01 ds and algorithm session_01
- 1. Data Structures and Algorithms Rationale Computer science is a field of study that deals with solving a variety of problems by using computers. To solve a given problem by using computers, you need to design an algorithm for it. Multiple algorithms can be designed to solve a particular problem. An algorithm that provides the maximum efficiency should be used for solving the problem. The efficiency of an algorithm can be improved by using an appropriate data structure. Data structures help in creating programs that are simple, reusable, and easy to maintain. This module will enable a learner to select and implement an appropriate data structure and algorithm to solve a given programming problem. Ver. 1.0 Session 1
- 2. Data Structures and Algorithms Objectives In this session, you will learn to: Explain the role of data structures and algorithms in problem solving through computers Identify techniques to design algorithms and measure their efficiency Ver. 1.0 Session 1
- 3. Data Structures and Algorithms Role of Algorithms and Data Structures in Problem Solving Problem solving is an essential part of every scientific discipline. Computers are widely being used to solve problems pertaining to various domains, such as, banking, commerce, medicine, manufacturing, and transport. To solve a given problem by using a computer, you need to write a program for it. A program consists of two components, algorithm and data structure. Ver. 1.0 Session 1
- 4. Data Structures and Algorithms Role of Algorithms The word algorithm is derived from the name of the Persian mathematician Al Khwarizmi. An algorithm can be defined as a step-by-step procedure for solving a problem. An algorithm helps the user arrive at the correct result in a finite number of steps. Ver. 1.0 Session 1
- 5. Data Structures and Algorithms Role of Algorithms (Contd.) An algorithm has five important properties: Finiteness Definiteness Input Output Effectiveness Ver. 1.0 Session 1
- 6. Data Structures and Algorithms Role of Algorithms (Contd.) A problem can be solved using a computer only if an algorithm can be written for it. In addition, algorithms provide the following benefits: Help in writing the corresponding program Help in dividing difficult problems into a series of small solvable problems Make decision making a more rational process Help make the process consistent and reliable Ver. 1.0 Session 1
- 7. Data Structures and Algorithms Role of Data Structures Different algorithms can be used to solve the same problem. Some algorithms may solve the problem more efficiently than the others. An algorithm that provides the maximum efficiency should be used to solve a problem. One of the basic techniques for improving the efficiency of algorithms is to use an appropriate data structure. Data structure is defined as a way of organizing the various data elements in memory with respect to each other. Ver. 1.0 Session 1
- 8. Data Structures and Algorithms Role of Data Structures (Contd.) Data can be organized in many different ways. Therefore, you can create as many data structures as you want. Some data structures that have proved useful over the years are: Arrays Linked Lists Stacks Queues Trees Graphs Ver. 1.0 Session 1
- 9. Data Structures and Algorithms Role of Data Structures (Contd.) Use of an appropriate data structure, helps improve the efficiency of a program. The use of appropriate data structures also allows you to overcome some other programming challenges, such as: Simplifying complex problems Creating standard, reusable code components Creating programs that are easy to understand and maintain Ver. 1.0 Session 1
- 10. Data Structures and Algorithms Types of Data Structures Data structures can be classified under the following two categories: – Static: Example – Array – Dynamic: Example – Linked List Ver. 1.0 Session 1
- 11. Data Structures and Algorithms Just a minute An array is a ___________ data structure, and a linked list is a ____________ data structure. Answer: static, dynamic Ver. 1.0 Session 1
- 12. Data Structures and Algorithms Identifying Techniques for Designing Algorithms Two commonly used techniques for designing algorithms are: Divide and conquer approach Greedy approach Ver. 1.0 Session 1
- 13. Data Structures and Algorithms Identifying Techniques for Designing Algorithms (Contd.) Divide and conquer is a powerful approach for solving conceptually difficult problems. Divide and conquer approach requires you to find a way of: Breaking the problem into sub problems Solving the trivial cases Combining the solutions to the sub problems to solve the original problem Ver. 1.0 Session 1
