Foundations of Computing: Concepts to Web Development

ITSC 1212: Introduction to Computing Computer Science Overview: Computer science is the study of algorithms, data structures, and computational systems. Algorithms are step-by-step instructions to solve problems efficiently. Data structures organize and store data to enable efficient manipulation. Programming Fundamentals: Programming languages are used to write instructions for computers. Syntax rules must be followed for code to be understood by the computer. Variables store data values; data types include integers, floats, strings, and booleans. Control structures, such as if statements and loops, guide program flow. Functions and Modularization: Functions group related code and can be reused throughout a program. Function parameters allow passing data into a function. Return values pass data back from functions. Modularization breaks programs into smaller, manageable pieces. Data Structures: Arrays store multiple values of the same data type. Linked lists organize data in nodes with pointers. Stacks and queues are linear data structures. Hash tables allow efficient data retrieval using keys. Recursion: Recursion is a technique where a function calls itself. Base cases provide stopping conditions for recursive functions. Recursion can be used to solve problems that can be broken down into simpler instances. Object-Oriented Programming (OOP): OOP organizes code around objects, which bundle data and methods. Classes define object blueprints, and objects are instances of classes. Encapsulation hides the internal details of objects. Inheritance allows classes to inherit properties and methods from other classes. Polymorphism enables objects of different classes to be treated as instances of a common class. Algorithm Design and Analysis: Algorithms are sets of steps to solve problems. Efficiency is measured using time and space complexity. Big O notation describes how an algorithm's runtime grows relative to input size. Searching and Sorting Algorithms: Linear search checks each element sequentially. Binary search works on sorted data and halves the search range. Bubble sort and insertion sort are simple sorting algorithms. Merge sort and quicksort are more efficient sorting algorithms. File Input/Output: Reading and writing files is crucial for data processing. Input validation ensures that the data is in the expected format. Handling exceptions and errors during file operations is important. Basic Data Analysis: Using libraries like NumPy and Pandas for data manipulation. Data visualization with tools like Matplotlib or Seaborn. Basic statistical analysis and data exploration techniques. Introduction to Algorithms: Algorithm efficiency is important for large-scale problems. Greedy algorithms make locally optimal choices at each step. Divide and conquer strategies split problems into smaller subproblems. Ethical and Social Considerations: Discussing ethical issues in computing, such as privacy, security, and bias. The societal impact of technology, data collection, and decision-making algorithms. Adhering to ethical principles in software development and data handling. Understanding the basics of computer hardware and software components. Differentiating between hardware and software, input and output devices. Overview of operating systems and their functions. Problem Solving and Programming Concepts: Steps involved in solving problems using computers. Algorithm design and pseudocode. Variables, data types, and basic programming concepts. Control structures: selection (if statements) and iteration (loops). Introduction to Programming Languages: Overview of programming languages and their differences (e.g., high-level vs. low-level). Syntax, semantics, and keywords. Writing and executing simple programs in a programming language like Python. Data Representation: Understanding binary, hexadecimal, and decimal number systems. Conversion between different number systems. Representing characters using ASCII and Unicode. Functions and Modularization: Defining and using functions in programming. Parameters, return values, and function libraries. Modular programming: breaking down programs into smaller, reusable components. Arrays and Lists: Introduction to arrays and lists as data structures. Declaring, initializing, and accessing elements in arrays/lists. Common array/list operations: searching, sorting, and iteration. File Input/Output: Reading and writing data to files in a program. Handling exceptions and errors in file operations. Storing and retrieving data from files. Object-Oriented Programming (OOP) Concepts: Basics of object-oriented programming paradigm. Classes, objects, attributes, and methods. Encapsulation, inheritance, and polymorphism. Introduction to Web Development: Basics of HTML and CSS. Creating simple web pages and styling elements. Understanding the client-server model and web technologies. Basic Data Structures: Introduction to stacks and queues. Linked lists and their implementations. Basic concepts of memory allocation and management. Software Development Life Cycle (SDLC): Overview of different phases in software development. Importance of requirements analysis, design, implementation, testing, and maintenance. Ethical and Legal Considerations in Computing: Discussing ethical issues related to computing. Intellectual property, privacy, and cybersecurity concerns. Adhering to ethical standards and practices in software development.