Daily Archives: June 30, 2014

Object-Oriented Programming, Programming Languages, Software Engineering Curriculum

Topic 5 – Object-Oriented Programming

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Why do I need to learn about object-oriented programming?

Because most of modern software are written using object-oriented programming languages. Using object-oriented programming as a tool and more importantly as a problem solving approach will save you a lot of time and cost when you create modern software systems.

What can I do after finishing learning object-oriented programming?

You will be able to write a program to allow a user
– to draw a picture, save it, then load it, or
– to play ping-pong game, or
– to store and search for an employee’s CV.

Alright! What should I do now?

Please read this Jim Keogh and Mario Giannini (2004). OOP Demystified. McGraw-Hill book.

Is that all?

Unfortunately, the book is not sufficient for writing real world object-oriented software. Although the book already gives you the fundamental concepts of object-oriented programming the examples are only to illustrate the concepts, not to demonstrate real world situations.

We recommend that you read it first because it introduces basic concepts of object-oriented programming very well. It helps you to distinguish object-oriented concepts from procedural programing concepts very clearly so that you could read other books to dig into real world object-oriented programming in a proper way.

Please read this RB Whitaker (2017). The C# Player’s Guide. Starbound Software book to learn how to apply object-oriented concepts to creating real world software using a specific object-oriented programming language (i.e. C# language).

After that please take a quick look at
– this Joseph Albahari (2023). C# 12 in a Nutshell – The Definitive Reference. O’Reilly Media book or
– this Herbert Schildt (2010). C# 4.0 The Complete Reference. McGraw Hill book so that you can refer to a specific topic that needs more study when developing real world software.

I hear that there are many object-oriented programming languages such as C++, C#, Java, PHP, Objective-C, Swift, Python, Ruby.
How many object-oriented programming languages do I need to know?

You should learn as many as possible. However, in this stage, we recommend that you learn only C++ or C# or Java.

C++ is the most used language for creating software systems that need high performance, including games, operating systems, compilers, database management systems, web browsers, graphics editors, medical and engineering applications.

Java and C# are the most used languages for creating enterprise systems.

If you are required or prefer to learn C++, please read this Bjarne Stroustrup (2013). The C++ Programming Language. Pearson Education book.

If you are required or prefer to learn Java, please read this Herbert Schildt (2019). Java: The Complete Reference. McGraw-Hill Education book.

After that please read
– this Matt Weisfeld (2019). The Object-Oriented Thought Process. Pearson Education book and
– this Bertrand Meyer (1997). Object-Oriented Software Construction. Prentice Hall book to get deeper understanding about object-oriented concepts and learn how to design software using object-oriented approach more efficiently.

Terminology Review:

  • Abstraction.
  • Encapsulation.
  • Class-based Inheritance.
  • Prototype-based Inheritance.
  • Polymorphism.
  • Types.
  • Method Variables (C++ Function Pointers, C# Delegates).
  • Concurrency.
  • Collections.
  • Object-Oriented Analysis.
  • Object-Oriented Design.
  • Object-Oriented Programming.

After finishing learning about object-oriented programming please click Topic 6 – Introduction to Windows Programming to continue.

Algorithms, Software Engineering Curriculum

Topic 4 – Introduction to Data Structures and Algorithms

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Why do I need to learn data structures and algorithms?

Your software should address real-world problems. While knowing a programming language is important for writing software, it doesn’t help you leverage existing solutions to save time and effort when building a system.

Many real-world problems have already been solved, and their solutions are documented as data structures and algorithms. It’s important to learn these so you don’t reinvent the wheel, enabling you to apply them to your specific problems, thereby reducing time and effort, or optimizing your solutions.

Additionally, learning algorithms and data structures will help you develop algorithmic thinking and problem-solving skills, which are essential for any software developer.

What can I do after finishing learning algorithms and data structures?

Given a problem, you’ll be able to choose the appropriate data structures to represent concepts in a computer.

You’ll be capable of writing a program that instructs the computer to:
– Store and search for data efficiently,
– Sort information,
– Compute irrational numbers,
– Find the shortest path between two locations, or
– Encrypt and decrypt sensitive information.

When examining source code, you’ll also be able to determine which program will run faster.

That sounds useful! How can I learn algorithms and data structures?

Please read this Jay Wengrow (2020). A Common-Sense Guide to Data Structures and Algorithms. Pragmatic Bookshelf first.

After that please read this Richard J. Trudeau (1993). Introduction to Graph Theory. Dover Publications book to learn about graph theory.

After that please read this Thomas H. Cormen et al. (2022). Introduction to Algorithms. The MIT Press book to learn in depth about algorithms and data structures.

Alternatively, please audit this MIT 6.006 – Introduction to Algorithms, Fall 2011 course (Lecture Notes).

After that, if you are interested in delving deeper into data structures and algorithms, you can audit this MIT 6.046J – Design and Analysis of Algorithms, Spring 2015 course (Lecture Notes).

Terminology Review:

  • Arrays.
  • Selection Sort.
  • Invariant Reasoning.
  • Order of Growth, Big O, Big Theta.
  • Insertion Sort. Complexity: O(n²).
  • Merge Sort, Recursion Tree. Complexity: O(nlgn).
  • Bubble Sort. Complexity: O(n²).
  • Quick Sort. Complexity: O(nlgn).
  • Counting Sort. Complexity: O(n + k).
  • Radix Sort. Complexity: O(nlogₙk).
  • Sets.
  • Stacks.
  • Queues.
  • Lists, Linked Lists, Sorted Lists.
  • Trees, Binary Trees, Heaps.
  • Heapsort. Complexity: O(nlgn).
  • Hash Tables, Maps, Dictionaries: Chaining, Division Method, Multiplication Method, Open Addressing, Linear Probing, Double Hashing, Cuckoo Hashing.
  • Linear Search.
  • Binary Search.
  • String Matching: Karp-Rabin Algorithm, Rolling Hash ADT.
  • Binary Trees: Depth, Height, Traversal Order.
  • Binary Search Trees (BST).
  • AVL Trees, Rotations, AVL Sort.
  • Sequence Binary Trees.
  • Red-Black Trees.
  • 2-3 Trees.
  • B-Trees.
  • B+ Trees.
  • Comparison Model of Computation, Decision Tree, Search Lower Bound, Sorting Lower Bound.
  • Hierarchical Structure.
  • Karatsuba’s Algorithm.
  • Newton’s Method for Computing Square Roots.
  • Graphs: Complete Graphs, The Handshaking Lemma, Identical Graphs, Graph Isomorphism,  Adjacency Lists, Implicit Graphs, Adjacency Matrix, Incidence Matrix.
  • Breadth-First Search: Shortest Paths.
  • Depth-First Search: Tree Edges, Nontree Edges (Back Edges, Forward Edges, Cross Edges), Cycle Detection, Job Scheduling, Topological Sort.
  • Dijkstra’s Algorithm.
  • Bellman–Ford Algorithm.
  • Single-source Single-target Dijkstra.
  • Bi-Directional Search.
  • A* Algorithm.
  • Dynamic Programming: Subproblems, Guessing, Recursion and Memoization, Bottom-up, Topological Order, Original Problem and Parent Pointers.
  • Dynamic Programming Examples: Fibonacci Numbers, Text Justification, Parenthesization, Edit Distance.
  • Computational Difficulty: P, NP, EXP, R.
  • Hardness and Completeness: NP-hard, NP-complete, EXP-hard, EXP-complete.

After finishing learning about data structures and algorithms please click Topic 5 – Object-Oriented Programming to continue.