginning and everything must be built from the ground up. This holds true even when
it comes to software engineering and programming languages. Data structures provide a grounding for
programming language and hold data and codes that determine what action will trigger what reaction.
Data structures and Algorithms are two important concepts when it comes to learning any programming
language, functional or object oriented, from the ground up. In order to master a language, you must
first master the basic groundwork for that language.