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Day 1.1. Syllabus / Overview of course.

2. Data structures.

3. Abstract programming methods.

4. Simulation.

5. Quality S/W.

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1. Syllabus / Overview See syllabus for:

1) Office hours.

2) Texts---

(a) for a few classes, we will reuse Laitinens text.

(b) McMillans book is the main text.

(c) The curriculum text Computer Science: An

overview (for this class and all further CSCclasses) is also helpful.

3) Objectives. 4) Grading. 5) Schedule.

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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.

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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.

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Overview The 4 main ideas of this course are:

1) Data structures;

2) Abstract Programming Methods;

3) Simulation;

4) Quality S/W.

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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.

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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 andan ADT.

An algorithm is an abstract specificationof.

An ADT is an abstract specification of

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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.

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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 languagee.g. no keyword stack, but we can simulate

them e.g. using arrays / linked lists.

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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.

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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.

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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?

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2. Abstract programming methods.

This leads to the idea ofabstract

programming, where we carefully

distinguish what a data structure is from

how it is implemented.

Note the parallel between control

abstraction and data abstraction.

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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 ofimplementation and providing simple,

intuitive services.

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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]),

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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.