Computer Science

Computer programming is the process of creating and implementing a set of instructions that tell a computer how to perform a task, akin to creating a recipe.

Python, Java, and C++ are some of the most popular programming languages used in the industry today.

Ada Lovelace is credited with writing the first computer program.

Practical examples of computer programs used in daily life include email services, word processing software, social media platforms, online banking apps, and weather forecasting software.

High-Level Languages and Low-Level Languages.

High-level languages are closer to human language and include Python, while low-level languages are closer to machine language and include Assembly language.

They differ based on their paradigm, such as imperative, object-oriented, functional, procedural, and logic-based.

Yes, many modern languages offer features from multiple paradigms like Python which is primarily an imperative language, but also has object-oriented features.

Structure brings order and clarity to code, making it readable and understandable. It comprises elements like sequence, selection, and iteration, which help in efficient execution of instructions, path choices, and repetition of program sections.

Strategies include tracing the program's flow, understanding data manipulation, recognising interactions between program parts, identifying bugs, developing testing/debugging strategies, practicing active reading, breaking code into chunks, using debuggers, not skipping documentation, and understanding the underlying problem.

An interpreter in computer programming goes beyond understanding the syntax; they recognise the implications and potential pitfalls of the code. They play a vital role in reading and understanding programs, making them more efficient and less prone to bugs.

Common mistakes include overlooking syntax/semantic errors, ignoring error messages, misinterpreting code's purpose, racing through the code and not testing enough. To avoid them, examine the code thoroughly, read error messages, understand the problem context, go slow, and test often.

An algorithm in computer science is a well-structured, unambiguous, and step-by-step set of instructions used to solve a problem or achieve a certain objective.

Algorithms reduce problem complexity, enable efficient data processing and memory usage, ensure data security, and are crucial for data search in large databases.

Bubble Sort is for sorting elements, Binary Search is used in searching elements in a sorted list, Dijkstra's Algorithm finds the shortest path in a graph, and Euclidean Algorithm determines the greatest common divisor of two numbers.

Data structures are ways of organising and storing data in a computer for efficient use, while algorithms are step-by-step procedures designed to perform specific operations. Together, they are critical elements for problem-solving in computer science.

Data structures are crucial in algorithms as they enable efficient data manipulation and computation. The choice of data structure can greatly affect an algorithm's efficiency in terms of speed and memory usage.

Data Structures are broadly classified into Primitive and Non-Primitive. Primitive types include integer, float, and char, while Non-Primitive types include user-defined types like Array, Stack, Queue, List, and Tree.

The two primary searching algorithms in computer science are Linear Search and Binary Search. The Linear Search starts at the beginning of a list and checks every element until it finds the targeted one. Binary Search is applicable to a sorted list or array, it divides the list into two halves and determines if the desired value is in the first or the second half, continuing this halving until it locates the item.

The performance of searching algorithms is often measured in terms of time complexity. Binary Search outperforms Linear Search as it has a logarithmic time complexity of O(log(n)) compared to the linear time complexity of O(n) for Linear Search. However, Binary Search requires the list to be sorted, while Linear Search does not.

The auto-complete feature of search engines is a practical example. The list of predicted words are sorted in the dictionary, and a quick search operation matches the user's input with the closest predictions. This optimal coordination between searching and sorting algorithms provides a seamless user experience.

Digital maps use algorithms such as Dijkstra’s algorithm to calculate the shortest or fastest route between two points, taking into account real-time data like traffic, distance, and estimated travel time.

Google and other search engines use complex algorithms to crawl the web, index pages, and retrieve and rank content that matches a user's query. A well-known example is Google’s PageRank algorithm.

E-commerce platforms use recommendation algorithms, analysing a user's browsing history, purchase history, and items in wishlist to suggest products that the user might be interested in.

The four types of time complexities are: Constant time complexity \(O(1)\), Linear time complexity \(O(n)\), Quadratic time complexity \(O(n^2)\), and Logarithmic time complexity \(O(\log n)\).

Practice, understanding time and space complexity, studying different types of algorithms, debugging, and participating in code reviews are key strategies to enhance understanding and competence in algorithms.

Deciding between time and space complexity often due to a trade-off between them, adhering to the problem's constraints such as time limits or limited memory space, and handling algorithm errors or bugs are some challenges in advanced study of algorithms.

Data structures organise data within a system for efficient use and manipulation. They enable different operations like searching, insertion, deletion, and sorting. Their selection impacts software performance, time, and space savings.

