1. 1 CS 311 Data Structures

2. 2 Instructor Name : Vana Doufexi Office : 2-229, Ford Building Email : vdoufexi@cs.northwestern.edu Office hours: By appointment (email me)

3. 3 Resources class webpage : http://www.cs.northwestern.edu/academics/courses/ 311 –Use it to download class notes, handouts and assignments look up your grades access online tutorials and references look up course policies on –grading –late homework –collaboration

4. 4 Resources class newsgroup : –The class newsgroup is cs.311 at news.cs.northwestern.edu –Use it to discuss class material, discuss the assignments (but NEVER post solutions)

5. 5 Problem Solving Problem solving = –Understanding the problem –Designing a solution –Implementing the solution What exactly is a solution?

6. 6 Problem Solving An algorithm is a computational method for solving a problem. It is a sequence of steps that take us from the input to the output. An algorithm must be –Correct It should provide a correct solution according to the specifications. –Finite It should terminate. –General It should work for every instance of a problem –Efficient It should use few resources (such as time or memory).

7. 7 Problem Solving Any algorithm we come up with will have to manipulate data in some way –The way we choose to organize our data directly affects the efficiency of our algorithm Solution = algorithm + data organization –Both components are strongly interconnected.

8. 8 What this class is about Classic data structures. –Design, implementation and use How to choose or devise the appropriate data structures for a problem. Classic algorithms and how the choice of data structure affects their efficiency. More C++ programming experience. Programming in the Linux/Unix environment

9. 9 Problem Solving Key characteristic: Abstraction –Oftentimes, different real-world problems can be modeled using the same underlying idea Examples: Runtime storage, Undo operation. In both cases we have a sequence of data items (activation records, actions) and we can only add and remove items from one end of the sequence. –We can design a data organization scheme that retains the general characteristics of this "data and operations" model (without any dependences on the type of data or method of implementation). –This is called data abstraction. Big advantage: code reuse. We can use the same scheme for both problems.

10. 10 Problem Solving Key characteristic: Encapsulation –The way the data is organized and the operations that can be performed on it are implemented should be hidden. Its properties are separated from the implementation –Data can be manipulated in a controlled way, only through an interface. The internal details are hidden. In-class example: library reserve desk –This is called encapsulation. Big advantage: The code has higher maintainability. The internal organization/implementation can be modified/improved without changing the interface. Big advantage: Outside objects cannot interfere with the internal organization, inadvertently corrupting it.

11. 11 Problem Solving Key characteristic: Information hiding –A solution typically consists of different modules that interact with one another. –Information hiding is the idea of concealing details from other modules that do not need to know those details.

12. 12 Data organization Major components: –The data –The operations allowed on it These make up an Abstract Data Type A Data Structure is a construct that implements a particular ADT. Example: An indexed-list ADT.

13. 13 Data organization Example: An indexed-list ADT. –DATA: An indexed sequence of items of some type The lower index The upper index –OPERATIONS: Access item at position (index) i Add an item at position i Remove an item at position i Change the bounds Make it more abstract by not assuming it's 0 Make it more abstract by not assuming a type Note that there is no mention of how this might be implemented.