- 14. Data Structures and Algorithms Identifying Techniques for Designing Algorithms (Contd.) Algorithms based on greedy approach are used for solving optimization problems, where you need to maximize profits or minimize costs under a given set of conditions. Some examples of optimization problems are: Finding the shortest distance from an originating city to a set of destination cities, given the distances between the pairs of cities. Finding the minimum number of currency notes required for an amount, where an arbitrary number of notes for each denomination are available. Selecting items with maximum value from a given set of items, where the total weight of the selected items cannot exceed a given value. Ver. 1.0 Session 1
- 15. Data Structures and Algorithms Just a minute The ___________ technique involves selecting the best available option at each step. Answer: Greedy Ver. 1.0 Session 1
- 16. Data Structures and Algorithms Designing Algorithms Using Recursion Recursion: Refers to the technique of defining a process in terms of itself Is used to solve complex programming problems that are repetitive in nature Is implemented in a program by using a recursive procedure or function. A recursive procedure or function is a function that invokes itself Is useful in writing clear, short, and simple programs Ver. 1.0 Session 1
- 17. Data Structures and Algorithms Just a minute Identify the problem in the following algorithm that attempts to find the sum of the first n natural numbers: Algorithm: Sum (n) 1. s = n + Sum(n – 1) 2. Return (s) Answer: There is no terminating condition in the given recursive algorithm. Therefore, it will call itself infinitely. The correct algorithm would be: 1. If (n = 1) Return(1) 2. s = n + Sum(n – 1) 3. Return(s) Ver. 1.0 Session 1
- 18. Data Structures and Algorithms Determining the Efficiency of an Algorithm Factors that affect the efficiency of a program include: – Speed of the machine – Compiler – Operating system – Programming language – Size of the input In addition to these factors, the way data of a program is organized, and the algorithm used to solve the problem also has a significant impact on the efficiency of a program. Ver. 1.0 Session 1
- 19. Data Structures and Algorithms Determining the Efficiency of an Algorithm (Contd.) The efficiency of an algorithm can be computed by determining the amount of resources it consumes. The primary resources that an algorithm consumes are: – Time: The CPU time required to execute the algorithm. – Space: The amount of memory used by the algorithm for its execution. The lesser resources an algorithm consumes, the more efficient it is. Ver. 1.0 Session 1
- 20. Data Structures and Algorithms Time/Space Tradeoff Time/Space Tradeoff: It refers to a situation where you can reduce the use of memory at the cost of slower program execution, or reduce the running time at the cost of increased memory usage. Example is data storage in compressed/uncompressed form. Memory is extensible, but time is not. Therefore, time considerations generally override memory considerations. Ver. 1.0 Session 1
- 21. Data Structures and Algorithms Method for Determining Efficiency To measure the time efficiency of an algorithm, you can write a program based on the algorithm, execute it, and measure the time it takes to run. The execution time that you measure in this case would depend on a number of factors such as: Speed of the machine Compiler Operating system Programming language Input data However, we would like to determine how the execution time is affected by the nature of the algorithm. Ver. 1.0 Session 1
- 22. Data Structures and Algorithms Method for Determining Efficiency (Contd.) The execution time of an algorithm is directly proportional to the number of key comparisons involved in the algorithm and is a function of n, where n is the size of the input data. The rate at which the running time of an algorithm increases as a result of an increase in the volume of input data is called the order of growth of the algorithm. The order of growth of an algorithm is defined by using the big O notation. The big O notation has been accepted as a fundamental technique for describing the efficiency of an algorithm. Ver. 1.0 Session 1
- 23. Data Structures and Algorithms Method for Determining Efficiency (Contd.) The different orders of growth and their corresponding big O notations are: – Constant - O(1) – Logarithmic - O(log n) – Linear - O(n) – Loglinear - O(n log n) – Quadratic - O(n2) – Cubic - O(n3) – Exponential - O(2n), O(10n) Ver. 1.0 Session 1
- 24. Data Structures and Algorithms Selecting an Efficient Algorithm According to their orders of growth, the big O notations can be arranged in an increasing order as: 2 3 n O(1) < O(log n) < O(n) < O(n log n) < O(n ) < O(n ) < O(2 ) n < O(10 ) Graphs depicting orders of growth for various big O notations: Microsoft Word Document Ver. 1.0 Session 1