An algorithm is a step-by-step procedural instruction that interacts with data structures. They define the steps needed to carry out various operations on given data structures, such as sorting, searching, insertion, and deletion.

Different data structures manage and organise data in ways matching their nature. For example, an array stores data based on an orderly index number, a stack uses a Last-In-First-Out method, trees and graphs are used for hierarchical data organisation.

Structured data refers to formally organised, easy-to-understand data that is highly-organised, easily searchable, and can be readily classified.

Arrays are fixed-size sequenced collections of same-type elements. Stacks follow a LIFO or FILO order for operations. Queues are containers that follow the FIFO rule for inserting and removing elements.

Linked Lists are collections where each element refers to the next. Trees are data structures used to represent hierarchical structures. Graphs are non-linear data structures representing a set of connected objects.

A Tree Data Structure is a hierarchical structure that emulates a tree with linked nodes, featuring a root node and parent-child relationships between nodes. Some basic terminologies include Node: a single element, Root: the topmost node without a parent, Child Node: a node directly connected to another moving away from the root, and Parent Node: the converse of a child node.

Tree Data Structures are used for hierarchical data organization, HTML DOM architecture, and network routing. A binary tree is a type of tree data structure where each node can have at most two children, referred to as the left and right child.

Advanced tree structures include AVL Trees, B-Trees, and Red-Black Trees. AVL Trees are used for quicker search times, B-Trees are used in databases and file systems for rapid search, insertions, and deletions, and Red-Black Trees are used in operations like the Completely Fair Scheduler in operating systems and the nginx web server.

Structured data examples include spreadsheets, relational database management systems (RDBMS) such as MySQL and Oracle Database, medical records, and online forms. These structures allow for efficient storage and retrieval of data.

Search engines like Google use inverted index data structures, large databases use hash tables, network technology uses tree data structures in routing tables, and Machine Learning algorithms use arrays, trees, and graphs. The chosen data structure can greatly influence performance.

In software development, data structures like arrays and linked lists are used in algorithm design, managing system resources uses queues, stacks, and heaps, in game development trees are used for AI decision-making, and GUI applications use trees and hashes for features like dropdown menus and windows.

Structured data is well-organised and easily understandable, typically stored in databases, whereas unstructured data lacks a predefined data model, making it complex to process and analyse.

Unstructured data is driving a need for advanced analytics techniques like NLP, image recognition, and machine learning, necessitating high-performance resources and sophisticated algorithms.

Initial systems were hierarchical and network DBMS. The relational model simplified data management, leading to RDBMS, which underpins most business applications today. RDBMS continue to evolve, incorporating features such as unstructured data handling and horizontal scalability.

Functional Programming is a style of programming that models computations as the evaluation of expressions. Functions are first-class entities in FP and the primary focus is the application of functions, rather than manipulation of data.

The core concepts of Functional Programming are Immutability, meaning once a data value is created, it can't be changed; Pure Functions, which always produce the same output given the same input with no side-effects; and First-Class and Higher Order Functions, where functions are treated as values and can be passed around.

Functional programming languages include Scala, Haskell, Clojure, and Erlang. Scala is a hybrid language combining features of object-oriented and functional languages. Haskell uses mathematical logic, a strong static type system, and lazy evaluation. Clojure is from the Lisp family and effectively handles concurrency and data processing. Erlang stands out for its concurrency and fault tolerance.

In Scala, a factorial is calculated through recursion, keeping in line with functional programming principles. In Haskell, the 'factorial' function is defined using recursion and pattern matching. Both languages use 'map' function to apply a given function to every item in a list, producing a new list with the results.

In Python, a simple example of a function in functional programming style is the 'add' function that adds two numbers. JavaScript supports higher order functions, 'applyToNumbers' function takes a function and an array as arguments and applies the function to every item in the array.

Advanced concepts of functional programming include recursion, showcased by calculating Fibonacci series in Haskell, managing side-effects by using monads in Haskell, and working with immutable data structures, demonstrated with the use of the Immut.js library in JavaScript.

The advantages of functional programming include immutability, first-class functions, pure functions, automatic memorisation, promotion of modular and efficient coding practices, and the simplified handling of concurrency due to the lack of mutable state. It also allows efficient processing of 'Big Data'.

Functional programming promotes purity, referential transparency, modularity, use of higher-order functions, and immutable data. It also provides efficient techniques for processing lists, thus making code easier to understand, debug, test, reuse, and maintain.