This document provides an overview of a course on data structures and algorithms. The course covers fundamental data structures like arrays, stacks, queues, lists, trees, hashing, and graphs. It emphasizes good programming practices like modularity, documentation and readability. Key concepts covered include data types, abstract data types, algorithms, selecting appropriate data structures based on efficiency requirements, and the goals of learning commonly used structures and analyzing structure costs and benefits.

1. Lecture 1
Data Structures

2. Introduction
• Course outline
• Rules and regulations
• Course contents
• Good Programming Practices
• Data Types and Data Structures
• ADT

3. Course Description
• Title: Data Structures and Algorithms
• Code: CMP-210
• Credit Hours:3
• Prerequisite: OOP
• Follow Up: Design and Analysis of Algorithms

4. Contents
• Introduction to Algorithms
• Arrays
• Stacks
• Recursion
• Queues
• Lists and its variations
• Trees
• Hashing
• Searching and sorting Techniques
• Graphs

5. Good Programming Practices
• Programs and subprograms should be well structured.
Use a modular approach for a complex problem.
Use the basic control structures when developing each code
segment.
Sequence, selection, repetition
Use local variables within subprograms.
Use parameters to pass information to and from subprograms.
Avoid global Variables.
• All source code should be documented.
 Each program should include opening documentation.
 Each subprogram should be documented in a manner similar to
programs.
 Comments should be used to explain key code segments and / or
segments whose purpose or design is not obvious.
 Use meaningful identifiers.

6. Good Programming Practices
• Source code should be aesthetic; it should be formatted in a
style that enhances its readability.
Put each statement of the program on a separate line.
Use uppercase and lowercase letters in a way that contributes
to program readability.
Put each {and} on separate lines.
Align each {and corresponding}.
When a statement is continued from one line to another, indent
the continued line(s).
Align the identifiers in each constant and variable declaration,
placing each on a separate line.
Insert blank lines between declarations and statements and
between blocks of statements to make clear the structure of the
program.

7. Data Types
In computer programming, a data type is a classification
identifying one of various types of data, such as floating-point,..
• Simple (basic)
– char, int, float, double
• Modifiers
– signed, unsigned
• Qualifiers
– static, const, volatile, void
• Structured (derived)
– Arrays, structures, unions, classes
• Advanced (composite)
– List, queues, stacks, trees, graphs

8. Data Structures and algorithms
• Algorithms
– Outline, the essence of a computational procedure, step
by step instruction
• Program
– An implementation of an algorithm in some computer
programming languages
• Data Structures
– Goal: organize data
– Criteria: facilitate efficient
• storage of data
• Retrieval/recovery of data
• Operation/manipulation of data
• Process:
– select and design appropriate data types
Data may be organized in many
different ways. the logical and
mathematical model of a
particular organization of data
is called Data structure

9. Need for Data Structures
 Data structures organize data  more
efficient programs.
 More powerful computers  more
complex applications.
 More complex applications demand more
calculations.

10. Organizing Data
 Any organization for a collection of records
that can be searched, processed in any
order, or modified.
 The choice of data structure and algorithm
can make the difference between a
program running in a few seconds or many
days.

11. Efficiency
 A solution is said to be efficient if it solves
the problem within its resource constraints.
– Space
– Time
 The cost of a solution is the amount of
resources that the solution consumes.

12. Selecting a Data Structure
Select a data structure as follows:
1. Analyze the problem to determine the
resource limitations a solution must meet.
2. Determine the basic operations that must
be supported. Count the resource
limitations for each operation.
3. Select the data structure that best meets
these requirements.

13. Examples
Searching an online phone directory: Linear search?
OK for small organizations
too slow for large ones
 Amount of data is an important factor.
Compiler lookup of an identifier's type, etc. in a symbol table:
Linear search? No, too slow
Binary search? No, too much work to keep sorted
 Number of accesses & speed required is an important factor.
Use hash tables
Text processing: Store in an array / vector?
OK for text analysis — word counts, average word length, etc.
Not for word-processing — Too inefficient if many insertions & deletions
   Static vs. dynamic nature of the data is an important factor

14. Abstract Data Type (ADT)
• Abstraction
– Separating data from operation
– Generalization
– a concept or idea not associated with any specific
instance
• ADT(Abstract Data Type)
– A collection of related data items together with basic
operations between them and operations to be performed
on them
– A data type is a collection of values and set of operation on
those values
• values and operation that are implemented using a hardware or
software data structure

15. abstract data type (ADT)
a collection of
related data items together with
an assoicated set of operations
e.g. whole numbers (integers) and arithmetic operators for
addition, subtraction, multiplication and division.
Why "abstract?"
Data, operations, and relations are studied
independent of implementation.
What not how is the focus.

16. implementation of an ADT
consists of
storage structures (aka data structures) to store the data items
and
algorithms for the basic operations and relations.
The storage structures/data structures used in implementation are
provided in a language (primitive or built-in)
or built from the language constructs (user-defined).
In either case, successful software design uses data abstraction:
Separating the definition of a data type from its implementation.

17. Goals of this Course
1. Reinforce the concept that costs and benefits
exist for every data structure.
2. Learn the commonly used data structures.
– These form a programmer's basic data structure
“toolkit”.
3. Understand how to measure the cost of a data
structure or program.
– These techniques also allow you to judge the merits
of new data structures that you or others might
invent.