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Introduction to Computer Science and Programming

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Chapter 2: Problem Solving

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Introduction to Computers, Problem Solving, and Programming - PowerPoint PPT Presentation

Introduction to Computers, Problem Solving, and Programming

Introduction to computers, problem solving, and programming chapter 1 1.1 overview of computers computer - a device for counting or computing dr. john atanasoff ... – powerpoint ppt presentation.

• Computer - A device for counting or computing
• Dr. John Atanasoff - 1st computer.
• ENIAC 1946 at U of Penn
• Dr. John Von Neumann - Princeton
• Stored program concept
• Memory rather than wires and switches.
• Computing History
• Von Neumann architecture basis for today's computers
• VLSI Technology made computers affordable, small and available to the public
• How programming has changed over time
• Every computer is organized roughly into six parts
• CPU - central processing unit
• Where decisions are made, computations are performed, and input/output requests are delegated
• Main Memory
• Stores information being processed by the CPU
• Secondary Memory
• Stores data and programs
• Input devices
• Allows people to supply information to computers
• Output devices
• Allows people to receive information from computers
• Network connection
• Modems / Ethernet interface
• Semi permanent data-storage capability
• Tape or Disk
• Secondary memory has much more storage capacity
• Brains of the computer
• Arithmetic calculations are performed using the Arithmetic/Logical Unit or ALU
• Control unit decodes and executes instructions
• Arithmetic operations are performed using binary number system
• Fundamental building block is a switch
• Switches are made from ultrasmall transistors
• The Pentium processor contains about three million transistors
• The Pentium Pro has about 5.5 million transistors
• Accessories that allow computer to perform specific tasks
• Receiving information for processing
• Return the results of processing
• Store information
• Common input and output devices
• Printer Joystick CD-ROM
• Keyboard Monitor
• LAN - Local area network
• Organizational
• WAN - Wide area network
• Introduced 1989
• Developed by CERN
• European Laboratory for Particle Physics
• Web browser
• Application software
• Programs designed to perform specific tasks that are transparent to the user
• System software
• Programs that support the execution and development of other programs
• Two major types
• Operating systems
• Translation systems
• Application software is the software that has made using computers indispensable and popular
• Common application software
• Word processors
• Desktop publishing programs
• Spreadsheets
• Presentation managers
• Drawing programs
• Controls and manages the computing resources
• Important services that an operating system provides
• File system
• Commands that allow for manipulation of the file system
• Ability to perform input and output on a variety of devices
• Management of the running systems
• MSDOS , Windows , Unix
• Machine Language
• Native tongue of the computer
• Binary 0s and 1s that specify what to do
• 0010 0000 0000 0100
• 1000 0000 0000 0101
• 0011 0000 0000 0110
• High - Level Languages
• Resemble human language (C, C, Pascal)
• cost price tax
• Language Standard
• Syntax (grammatical form) (rules)
• programs used without modification
• Source Program
• Object Program
• Executable Program
• OOP derived from C
• Bjarne Stroustrup
• Popular because of reuse
• Organized in a Hierarchy
• Super Classes
• Sub Classes
• Object-oriented design and programming supports good software engineering
• Object-oriented design promotes thinking about software in a way that models the real world
• Algorithms are the basis for the procedural sections (Structured Programs)
• Highly structured
• Top-down design
• Step-wise refinement
• Abstraction
• Extract the relevant properties of an object while ignoring inessential details
• Encapsulation
• Breaking down an object into parts, hiding and protecting its essential information, and supplying an interface to modify the information in a controlled and useful manner
• Dividing an object into smaller pieces or modules such that the object is easier to understand and manipulate
• Ranking or ordering of objects based on some relationship between them
• Process of extracting only the relevant properties of an object
• Extracted properties define a view of the object
• Car dealer views a car from selling features standpoint
• Price, warranty, color, etc.
• Mechanic views a car from systems maintenance standpoint
• Oil, oil filter, spark plugs, etc.
• By hiding the information its representation and content can be changed without affecting other parts of the system
• Example - car radio
• Controlled by switches and knobs
• The details of how it works is hidden
• Dividing an object so that the object holds useful information and it is easier to understand
• Most complex systems are modular
• Cooling System
• Ignition System
• Fuel System
• Example - Automobile can be decomposed into subsystems
• Cooling system
• Ignition system
• Spark plugs
• Hierarchies facilitate understanding complex organizations and systems
• Example - a company hierarchy helps employees understand the structure of their company and their positions
• Later we will use data abstractions
• Will apply OO techniques
• Structured Programming
• Object Oriented Programming
• Model our own objects or abstractions
• Set of programs used to develop software
• A key component of a is a translator
• Types of translators
• g, Borland C , Microsoft Visual C
• Editor used to enter the program
• Source program (file.cpp)
• UNIX vi text editor
• Compiler translates the source program
• Displays syntax errors (not descriptive)
• Linker/Loader to combine object file with other object files
• Executable program
• Major activities
• Linking with pre-compiled files
• Object files
• Library modules
• Loading and executing
• Viewing the behavior of the program
• Problem Analysis - (Correct Problem)
• Identify data objects
• Goal to model properties
• Determine Input / Output data
• Constraints on the problem
• Decompose into smaller problems
• Top-down design (divide and conquer)
• Develop Algorithm (Desk check)
• Implementation
• Writing the algorithm
• Verify the program meets requirements
• System and Unit test
• Documentation
• Key part in the development process
• Software engineering
• Area of computer science concerned with building large software systems
• Tremendous advances in hardware have not been accompanied by comparable advances in software
• Reliability
• An unreliable life-critical system can be fatal
• Understandability
• Future development becomes very difficult if software is hard to understand
• Cost Effectiveness
• Cost to develop and maintain should not exceed profit
• Adaptability
• System that is adaptive is easier to alter and expand
• Reusability
• Improves reliability and maintainability, and reduces development costs
• Case Study Converting Miles to Kilometers
• Problem  Your summer surveying job requires you to study some maps that give distances in kilometers and some that use miles. You and your coworkers prefer to deal in metric measurements. Write a program that performs the necessary conversion.
• Analysis  The first step in solving this problem is to determine what you are asked to do. You must convert from one system of measurement to another, but are you supposed to convert from kilometers to miles, or vice versa? The problem states that you prefer to deal in metric measurements, so you must convert distance measurements in miles to kilometers.
• Design  The next step is to formulate the algorithm that solves the problem. Begin by listing the three major steps, or sub problems, of the algorithm.
• Implementation  To implement the solution, you must write the algorithm as a C program.
• Testing  How do you know the sample run is correct?
• Privacy and Misuse of Data
• Computer Hacking
• Plagiarism and Software Piracy
• Misuse of a Computer Resource

