1. CS3901 Intermediate Programming & Data Structures Introduction

2. What will we learn? (1)‏ C++ Programming Java Programming
Memory management
Lists
Programming with structs and classes
Java Programming
Object-Oriented programming concepts, including
Inheritance
Polymorphism
Abstract Classes
Interfaces
Generics
Packages
The syntax we learned will be for C++ and Java, but the concepts appear in many other languages.

3. What will we learn? (2)‏ Primitive Data Structures
Arrays
Linked Lists
Trees
Linked Node and Array Based
Heaps
Binary Search Trees, AVL Trees, 2-4 Trees, Red-Black Trees
Hash Tables
Abstract Data Types (Built on Primitives)‏
Lists
Stacks
Queues
Maps
Dictionaries
Possibly:
Sets
Graphs
There exist more complicated data structures than the ones covered in the course.
Understanding the above data structures will give you a basis for understanding more advanced structures

4. What will we learn? (3)‏

5. Data Structures Problems
The following slides list various problems or domain areas in computer science, which require use data structures we will learn about in this course.
Think about the structure of the data we want to represent.
What additional or related problems can you come up with?

6. Databases Simple ones store key-value pairs (on disk)‏
More complex versions:
Relational Databases
XML Databases
OO Databases

7. Timetabling Timetabling (creating a university class schedule)‏
Students belong to classes
Teachers belong to classes
Classes have room assignments
Goal: choose a set of time slots for each class which
do not have conflicts in teaching and room assignments
minimize conflicts in students’ schedules

8. Forensics
Task: record the flow of control in assembly language programs, either at the individual statement level, or at the function call level.
Goal: search for cycles (to recognize loops)‏

9. Computer Graphics
In Computer Graphics, we want to represent the logical organization of scene:
A slightly more complicated version is called a Scene Graph (although it’s really a tree).

10. Matrices
Matrices often need to be designed to accommodate a property of sparseness.
In math, a sparse matrix is one where most of the values are zero.
In data structures, a sparse matrix is one where most of the elements are un-allocated.
Each node knows its own virtual coordinate, and points to some other actual node (or null), depending on that virtual coordinate.
Uses in computer vision, graphics, engineering

11. ???
<Your problem here.>

12. Homework Windows users: Mac users: Linux/Unix users: All Users:
Install
Cygwin
gcc
Mac users:
Install the latest version of Xcode
Linux/Unix users:
Install gcc
All Users:
Install an editor with syntax highlighting