Introduction to Computer Science (DS111) PDF

DS111 Introduction to Computer Science ————————————————– Introduction to Computer Science and Data Science ————————————————– 1. Introduction to Computer Science Definition: - Computer Scienc...

DS111 Introduction to Computer Science ————————————————– Introduction to Computer Science and Data Science ————————————————– 1. Introduction to Computer Science Definition: - Computer Science is the study of computers and computational systems. It involves understanding algorithms, data structures, software development, hardware, and the principles behind how computers operate. Why is it important? Understanding the history and evolution of computer science allows us to appreci- ate the tremendous progress that has been made, which in turn helps us comprehend the current landscape and anticipate future developments. It also provides context for the fundamental principles and technologies that underpin modern computing. How did computer science evolve? Early Beginnings: – Abacus (circa 2400 BC): The earliest known tool for computation, representing the first steps toward systematic calculation. 1  – Mechanical Calculators (17th Century): Inventors like Blaise Pascal and Gottfried Wilhelm Leibniz developed mechanical calculators capable of performing basic arithmetic operations. – Charles Babbage (19th Century): Known as the “father of the computer,” Babbage designed the Analytical Engine, which laid the foundation for programmable computers. The Advent of Modern Computing: – Alan Turing and the Turing Machine (1936): Turing introduced the concept of a machine that could simulate any algorithmic process, forming the basis for theoretical computer science. 2  The Turing Machine at Bletchley Park was a giant piece of technology, but very basic by today’s standards. Douglas Hoyt/Flickr (CC BY-NC-ND 2.0) – World War II and ENIAC (1940s): The war accelerated the development of electronic computers like ENIAC, which was used to calculate artillery firing tables. – Transistors and Microprocessors (1950s-1970s): The invention of the transistor in 1947 and the microprocessor in 1971 revolutionized computing by making machines smaller, faster, and more reliable. 3  Software and the Rise of Personal Computers: – Software Development (1950s-1970s): The introduction of programming languages like FOR- TRAN and COBOL, and the development of operating systems, made computers more accessible and versatile. – Personal Computers (1980s): Companies like Apple and IBM brought computers to homes and small businesses, democratizing access to computing power. The Internet and the Information Age: – The World Wide Web (1990s): Tim Berners-Lee’s creation of the World Wide Web trans- formed the internet from a niche network into a global information system. – Mobile Computing (2000s-present): The advent of smartphones and tablets, along with cloud computing, has made computing ubiquitous and accessible anytime, anywhere. What has this led to today? Today, computer science is a vast and dynamic field, encompassing everything from artificial intelligence (AI) and machine learning (ML) to cybersecurity and quantum computing. Its evolution continues to shape our world in profound ways, driving innovation across industries. Key Concepts in Computer Science: Algorithms: Step-by-step procedures for solving problems or performing tasks. Data Structures: Ways to organize and store data for efficient access and modification (e.g., arrays, lists, trees). Programming Languages: Formal languages used to communicate instructions to a computer (e.g., Python, Java). Software Development: The process of designing, coding, testing, and maintaining software appli- cations. Computer Hardware: The physical components of a computer system (e.g., CPU, memory, storage devices). 4 Interconnection between Data Science and Computer Science Why Data Science and Com- puter Science are Interconnected: Foundational Knowledge: Computer science provides the foundational knowledge and skills nec- essary for data science. Understanding algorithms, data structures, and programming is essential for implementing data science techniques. Efficient Data Processing: Data science often involves working with large datasets that require efficient storage, retrieval, and processing. Computer science principles help optimize these processes. Tool Development: Many of the tools and frameworks used in data science (e.g., Python, R, Hadoop, Spark) are rooted in computer science. Knowing how these tools work under the hood can improve their effective use. Algorithm Design: Machine learning, a key component of data science, relies heavily on algorithms. Computer science provides the theoretical background for designing and understanding these algo- rithms. Automation: Data science tasks often require automation, such as data cleaning, feature engineer- ing, and model training. Knowledge of software development and scripting from computer science is invaluable here. Why Study Computer Science in Data Science: Problem-Solving Skills: Computer science enhances problem-solving skills, enabling data scientists to tackle complex data challenges systematically. Programming Proficiency: Proficiency in programming is essential for implementing data science algorithms, creating data pipelines, and developing applications. System Understanding: Understanding how computer systems work helps data scientists optimize performance and scalability of their data processes. Interdisciplinary Approach: Data science is inherently interdisciplinary, drawing from statistics, mathematics, and domain-specific knowledge. Computer science integrates these disciplines through computational methods. Innovation and Research: Advances in computer science drive innovation in data science. Tech- niques such as deep learning, data mining, and distributed computing are born out of computer science research. Why Not Study Only Computer Science or Only Data Science: Limited Scope: Studying only computer science may limit exposure to statistical methods and domain-specific applications essential for data science. Tool-Specific Knowledge: Data science focuses on specific tools and techniques for analyzing and visualizing data, which may not be covered in a traditional computer science curriculum. Industry Relevance: Data science is tailored to industry needs, emphasizing practical applications and problem-solving in various domains. A data science curriculum includes case studies and projects relevant to real-world scenarios. Holistic Understanding: Combining computer science and data science provides a holistic under- standing, equipping students with both technical skills and practical knowledge to address complex data problems effectively 5 References: - Ceruzzi, P. (2003). A History of Modern Computing. MIT Press. - Turing, A. M. (1936). On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society. 6