2. DATA STRUCTURES (CS212D) Overview & Review

3. Instructor Information 2  Instructor Information:  Dr. Radwa El Shawi  Room:2.501.29  Email:rmelshawi@pnu.edu.sarmelshawi@pnu.edu.sa

4. Course objectives  Be familiar with problem solving.  Be familiar with basic techniques of algorithm analysis.  Be familiar with the concept of recursion.  Master the implementation of linked data structures such as linked lists, stacks, and queues.

5. Course objectives Cont.  Be familiar with advanced data structures such as balanced search trees, graphs and hash tables.  Master analyzing problems and writing program solutions to problems using the above techniques  Be able to select appropriate data structures and algorithms for given problems

6. Course Outline  Introduction to data structures  Arrays  Recursion  Linked Lists  Stacks and queues  Trees  Graphs  Hash tables

7. Course Material  Textbook: Data Structures & Algorithms in JAVA (6 th Edition), by M. Goodrich, R. Tamassia and M. Goldwasser, John Wiley & Sons, inc., 2014.  Lecture hand-outs  Lab notes and exercises

8. Grading/Evaluation Course Assessment ToolsPercent Mid term 115% Mid term 215% Lab assignments, quizzes and participation 10% Final lab20% Final40%

9. What is data?  Data  A collection of facts from which conclusion may be drawn  e.g. Data: Temperature 35°C; Conclusion: It is hot.  Types of data  Textual: For example, your name (Nourah)  Numeric: For example, your ID (090254)  Audio: For example, your voice  Video: For example, your voice and picture  (...)

10. What is data structure?  A particular way of storing and organizing data in a computer so that it can be used efficiently and effectively.  Data structure is the logical or mathematical model of a particular organization of data.  A group of data elements grouped together under one name.  For example, an array of integers

11. There are many, but we named a few. We’ll learn these data structures in details through this term! Array Linked List Tree Queue Stack Types of data structures

12. The Need for Data Structures  Goal: to organize data  Criteria: to facilitate efficient  storage of data  retrieval of data  manipulation of data  Design Issue:  select and design appropriate data types (This is the main motivation to learn and understand data structures)

13. Data Structure Operations  Traversing  Accessing each data element exactly once so that certain items in the data may be processed  Searching  Finding the location of the data element (key) in the structure  Insertion  Adding a new data element to the structure

14. Data Structure Operations (cont.)  Deletion  Removing a data element from the structure  Sorting  Arrange the data elements in a logical order (ascending/descending)  Merging  Combining data elements from two or more data structures into one

15. What is algorithm?  A finite set of instructions which accomplish a particular task  A method or process to solve a problem  Transforms input of a problem to output  Algorithm = Input + Process + Output  Algorithm development is an art – it needs practice, practice and only practice!

16. What is a good algorithm?  It must be correct  It must be finite (in terms of time and size)  It must terminate  It must be unambiguous  Which step is next?  It must be space and time efficient  A program is an instance of an algorithm, written in some specific programming language

17. A simple algorithm  Problem: Find maximum of a, b, c  Algorithm  Input = a, b, c  Output = max  Process o Let max = a o If b > max then max = b o If c > max then max = c o Display max  Order is very important!!!

18. Algorithm development: Basics  Clearly identify:  what output is required?  what is the input?  What steps are required to transform input into output o The most crucial bit o Needs problem solving skills o A problem can be solved in many different ways o Which solution, amongst the different possible solutions is optimal?

19. Algorithm Design: Practice  Example 1: Determining even/odd number  A number divisible by 2 is considered an even number, while a number which is not divisible by 2 is considered an odd number. Write pseudo-code to display first N odd/even numbers.  Example 2: Computing Weekly Wages  Gross pay depends on the pay rate and the number of hours worked per week. However, if you work more than 40 hours, you get paid time-and-a-half for all hours worked over 40. Write the pseudo-code to compute gross pay given pay rate and hours worked

20. Even/ Odd Numbers  Input range  for num←0; num<=range; num←num+1 do  if num % 2 = 0 then  print num is even  else  print num is odd  endif  endfor

21. Computing weekly wages Input hours_worked, pay_rate if hours_worked <= 40 then gross_pay ← pay_rate x hours_worked else basic_pay ← pay_rate x 40 over_time ← hours_worked – 40 over_time_pay ← 1.5 x pay_rate x over_time gross_pay ← basic_pay + over_time_pay endfor print gross_pay

22. What is algorithm analysis?  A methodology to predict the resources that the algorithm requires  Computer memory  Computational time  We’ll focus on computational time  It does not mean memory is not important  Generally, there is a trade-off between the two factors o Space-time trade-off is a common term

23. How to analyse algorithms?  Experimental Approach  Implement algorithms as programs and run them on computers  Not a good approach, though! o Results only for a limited set of test inputs o Difficult comparisons due to the experiment environments (need the same computers, same operating systems, etc.) o Full implementation and execution of an algorithm  We need an approach which allows us to avoid experimental study

24. How to analyse algorithms?  Theoretical Approach  General methodology for analysing the running time o Considers all possible inputs o Evaluates algorithms in a way that is independent from the hardware and software environments o Analyses an algorithm without implementing it  Count only primitive operations used in an algorithm  Associate each algorithm with a function f(n) that characterises the running time of the algorithm as a function of the input size n o A good approximation of the total number of primitive operations

25. Primitive Operations  Basic computations performed by an algorithm  Each operation corresponding to a low-level instruction with a constant execution time  Largely independent from the programming language  Examples  Evaluating an expression ( x + y )  Assigning a value to a variable ( x ←5 )  Comparing two numbers ( x < y )  Indexing into an array ( A[i] )  Calling a method ( mycalculator.sum() )  Returning from a method ( return result )

26. Counting Primitive Operations  Total number of primitive operations executed  is the running time of an algorithms  is a function of the input size  Example  Algorithm ArrayMax(A, n) # operations  currentMax ←A[0] 2: (1 +1)  for i←1;i<n; i←i+1 do 3n-1: (1 + n+2(n- 1))  if A[i]>currentMax then 2(n − 1)  currentMax ←A[i] 2(n − 1)  endif  endfor  return currentMax 1  Total: 7n − 2

27. Algorithm efficiency: growth rate  An algorithm’s time requirements can be expressed as a function of (problem) input size  Problem size depends on the particular problem:  For a search problem, the problem size is the number of elements in the search space  For a sorting problem, the problem size is the number of elements in the given list  How quickly the time of an algorithm grows as a function of problem size -- this is often called an algorithm’s growth rate

28. Algorithm growth rate Which algorithm is the most efficient? [The one with the growth rate Log N.]

29. Homework 1.Write an algorithm to find the largest of a set of numbers. You do not know the number of numbers. 2.Write an algorithm that finds the average of (n) numbers. For example numbers are [4,5,14,20,3,6]s