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ITB001 Problem Solving and Programming Lecture 8

Mar 17, 2019

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ITB001 Problem Solving and Programming Lecture 8. Faculty of Information Technology Queensland University of Technology. Aims of this lecture. This lecture further illustrates the notion of data abstraction with three important examples, tables , trees and tagged data

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ITB001Problem Solving and ProgrammingLecture 8 Faculty of Information Technology Queensland University of Technology

Aims of this lecture • This lecture further illustrates the notion of data abstraction with three important examples, tables, trees and tagged data • Tables introduce some of the basic operations needed to maintain databases • Trees are a commonly-used recursive data structure for sorting data • Tagged data items allow different representations of data values to coexist

References • Concrete Abstractions: An Introduction to Computer Science Using Scheme, page 532 and Sections 8.1, 8.2 and 9.3 • Structure and Interpretation of Computer Programs, Sections 2.2.2, 2.3.3 and 2.4.2

Data abstractioncase study 1: Tables

The notion of tables • The idea of a table of values is a familiar way of associating a ‘key’ with a particular value • For example, the following table associates names (the keys) with heights (in metres)

The notion of tables • Given such a table we can look up the values to discover, for instance, that Harpo’s height is 1.62m • However, we cannot use this table to learn Zeppo’s height because key ‘Zeppo’ does not appear in the table • The particular table shown above is unsorted, although we could choose to order the table by key

A simple data representation for tables • An easy way to represent a table is as an association list, i.e., a list of key-value pairs • An advantage of using lists is that we can easily increase the size of the table • Association lists are not a very efficient data structure for large databases, but are suitable for maintaining small tables

A simple data representation for tables • For example, the table of heights above could be represented in Scheme as the following nested list of strings and numbers: • (list (list "Groucho" 1.57) • (list "Harpo" 1.62) • (list "Chico" 1.61)) • However, as usual, we don’t want users of tables to be aware of this underlying data representation • Preventing this allows us to change the represent-ation to a more efficient one such as a ‘hash table’

Exercise 8-1: Some basicoperations on tables • Define the following procedures for tables, using association lists as the data representation • (make-table) is a parameterless constructor that returns an empty table • (add-to-table kvt) is an operation that returns table t with a new association between key k and value v (overriding any previous association) • (lookup-table kt) is a selector that returns the value in table t associated with key k, or returns string "ERROR" if there is no such key in the table

