The document discusses algorithm and data structures as the basic foundations of IT problem solving. It covers topics in discrete mathematics, computer architecture, computer science theory, and information technology from both theoretical and engineering perspectives. Key aspects covered include programming principles, algorithms, common data structures like arrays, lists, maps, stacks and queues. It emphasizes that algorithms and data structures are closely related, with algorithms operating on data structures to solve problems. Examples are provided of problems, inputs, outputs, and the algorithms and data structures that can be used.

1. Algorithm and Data Structures
as the basic of IT Problem Solving
Christian A – Mei 2014
Training Material

2. • Information Technology Overview
– Theoretical
– Engineering
– Management
• Programming Principles
• Algorithm
• Data Structures
• Programming Concepts
Topics

3. • Discrete Mathematics
– Number Theory
– Set Theory, Relation, Function
– Combinatorics, Probability
– Graph
– Logic, Proof Theory
• Formal Foundation:
– Automata
– Turing Machine (model of computation: 1970 – now)
• Computer Architecture
– Von Neumann Architecture:
stored program architecture
• Computer Science (birth of : 1937-1970)
– Theory of Computation
– Branches:
• Automata Theory
• Computability Theory
• Computational Complexity Theory
Information Technology - Theoretical

4. • Programming
• Software Engineering:
– Requirement, Analysis, Design, Implementation
• Database
• Computer Networks
• Computer Security
Information Technology - Engineering

5. • Project Management
• Enterprise IT Architecture
• IT Governance
• IT Strategic Planning
• R
Information Technology - Management

6. “Algorithm + Data Structures = Program”
– Nicklaus Wirth (Father of Computer Science),
Creator of Pascal Language
“Program Cost: Time vs Space”
CPU Time vs Memory
“Program: Input – Process - Output”
Programming Principles

7. • Dynamic aspect of a program
• Sequence of steps to solve a problem
– Have a problem definition
– Have specific input
– Operate on specific data
structure (problem model)
– Produces output
• Popular algorithm maybe named for
easy communication
• Example:
• Problem: sorting
– Input: array
– Data structure: array
– Output: array
– Algorithm: Bubble Sort, Quick
Sort, Merge Sort, etc
Algorithm and Data Structure
• Static aspect of a program
• Representation of a problem (model)
• The structure / shape of the data (to
be processed)
• Is subject to be processed by an
algorithm
• Common data structures is named
• Example:
• Array
• List
• Map
• Stack
• Queue
• Matrix
• Graph
• etc
ALGORITHM DATA STRUCTURE

8. Algorithm and Data Structure - Example
• Problem: Find shortest path between a
source vertex and destination vertex
• Input:
– Source vertex
– Destination vertex
– Weighted graph
• Output:
– Shortest Path
• Algorithm: Dijkstra Algorithm

9. • Pseudocode: a pseudo programming language to describe algorithm that
will be understandable by a human reader, facilitates communication
• Programming: activity that translates the algorithm in the mind of the
programmer into specific Computer Language
• Program Source Code: for Computer or Human ?
• Human:
– Readibility
– Understandibility
– Clarity
– Structure
– Focus on the semantic (meaning)
• Computer:
– Executes binary instructions (bits)
– Source code is translated into binary instructions (machine language)
– Translation: Focus on the syntactic (what is written)
Pseudocode

10. • Variable
• Data Type
• Operators
• Control Structures
• Subprogram
• Abstract Data Type
Programming Concepts - Basic

11. • A location in memory to store a value
while a program is in execution
• Variable has:
– Name (label)
– Type (allowed values)
– Value
• Operation:
– Declaration
– Initialization
– Assignment (Write)
– Read
Variable

12. • Classify/restrict possible values that can be given to a data (domain values)
• Have a specific operations
• Have a name
• Primitive/Basic Data Types:
– Numeric:
• Integer: byte, short, int, long
• Real/Float: float, double
– Boolean
– Character
• Composite Data Type
– Derived from multiple primitive data types
– Combines into data structures
– Example:
• String
• Collection: Array of T, List of T, Map of <T1,T2>, Set of T
• Struct / Record { T1 f1, T2 f2, …}
• Class
Data Type

