U6

• Abstraction

  Ignoring unnecessary information and focusing only on the important facts
• Abstraction


  • Simplifies a problem to make it less complex
  • Makes the problem more straightforward to understand and create a solution
• Decomposition


  Breaking a problem down into smaller tasks so that it is easier to solve
• Decomposition


  • Each individual problem can be separately tested and solved
  • Enables different people to work on the different parts of a larger problem that can later be recombined to produce a full solution
• Algorithmic thinking

  Following logical steps to solve the problem
• Algorithmic thinking

  1. Break down the problem using decomposition into smaller problems

  2. Consider the required data and relevant data structures using abstraction

  3. Follow logical steps to solve the problem
• Algorithm

  A set of instructions, presented in a logical sequence
• Pseudocode


  A method of defining algorithms
• Flowcharts

  A method of defining algorithms
• OCR Exam Reference Language

  The language used in OCR exams to represent algorithms
• Algorithm design

  Programmers create algorithm designs as a method of planning a program before writing any code. This helps them to consider the potential problems of the program and makes it easier to start creating source code.
• Basic commands in OCR Exam Reference Language

  • Annotation
  • Assignment
  • Constants and Global Variables
  • Input / Output
  • Casting
  • Random Number
• Selection (if-then-else)

  if firstname == "Steven" then

  print("Hello" + firstname)

  elif firstname == "Steve" then

  print("Please use full name")

  else

  print("Who are you?")

  end if
• Selection (case select)

  switch day:

  case "Sat":

  print("It is Saturday")

  case "Sun":

  print("It is Sunday")

  default:

  print("It is a Weekday")

  endswitch
• Iteration (for loop)


  for i = 1 to 10 step 1

  input item

  next i
• Iteration (while loop)


  while firstname != "Steven"

  firstname = input("Try again:")

  endwhile
• Iteration (do while loop)

  do

  firstname = input("Guess name:")

  until firstname == "Steven"
• String Handling

  Operations that can be performed on strings, such as finding the length, extracting substrings, concatenation, and converting between upper and lower case
• ASCII Conversion


  Converting between character and ASCII code
• File Handling - Reading Lines

  file1 = open("Customers.txt")

  while NOT file1.endOfFile()

  print(file1.readLine())

  endwhile

  file1.close()
• File Handling - Writing to a (New) File

  newFile("paint.txt")

  file2 = open("paint.txt")

  paint = input("Enter a paint colour:")

  file.writeLine(paint)

  file2.close()
• Arrays


  Declaring and assigning values to 1D and 2D arrays
• Flowcharts

  • A flowchart can be used to visually represent an algorithm. It is more likely you will need to be able to interpret a flowchart rather than draw one.
• Flowchart symbols

  • Algorithm
  • Input/Output
  • Process
  • Decision
  • Connector
  • Terminator
• Flowchart algorithm


  Flowchart diagram representing the same algorithm as the pseudocode
• Trace tables

  Used to track the value of variables as a program is run. Each row in the trace table represents another iteration, and each column stores the value of a variable as it changes.
• Decomposition


  Decomposition allows large teams to each take a part of a problem and work on it.

  Decomposition allows seemingly impossible problems to be solved by splitting them into simple tasks.
• Structure diagrams

  • Structure charts are used to visually represent breaking a large problem down into the smaller parts that make it up.
  • Each box represents a smaller problem to be solved.
  • Lines show which bigger problem the box is a part of.
• Linear search


  • The most simple search algorithm
  • Each data item is searched in order from the first value to the last as if they were all laid out in a line.
  • The list does not have to be in any order before it is searched.
  • Also known as a sequential search.
• For large lists, linear search is not very efficient
• Binary search

  • A much more efficient searching algorithm
  • searches through fewer data and is often much quicker, especially for large data sets.
  • The list of data must already be sorted in order before a binary search can take place.
• Merge sort


  • A sorting algorithm based on the idea of 'divide and conquer'.
  • Divides a list into half, again and again until each data item is separate.
  • Then the items are combined in the same way as they were divided, but now in the correct order.
  • When the individual lists are all merged together as one list again, then the data is in order and the algorithm will end.
• Bubble sort

  • An algorithm based on the comparison of adjacent data elements.
  • Data elements are swapped if they are not in the correct order.
  • Not suitable for large sets of data.
• Insertion sort

  • The list is logically split into sorted values (on the left) and unsorted values (on the right).
  • Starting from the left, values from the unsorted part are checked and inserted at the correct position in the sorted part.
  • This continues through all elements of the list until the last item is reached, and sorted.
• Insertion sorts are efficient for small data sets but would be slow to sort large sets, compared to alternatives such as a merge sort
• Algorithmic thinking


  A way of getting to a solution by identifying the individual steps that are needed, creating a set of rules called an algorithm that if followed precisely leads to an answer
• Algorithmic thinking


  Allows solutions to be automated
• Algorithmic thinking involves breaking down a problem, identifying the important data, and creating a step-by-step solution that can be automated