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EDUQAS Computer Science: Component 1 > 1.3 > Flashcards

1.3 Flashcards

1
Q

Algorithm

A

A set of instructions designed to solve a problem

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2
Q

Identifier

A

A name given to an element of a program so it can be referred to throughout

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3
Q

Good Programming Practice

A
  • Meaningful names, clear what variable is holding
  • Annotation, easier for programmers to follow
  • Program Layout, see each function and aids readability
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4
Q

Variable

A

An attribute within a program whose value can be reassigned (during execution)

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5
Q

Constant

A

An attribute within a program which never changes its value (during execution)

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6
Q

Global Variable

A
  • Declared at the top of the program
  • Available to the whole program
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7
Q

Local Variable

A
  • Declared inside a subroutine
  • Available only to that subroutine
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8
Q

Sequence

A

Steps processed once

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9
Q

Selection

A
  • Uses a logical condition to determine which line of code to process next
  • Purpose: to execute code if a certain condition is met
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10
Q

Nesting

A

When another selection statement is contained within an existing selection statement

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11
Q

Repetition

A

Purpose: to repeatedly execute code until a certain condition is met

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12
Q

Count

A

A value in a loop that will increase/decrease each time the loop is executed

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13
Q

Rogue Value

A

a value which tells the computer that input has finished

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14
Q

DIV

A

//

How many times one number goes into another

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15
Q

MOD

A

%

Remainder after DIV

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16
Q

Lossy Compression

A
  • Cannot be decompressed to yield back the original data
  • Not Good method for critical data such as text
  • Good for digitally sampled analogue data e.g. Sound/Video/Graphics
  • Algorithms vary but many use threshold truncation
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17
Q

Lossless Compression

A
  • Original message can be decompressed back to it’s original form
  • Good for text
  • Good for high quality media e.g. .FLAC .PNG
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18
Q

Character Combinations (Compression)

A

Finding frequently occurring character combinations and replace them with tokens

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19
Q

Run Length Encoding

A

Replace long strings with a number

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20
Q

Photo Compression

A

Approximate all shades of blue in a photograph to one shade and only store this colour instead of every pixel

21
Q

Sub Sections

A
  • Algorithms and programs can be broken down into smaller parts.
  • These are named, reusable pieces of code that can be called any number of times within an algorithm to perform a specific task.
22
Q

Sub Sections Advantages

A
  • Can be developed by teams, easier to write.
  • Code is more readable
  • Avoids duplication of code.
  • Sections can be tested individually.
23
Q

Standard Module and Function

A

STANDARD FUNCTION
Carries out a common task for a standard situation in a program and is provided by the compiler/interpreter

STANDARD MODULE
Contains a group of related standard functions

EG: Module = math
Function = math.cos(x)

24
Q

Standard Module Advantages

A
  • Pre-written / No need to write again (Saves Development Time)
  • High Quality as written by experts
  • Already Tested/Used – Less Errors
25
Q

User defined sub functions

A

a section of code (method) which is written for a specific purpose by the programmer

26
Q

Parameter

A

a value which can be passed to a procedure or function

27
Q

Reference Parameter

A

the memory address of the original data is passed to the procedure

  • The (original) data can be altered
  • A list, object or any non-primitive data type
28
Q

Value Parameters

A

a copy of the data is passed into the procedure

  • Useful in calculations (not change original data)
  • Avoids unwanted side – Prevents the original value being changed by mistake
29
Q

Linear Search

A
  • Used on an unordered (or ordered) array

METHOD

  • Cycle through each element in turn
  • Compare current element with SearchValue until a match is found or we get to the end of the list
30
Q

Dry Run

A

A dry run can be used to determine what a section of unknown code does.

31
Q

Binary Search

A
  • Used for searching an ordered array

METHOD

  • Calculate mid element in the array
  • Compare SV with the middle element
  • If SV is not found, search lower/upper half
  • Repeat until found (or not present in array)
32
Q

Bubble Sort

A
  • A pass through the data comparing each value with the following one and swapping values if necessary
  • A number of passes is made until the data is in order
33
Q

Insertion Sort

A
  • Whilst being processed, the array is effectively split into sorted/unsorted
  • Items are copied one by one (from unsorted part of the array)
  • Each new item is inserted in the correct place in the output (sorted part of the array)
34
Q

Why Psuedocode

A
  • Language independent
  • Easy to read/understand

Translatable into any programming language

35
Q

Logical Error

A

A bug in a program that causes it to operate incorrectly, but not to terminate abnormally

  • produces unintended or undesired output or other behaviour, although it may not immediately be recognised as such
36
Q

Recursive Algorithm

A
  • Calls itself (with different input values)
    until the terminating condition is met.
  • Values are placed onto the stack
  • Complicated to program and debug
  • Uses more memory, if not terminated could use all stack space causing the program to crash
  • Can be a more elegant solution to a problem e.g. factorial
  • Usually less lines of code.
37
Q

Recursion Potential Problems

A
  • Difficult to program (effectively)
  • Memory overheads of stack use with many recursive calls
  • Infinite Loop (Run out of memory)
  • Stack Overflow Exception risk
  • Difficult to debug if there is a fault
38
Q

Quick Sort

A
  • Quicksort is a recursive sorting algorithm

METHOD

  • Select a pivot value
  • Re-order the data, producing 2 sub-lists: one which contains items smaller than the pivot, one which contains items greater than the pivot
  • Perform the same action on the sub-lists (recursively)
    until each sub-list has only one element
  • Rebuild the List
39
Q

Big O

A

An approximation to compare algorithm efficiency

  • Constants are dropped
  • As n tends towards infinity, the constant will become insignificant
  • The biggest value of O will always override all other values
40
Q

Big O, 1 loop through all items

A

O(n)

41
Q

Big O, 2 nested for loop

A

O(n^2)

42
Q

Big O Linear Search

A

O(n)

The time to complete grows linearly in comparison to the size of the data.

43
Q

Big O Binary Search

A

O(log n)

The time to complete initially grows in a linear way when compared to the size of the data. As the data size grows larger the time taken starts to flatten off.

44
Q

Big O Quick Sort

A

O(n log n)

45
Q

Big O Bubble Sort/ Insertion Sort

A

O(n^2)

46
Q

Big O Fibonnacci

A

O(2^n)

47
Q

Big O Factorial

A

O(n!)

48
Q

is cato cool

A

yes

EDUQAS Computer Science: Component 1 (9 decks)

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