Exercise 8-1: Some basicoperations on tables • Example: • (define heights • (add-to-table "Chico" 1.61 • (add-to-table "Harpo" 1.62 • (add-to-table "Groucho" 1.57 • (make-table))))) • (lookup-table "Harpo" heights) returns 1.62 • (lookup-table "Zeppo" heights) returns "ERROR"

Exercise 8-1: Some basicoperations on tables • Example: • (define heights • (add-to-table "Zeppo" 1.59 heights)) • (lookup-table "Zeppo" heights) returns 1.59 • (define heights • (add-to-table "Harpo" 1.58 heights)) • (lookup-table "Harpo" heights) returns 1.58

Exercise 8-2: Removingassociations from a table • So far we have been able to make our tables grow larger only • Define a procedure (remove-from-table kt) which returns table t with any association for key k removed • If key k did not appear in table t then remove-from-table should just return table t unchanged • If key k appears more than once in table t then all such associations should be removed

Exercise 8-2: Removingassociations from a table • Example: • (define heights • (add-to-table "Chico" 1.61 • (add-to-table "Harpo" 1.62 • (add-to-table "Groucho" 1.57 • (make-table))))) • (lookup-table "Harpo" heights) returns 1.62

Exercise 8-2: Removingassociations from a table • (define heights • (remove-from-table "Harpo" heights)) • (lookup-table "Harpo" heights) returns "ERROR"

Exercise 8-3: Maintaining a sorted table • For efficient searching we may want to keep entries in the table sorted by key • We can do this by defining a version of procedure add-to-table that always puts associations in the right place • However, to know what the ‘right’ place is we need to know what criterion is used to sort keys • Define a new procedure (add-to-table kvpt) which accepts a predicate p that can be applied to two keys to determine their order

Exercise 8-3: Maintaining a sorted table • Procedure add-to-table should add the new association to the table in a position such that the key of the preceding association (if any) is ‘less than’ key k, according to predicate p, and the key of the succeeding association (if any) is ‘greater than’ key k, according to predicate p • If table t already contained an association for key k the old association should be removed from the table • You can assume that table t is already sorted and that key k occurs at most once in table t

Exercise 8-3: Maintaining a sorted table • Example: • (add-to-table "Chico" 1.61 string<? • (add-to-table "Harpo" 1.62 string<? • (add-to-table "Groucho" 1.57 string<? • (make-table)))) • orders associations by predicate string<? and represents the table as • (list (list "Chico" 1.61) • (list "Groucho" 1.57) • (list "Harpo" 1.62))

Exercise 8-3: Maintaining a sorted table • Comment: To explain the example above we broke the data abstraction barrier for tables • A user of tables should not directly examine (or even care about) their representation

Data abstraction barriersin the tables case study • With these operations on tables in place it is now possible to use tables without even knowing that they are implemented using association lists • This would allow us to change the representation of tables, or otherwise modify the operations on them, without affecting users of tables

Data abstraction barriersin the tables case study Programs that use tables Tables in the problem domain lookup-table add-to-table … Tables as association lists cons first rest … Scheme’s implementation of lists

Data abstraction casestudy 2: Binary trees

The importance of trees • Trees are one of the most important data structures in computing and are used to solve a wide variety of data structuring, sorting and searching problems • Like lists, they are an example of a recursively-defined data structure • Indeed, we will build our data abstraction for trees on top of that for lists

An obvious example of a tree is the directory structure of your computer Each folder is the root of a tree with branches containing other folders and files Each file is a leaf that ends a branch A common example A B 1 2 3 C D 4 5

A conventional notation • Traditionally, trees are drawn as directed graphs which ‘grow’ downwards • Thus the root is at the top and the leaves are at the bottom A C B 1 2 3 D 5 4

Binary trees • In general a node within a tree may have any number of branches • In the preceding example node B had three branches • However, an important special case is that of binary trees whose nodes have at most two branches • Binary trees are commonly used for storing values in a particular order so that they can be retrieved quickly

An ordered binary tree is one in which, for all nodes, all values in its left-hand branch are ‘less than’ the root value and all values in its right-hand branch are ‘greater than’ the root value The tree on the right is ordered by the ‘<’ relationship for numbers Ordered binary trees 4 2 5 1 3

