Books give an overview of a subject and provide information on core concepts and theories. This makes them a good starting point for your assignments. Core books for your subject will be listed on your module reading list. If you have an online reading list on Blackboard or another learning environment, this will show you the copies of the book available from the library. You can also find books by searching FINDit, the Library search engine.
Packt Publishing provides specific IT learning on emerging technologies and key skills in more established tools. Five subject packages are offered in Artificial Intelligence, Data & Machine Learning, Cloud & Security, Programming and Web Development.
Artificial Intelligence: The bundle contains a comprehensive collection of titles that delve into the latest tools and cutting-edge topics essential for those aspiring to excel in the realms of artificial intelligence and data science. The bundle includes titles that cater to job roles such as Data Analyst, Machine Learning Engineer, Data Scientist, AI Engineer, Data Architect, Database Administrator, or BI Developer.
Data & Machine Learning: This bundle includes titles that cover all the latest tools and topics required to become a successful data scientist or machine learning engineer. The titles in this bundle cater to job roles such as Data Analyst, Machine Learning Engineer, Data Scientist, AI Engineer, Data Architect, Database Administrator and BI developer.
Cloud & Security: This bundle consists of titles that are relevant for job roles like Cybersecurity Analyst, Security Engineer, Network Engineer, Cloud Developer, Cloud Architect, Cloud Administrator and DevOps Engineer.
Programming: This bundle includes titles that cater to job roles such as Programmer, Systems Programmer, Language Developer, Software Architect, and API developer. These topics would help programmers polish their skills to write high performance and maintainable code.
Web Development: This bundle contains titles that are relevant for job roles such as Front-End Developer, Server-Side Developer, Backend Developer, Full-Stack Developer, Web Graphic Designer, and CMS Admins/Developer.
1. To find a specific book title, enter the book title or author in the FINDit search box and select the Libraries search option.
2. To find books on a subject, enter a keyword into the FINDit search box and select the Libraries search option.
3. Can’t find the book you are looking for? You can request it as an interlibrary loan or Recommend the book to your Librarian
The Dewey Decimal system is a classification system used by libraries to arrange books by subject. Each book is issued a shelfmark number, which is placed on the spine of the book and arranged in numerical order. After the numbers, there are three letters that refer to the author or editor of the book and are in alphabetical order.
Here are some key shelfmarks for your subject area.
| 003.3 | Computer modeling & simulation |
| 004.0151 | Mathematics for computing |
| 004.2 | Systems analysis, design & architecture |
| 005.133 | Programming languages |
| 005.74 | Databases |
| 005.8 | Computer security and cryptography |
| 006.3 | Artificial intelligence |
| 006.6 | Computer graphics and animation |
| 006.7 | Multimedia systems |
| 344.20399 | Computing law |
| 344.2067 | Commercial and e-commerce law |
| 364.168 | Cybercrime |
| 384.3 | The Internet and World Wide Web |
| 510 | Mathematics |
| 658.05 | Business computing |
| 794.8 | Computer games |
Think Python
by
Python is an excellent way to get started in programming, and this clear, concise guide walks you through Python a step at a time--beginning with basic programming concepts before moving on to functions, data structures, and object-oriented design. This revised third edition reflects the growing role of large language models (LLMs) in programming and includes exercises on effective LLM prompts, testing code, and debugging skills. With this popular hands-on guide at your side, you'll get: A grounding in the syntax and semantics of the Python language A clear definition of each programming concept, with emphasis on clear vocabulary How to work with variables, statements, functions, and data structures in a logical progression Techniques for reading and writing files and databases A solid understanding of objects, methods, and object-oriented programming Debugging strategies for syntax, runtime, and semantic errors An introduction to recursion, interface design, data structures, and basic algorithms How to use LLMs--including effective prompts, testing code, and debugging And more
Basics of Python Programming
by
Basics of Python Programming: A Quick Guide for Beginners is an essential companion to mastering the Python programming language. The book presents information about Python in 12 structured chapters with a strong emphasis on fundamentals and practical information. Starting with basic operators, functions and expressions, contents explain file handling, exception handling and modules. The book concludes with advanced topics such as object oriented programming and machine learning. Key Features: Fundamental Focus: Covers the core concepts of Python programming to build a strong foundation in python programming in an easy-to-understand format.Practical Demonstrations: Learn by doing. This textbook includes hands-on practical demonstrations that reinforce your understanding of Python concepts.IDE Guidance: Includes programming and installation guidance for Python-supporting Integrated Development Environments (IDEs).Explores Python Frameworks: Introduces Python frameworks such as Matplotlib, TensorFlow, PyTorch, Scikit-Learn, and NLTK for complex projects.Python for Machine Learning: Gives a preliminary understanding of Python for machine learning tasks for data science and AI applications. Basics of Python Programming: A Quick Guide for Beginners is the perfect starting point for aspiring students, programmers and tech enthusiasts. Whether you're a student looking to build a solid foundation in Python or an industry professional venturing into machine learning and artificial intelligence, this textbook has you covered. Readership Computer science, engineering and technology students; programming enthusiasts and professionals.
