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Day 1.

Day 1. 1. Syllabus / Overview of course. 2. Data structures. 3. Abstract programming methods. 4. Simulation. 5. Quality S/W. 1. Syllabus / Overview. See syllabus for: 1) Office hours. 2) Texts--- (a) for a few classes, we will reuse Laitinen’s text.

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Day 1.

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  1. Day 1. 1. Syllabus / Overview of course. 2. Data structures. 3. Abstract programming methods. 4. Simulation. 5. Quality S/W.

  2. 1. Syllabus / Overview • See syllabus for: • 1) Office hours. • 2) Texts--- • (a) for a few classes, we will reuse Laitinen’s text. • (b) McMillan’s book is the main text. • (c) The curriculum text Computer Science: An overview (for this class and all further CSC classes) is also helpful. • 3) Objectives. 4) Grading. 5) Schedule.

  3. Web-site. • As with CSC 250, the CSC 300 Web-site is found from www.cs.cuw.edu • You will find the syllabus, PowerPoints and lecture notes, and example programs, many of which are in process of being re-written.

  4. Extra help. • If anyone wants a tutor for this class, please let me know as soon as possible. • Also, work with me, by letting me know promptly when you need help. You can send me questions / code, come to office hours.

  5. Overview • The 4 main ideas of this course are: • 1) Data structures; • 2) Abstract Programming Methods; • 3) Simulation; • 4) Quality S/W.

  6. 1) Data structures. • 3 definitions: • 1) A data structure is an organized collection of data with an access method. • 2) An access method is a mechanism for accessing individual data elements. • 3) An (A)bstract (D)ata (T)ype = the abstract, logical idea of a data structure.

  7. Data Structures (cont.). • “Abstract” in what sense? • A concept / logical idea, independent of H/W and S/W implementation. • Note the analogy between an algorithm and an ADT. • An algorithm is an abstract specification of……. • An ADT is an abstract specification of……

  8. Data Structures (cont.). • Just as one algorithm can be translated into many programs and run on many H/W platforms, one ADT can be implemented in many different ways. • All the ADTS we study can be implemented by static and dynamic approaches.

  9. Data Structures (cont.). • We will study the following 4 main ADTs: • (1) Stacks; • (2) Queues; • (3) Lists; • (4) Trees. • These do not exist in the programming language—e.g. no keyword “stack,” but we can simulate them e.g. using arrays / linked lists.

  10. Data Structures (cont.). • Simulations are not the same as the real thing (e.g. simulated leather). • E.g. a real stack can shrink or grow, but an array cannot. But we can model this by having the stack shrink or grow inside the stack. • Idea of virtual characteristics.

  11. Data Structures (cont.). • All ADTS are illustrated by a visit to the station. • E.g. • 1) Stack of mail on a cart; • 2) Queue of customers at the ticket office; • 3) List of platforms and destinations; • 4) Tree of railway lines from a major hub.

  12. Data Structures (cont.). • Why study ADTs? • 1) We need to understand the idea / concept of a data structure before we can implement it. • 2) We then have the freedom to implement the ADT in the best way (e.g. most efficient / most flexible) for a given application. • 3) We can focus on a standardized interface independent of implementation. Why?

  13. 2. Abstract programming methods. • This leads to the idea of abstract programming, where we carefully distinguish what a data structure is from how it is implemented. • Note the parallel between control abstraction and data abstraction.

  14. The .NET framework. • C# facilitates data abstraction with the .NET framework. This is extra code which makes programming with complex data easier, e.g. there are ArrayList and StringBuilder classes that simplify working with linear lists and strings, by hiding details of implementation and providing simple, intuitive services.

  15. 3. Simulation. • Simulation operates at 2 levels: • 1) We will use virtual characteristics e.g. static features can seem to be dynamic; • 2) We will model real world systems, e.g. real world queues (electronic time models real time, numbers represent real objects [like The Matrix]),

  16. 4. Quality S/W. • This topic is explored in more depth in CSC370, but we emphasize S/W that is: • 1) Carefully planned; • 2) Modifiable; • 3) Well-documented; • 4) Robust; • 5) Testable and rigorously tested.

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