A data structure forrepresenting binary trees • In Scheme we can represent trees easily using nested lists • Each node in the tree can be represented as a list of length three, containing the node’s value and the left and right branches • If the tree is a leaf then both the branches will be the empty tree • The empty tree can be represented by the empty list

A data structure forrepresenting binary trees • Example: The following list represents the ordered binary tree shown on the right • (list 4 • (list 2 • (list 1 empty empty) • (list 3 empty empty)) • (list 5 empty empty)) 4 2 5 1 3

A data structure forrepresenting binary trees • Example: The following binary tree is unordered • (list 7 • (list 9 • (list 6 empty empty) • (list 3 • (list 4 empty empty) • empty))) • (list 0 • (list 8 • empty • (list 2 empty empty)) • (list 5 empty empty))) 7 9 0 3 8 5 6 4 2

A data abstraction for binary trees • However, representing trees using lists in this way exposes the underlying data structure • Instead, we should define meaningful constructors, selectors and other operations on binary trees that hide the way we have chosen to implement trees • We can represent the empty tree by a special value: (define stump empty)

A data abstraction for binary trees • The following two constructors are convenient for creating a tree with two branches and a tree with no branches, respectively: (define [root root-value left-branch right-branch] (list root-value left-branch right-branch)) (define [leaf leaf-value] (root leaf-value stump stump))

A data abstraction for binary trees • The following three selectors for extracting parts of a given tree are defined easily as synonyms of the corresponding list operations: (define root-value first) (define left-branch second) (define right-branch third)

A data abstraction for binary trees • The following two predicates are helpful when traversing trees: (define stump? empty?) (define [leaf? tree] (and (not (stump? tree)) (stump? (left-branch tree)) (stump? (right-branch tree)))

A data abstraction for binary trees • Using these constructors we can define a binary tree in a more readable way: • (root 4 • (root 2 • (leaf 1) • (leaf 3)) • (leaf 5)) 4 2 5 1 3

A data abstraction for binary trees • Similarly in this more complicated case: • (root 7 • (root 9 • (leaf 6) • (root 3 • (leaf 4) • stump))) • (root 0 • (root 8 • stump • (leaf 2)) • (leaf 5))) 7 9 0 3 8 5 6 4 2

Counting leaves • Given this representation we can easily write a recursive procedure for counting the leaves in a tree: (define [count-leaves tree] (cond [(stump? tree) 0] [(leaf? tree) 1] [else ; node is the root of a sub-tree (+ (count-leaves (left-branch tree)) (count-leaves (right-branch tree)))] ))

Counting leaves • Procedure count-leaves has two base cases: • One for a leaf • One for an empty tree (‘stump’) • It also makes two recursive references to itself: • One for the left branch of a sub-tree • One for the right branch of a sub-tree

The computational processfor counting leaves (count-leaves (root 9 (root 5 (leaf 8) stump) (root 3 (leaf 2) (leaf 4)))) = (+ (+ 1 0) (+ 1 1)) = 3 (+ 9 (+ 5 (+ 3 0) 8 2 4 1 1 1))

Exercise 8-4: Depth of a binary tree • We define the ‘depth’ of a tree as the length of the longest path from the root to a leaf • In the case of sorted trees, this tells us how many steps will be required to find an item in the worst case • Define a procedure tree-depth which returns the maximum depth of a given binary tree

Exercise 8-4: Depth of a binary tree • Examples: • (tree-depth stump) returns 0 • (tree-depth (leaf 8)) returns 1 • (tree-depth (root 9 • (root 5 • (leaf 2) • (leaf 6)) • (leaf 4))) returns 3

Exercise 8-4: Depth of a binary tree • (tree-depth (root "a" • (leaf "z") • stump)) returns 2

Exercise 8-5: Binary tree predicate • We have seen that Scheme provides numerous predicates for checking to see what the type of a given value is, e.g., number?, list?, string?, etc • Having introduced a notion of binary trees we should similarly define a predicate for checking to see whether a given data structure is a properly-formed tree • In addition, we will check that the tree is homogeneous in the sense that all of its leaves are of the same type

Exercise 8-5: Binary tree predicate • Define a higher-order procedure check-tree which accepts a type predicate (such as number?, string?, etc) • Procedure check-tree should return a predicate (a Boolean-valued procedure) which accepts a data structure and returns #t (logical true) only if the structure is a well-formed binary tree and all the leaves are of the right type