- 25. Data Structures and Algorithms Group Discussion: Dependence of Efficiency on Selected Algorithm Problem Statement: You need to write an algorithm to search for a given word in a dictionary. Discuss how different algorithms and different ways of organizing the dictionary data affect the efficiency of the process. Ver. 1.0 Session 1
- 26. Data Structures and Algorithms Summary In this session, you learned that: An algorithm can be defined as a step-by-step procedure for solving a problem that produces the correct result in a finite number of steps. An algorithm has five important properties: – Finiteness – Definiteness – Input – Output – Effectiveness An algorithm that provides the maximum efficiency should be used for solving the problem. Ver. 1.0 Session 1
- 27. Data Structures and Algorithms Summary (Contd.) Data structures can be classified under the following two categories: – Static – Dynamic Two commonly used techniques for designing algorithms are: – Divide and conquer approach – Greedy approach Recursion refers to a technique of defining a process in terms of itself. It is used to solve complex programming problems that are repetitive in nature. The primary resources that an algorithm consumes are: – Time: The CPU time required to execute the algorithm. – Space: The amount of memory used by the algorithm for execution. Ver. 1.0 Session 1
- 28. Data Structures and Algorithms Summary (Contd.) Time/space tradeoff refers to a situation where you can reduce the use of memory at the cost of slower program execution, or reduce the running time at the cost of increased memory usage. The total running time of an algorithm is directly proportional to the number of comparisons involved in the algorithm. The order of growth of an algorithm is defined by using the big O notation. Ver. 1.0 Session 1
Editor's Notes
- #2: Student already have learnt about SCDs in Module I. Therefore, you can start this topic by asking the following questions to students: What are type 1 SCDs? Given an example to explain type 1 SCDs. This will recapitulate what they have learnt about type 1 SCD in Module 1. Now explain the strategy to load the data into these dimension tables with help of the given diagram. Relate this diagram to the example given in SG.
- #4: To start the session, you need to get a set of playing cards in the class. Follow the instructions as given below to begin the game of Rummy. 1. The game begins by dealing a fixed number of cards to all players. The remaining cards are placed face down to form a “stock” pile. 2. There is also a face-up pile called the “discard” pile. 3. Initially, the discard pile contains only one card which is obtained by picking the topmost card from the stock pile. 4. Each player can draw either the topmost card of the stock pile or the topmost card on the discard pile to make a valid sequence in his/her hand. 5. After this, the player must discard one card on top of the discard pile. 6. The next player, can then draw either the topmost card of the draw pile or the topmost card of the discard pile. 7. Therefore, if a player has to draw a card from the discard pile, he/she can draw only the topmost card of the discard pile. 8. Similarly, when a player has to discard a card, he/she must discard it on the top of the discard pile. 9. The discard pile can therefore be considered a Last-In-First-Out list. 10. The last card placed on top of the discard pile is the first one to be drawn. 11. To represent and manipulate this kind of a discard pile in a computer program, you would like to use a list that: a. Contains the details of all the cards in the discard pile. b. Implements insertion and deletion of card details in such a way that the last inserted card is the first one to be removed. This kind of a list can be implemented by using a stack. Ask students to define a stack? Ask them to refer to the game and come up with some characteristics of a stack. Then come to next slide and give them the definition of stacks.
- #9: You can give some more explanation of stacks by with the help of the following example. 1. A stack is like an empty box containing books, which is just wide enough to hold the books in one pile. 2. The books can be placed as well as removed only from the top of the box. 3. The book most recently put in the box is the first one to be taken out. 4. The book at the bottom is the first one to be put inside the box and the last one to be taken out.
- #12: In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.
- #16: In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.
- #18: In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.