13. • Specific operations on a data type
• Arithmetic : Numeric, Numeric -> Numeric
• Ex: addition, subtraction, multiplication, division, modulo
• Relational: Numeric, Numeric -> Boolean
• Ex: less than, less than or equal, greater than, greater than or equal, equal,
not equal
• Logic : Boolean, Boolean -> Boolean
• Ex: not, and, or
• String operations:
• Ex: concat, substring, index of, length, …
Operators

14. • Literal
– Written text that represents a value that have a type
– Integer literal: 1, 1000, -1
– Float literal: 1.001, -0.2, -2E-03
– Boolean literal: true, false
– String literal: “hello”, “Jakarta”, “”
• Expression
– Combination of values that evaluates to a value
– 2+3
– sin(pi)
– x
– x+3
• Statement
– A single program action
• Block
– Combination of several statement considered as a single statement (block statement)
• Recursion
– A subprogram activity that calls itself
Programming Concepts

15. • Mechanism to controls the flow of the program
• Sequence
• Conditional
• Loop
Control Structures

16. • Basic sequential actions flow
statement 1;
statement 2;
statement 3;
…
Control Structures - Sequence

17. • Conditional / choice / selection of actions based on a boolean condition
IF
If condition then
statement;
IF-ELSE
if condition then
statement 1;
else
statement 2;
Control Structures - Conditional

18. • if-else-if-else …. visually forms a decision tree
• Make sure all possibilities have been covered !
Control Structures – Decision Tree

19. • Conditional loop
– while-do
– do-while / repeat-until
• Iteration
– Using a counter variable
– Over a collection (foreach)
• Loop control statement:
– Break statement
– Continue statement
Control Structures – Loop

20. • while-do
– Checks condition at top
– Loop Statement may /may not execute
• do-while
– Checks condition at bottom
– Loop Statement executed at least once
Control Structures – Conditional Loop

21. • for: index style
– Using integer index for loop condition
– While-do in compact form
• for-collection iteration
– Iterates over each element in collection
– No need for index
Control Structures – Iteration

22. • Break
– Used to exit loop immediately
– Application: searching
• Continue
– Used to skip next process and proceed
to next iteration
– Application: filtering
Control Structures – Loop - Break/Continue

23. • A program may divided into subprograms, or use existing subprograms
• Each subprogram perform specific tasks
• A program /sub program may call another sub program
– A call to a subprogram may pass data to that subprogram (parameter, argument)
– A call to a subprogram may return an output
• Main Program: program that initiates the main flow of control
• Subprogram types:
– Procedure: performs tasks, do not return an output
– Function: perform tasks and returns an output
• Parameter/argument types:
– Formal parameter: parameter that’s declared for the subprogram
– Actual parameter: actual value that’s passed to the subprogram
• Signature:
– Combination of input parameter types and output/return types
• double sin(double x) : double -> double
• int add(int x, int y) : int, int -> int
• void hello () : () -> void
Subprogram

24. Data Structures
Engineering Structures
Data Structures
Structure:
• Something arranged in a definite
pattern of organization
• Organization of parts as dominated by
the general character of the whole

25. • Compound Data type that contains multiple data each with the same type
• Array of T:
– Fixed size/length at allocation
– Static: May not grow
– Access: by index (direct access)
• List of T:
– Zero size at allocation
– Dynamic: may grow (add, remove)
– Access: by index (involves loop)
• Map of <T1,T2>:
– Map from an KEY type to VALUE type
– Example: to associate a Student with his Student Number
– Dynamic/may grow: put, remove
– Access: by key (direct access)
• Set of T:
– Dynamic collection of elements from type T
– Order is not guaranteed
– Each element must be unique in the set
Data Structures - Collection

26. • Data structures that represents an abstract concept
– Provide abstraction to a concept
– Provide encapsulation (hide internal implementation details)
– Provide specific operations
• Example:
– Stack: a concept that can be “created” from a array or list (the internal
implementation)
– Stack operations:
• void push (Stack s, T elem)
• T pop (Stack s)
• boolean isEmpty(Stack s)
• T getTop(Stack s)
Data Structures – Abstract Data Type

27. Tanya Jawab
Christian A
Email: coolpie at gmail.com
Q & A