Exercise 8-5: Binary tree predicate • Example: • (define tree-of-strings? • (check-tree string?)) returns a procedure which tests to see if a given data structure is a binary tree containing only strings • (tree-of-strings? • (root "a" • (leaf "b") • (root "c" stump • (leaf "d")))) returns #t

Exercise 8-5: Binary tree predicate • (tree-of-strings? • (root 1 (leaf 2) • (leaf 3))) returns #f because the values in the tree are not strings • (tree-of-strings? • (list "a" • (list "b") • (list empty (list "c")))) returns #f because the data structure is not a properly-formed binary tree

Manipulating the values in a tree • Given a tree of numbers, the following procedure multiplies all the values by a given factor: (define [scale-tree numbers factor] (if (stump? numbers) stump (root (* factor (root-value numbers)) (scale-tree (left-branch numbers) factor) (scale-tree (right-branch numbers) factor))))

Manipulating the leaves of a tree • Example: • (scale-tree • (root 4 (root 2 (leaf 1) (leaf 3)) • (leaf 5)) • 10) • returns • (root 40 (root 20 (leaf 10) (leaf 30)) • (leaf 50)) • Thought exercise: Why didn’t procedure scale-tree need to treat leaves as a special case?

Cloning trees • Like count-leaves, the scale-tree procedure ‘walks’ through the given tree by following the branches • However, rather than returning a single value, it returns a whole tree • Importantly, the tree that is returned is built with tree constructor root and special value stump • The returned tree is not the same tree provided as an argument to procedure scale-tree • It is a new tree with the same ‘shape’ as the original one

Exercise 8-6: Largest number in a tree • Define a procedure tree-max which returns the largest value in a (not necessarily sorted) binary tree of non-negative numbers (or 1 if the tree is just a stump) • Examples: • (tree-max stump) returns -1 • (tree-max • (root 6 • (root 7 (leaf 1) (leaf 8)) • (leaf 2))) returns 8

Exercise 8-6: Largest number in a tree • (tree-max • (root 4 • (root 8 • (root 5 (leaf 7) stump) • (leaf 2)) • (root 5 (leaf 9) stump))) • returns 9

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2. ROI – Return on Investment

Return on Investment is a metric that measures the profitability of an investment, telling you how much profit or loss you have earned.

3. KPI – Key Performance Indicator

KPIs are metrics that showcase how well your company is performing in a certain area. They can be used internally or for clients, to demonstrate the impact of a company’s provided services.

4. CRM – Customer Relationship Management

Customer relationship management (CRM) refers to technology or processed used to manage, track, and analyze your company’s relationships with customers and potential customers.

5. OOO – Out of Office

Out of Office (OOO) is an acronym used for employees who are not in the office, whether they are sick, on leave, or taking a vacation.

6. COB – Close of Business

Close of business refers to the end of the business day, which is usually at 5PM.

7. SOP – Standard Operating Procedure

A standard operating procedure (SOP) provides established protocols and step-by-step instructions to ensure consistency and efficiency in executing routine tasks and operations.

8. API – Application Programming Interface

An application programming interface (API) is a software that serves a distinct function between different software applications, allowing seamless data exchange and integration, thereby enhancing functionality and user experience.

9. SME – Subject Matter Experts

SMEs, or subject matter experts, possess specialized knowledge and expertise, serving as invaluable resources for guidance and problem-solving.

10. SMB – Small and Medium-sized Business

SMB, or small and medium sized business, refers to companies and enterprises that are smaller in size and revenue than large corporations, but not as small as microbusinesses.

11. CTA – Call to Action

CTA prompts users to take specific actions, such as making a purchase or subscribing to a service, enhancing engagement and conversion rates in marketing campaigns.

12. B2B – Business to Business

Business to Business (B2B) transactions involve commerce conducted between businesses, encompassing sales, partnerships, and collaborations tailored to meet the needs of corporate clients.

13. B2C – Business to Consumer

B2C transactions involve direct commerce between businesses and individual consumers, encompassing retail sales and service provision to end-users.

14. Admin – Administration

Admin pertains to administrative tasks and responsibilities, encompassing a wide range of duties aimed at ensuring the smooth functioning of organizational processes.

15. HR – Human Resources

Human Resources (HR) oversees personnel management and workforce development initiatives, encompassing recruitment, training, employee relations, and policy implementation.

Learning corporate and professional acronyms and abbreviations make it easier transitioning into a professional role. You can effectively communicate and comprehend colleagues, while effectively managing time and improving operational efficiency. For more ways to improve communication in the workplace, like learning how to craft work emails and meeting agendas , learn more writing tips .

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