Getting Data Science Done
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This text outlines the essential stages in running successful data science projects - providing comprehensive guidelines to help you identify potential issues and then a range of strategies for mitigating them.
How to Think about Data Science
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This book is a timely and critical introduction for those interested in what data science is (and isn't), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist's approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist's approach to explaining data science through questions and examples.
Unlocking Artificial Intelligence
This open access book provides a state-of-the-art overview of current machine learning research and its exploitation in various application areas. It has become apparent that the deep integration of artificial intelligence (AI) methods in products and services is essential for companies to stay competitive.
Maths for Computing
by
This introductory textbook covers all the mathematical concepts necessary for a computing degree, limiting coverage only to the material needed for the fundamentals of computing rather than delving into the higher mathematical concepts.
Essential Math for Data Science
by
To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.
Practical Statistics for Data Scientists
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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data
Math adventures with Python
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Teaches math concepts through programming. Begins with a crash course in basic programming concepts and how to use Python's built-in Turtle module, and then explores topics like writing programs to solve equations and matrices, drawing polygons and fractals, and using the Processing graphics library to create interactive 2D and 3D models.
Mathematics
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Math does not need to be difficult. This book, complete with exercises and answers, forms a course which will take you from beginner or intermediate level to being a confident mathematician. This book includes: simple step-by-step explanations, to help you grasp new topics or those that have previously confused you; practice questions throughout, to help you embed your learning and improve your confidence; and end of chapter summaries, to help you remember the key points you've learned--all in one great-value book, so you don't need any separate workbooks or course books. Chapters include: number, angles, fractions, two-dimensional shapes, decimals, statistics, directed numbers, graphs, measurement, perimeter and area, algebraic, three-dimensional shapes, ratio and proportion, pythagoras' theorem and trigonometry, indices and standard form.
The Self-Taught Computer Scientist
by
The follow-up to Cory Althoff's bestselling The Self-Taught Programmer, which inspired hundreds of thousands of professionals to learn to program outside of school! Fresh out of college and with just a year of self-study behind him, Cory Althoff was offered a dream first job as a software engineer for a well-known tech company, but he quickly found himself overwhelmed by the amount of things he needed to know, but hadn't learned yet. This experience combined with his personal journey learning to program inspired his widely praised guide, The Self-Taught Programmer. Now Cory's back with another guide for the self-taught community of learners focusing on the foundations of computer science. The Self-Taught Computer Scientist introduces beginner and self-taught programmers to computer science fundamentals that are essential for success in programming and software engineering fields. Computer science is a massive subject that could cover an entire lifetime of learning. This book does not aim to cover everything you would learn about if you went to school to get a computer science degree. Instead, Cory's goal is to give you an introduction to some of the most important concepts in computer science that apply to a programming career. With a focus on data structures and algorithms, The Self-Taught Computer Scientist helps you fill gaps in your knowledge, prepare for a technical interview, feel knowledgeable and confident on the job, and ultimately, become a better programmer. Learn different algorithms including linear and binary search and test your knowledge with feedback loops Understand what a data structure is and study arrays, linked lists, stacks, queues, hash tables, binary trees, binary heaps, and graphs Prepare for technical interviews and feel comfortable working with more experienced colleagues Discover additional resources and tools to expand your skillset and continue your learning journey It's as simple as this: You have to study computer science if you want to become a successful programmer, and if you don't understand computer science, you won't get hired. Ready for a career in programming, coding, or software engineering and willing to embrace an "always be learning" mindset? The Self-Taught Computer Scientist is for you.
Essential Math for Data Science
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Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
Managing Your Individual Computing Project 2nd Edition
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