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Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
Herodotus' Histories are the primary source for the conflict between Greece and the enormous Persian Empire in the fifth century BC. Book VII begins after the defeat of the first Persian invasion by Athens at the Battle of Marathon and covers the Persian decision to launch the second invasion through to the first encounter of its army with a small but determined Greek force at Thermopylai. After fierce resistance, the Greeks are outflanked and surrounded, and the rearguard is massacred. The story of the battle passed rapidly into legend and has exercised a profound and lasting influence on the imagination across the world. Book VII merges many of the central themes of the Histories and is arguably Herodotus' most sustained engagement with Greek epic, whilst also rich in ethnographic and geographical detail. This edition provides all the linguistic help and historical background required by students to read and appreciate it.
Emphasizing how and why machine learning algorithms work, this introductory textbook bridges the gap between the theoretical foundations of machine learning and its practical algorithmic and code-level implementation. Over 85 thorough worked examples, in both Matlab and Python, demonstrate how algorithms are implemented and applied whilst illustrating the end result. Over 75 end-of-chapter problems empower students to develop their own code to implement these algorithms, equipping them with hands-on experience. Matlab coding examples demonstrate how a mathematical idea is converted from equations to code, and provide a jumping off point for students, supported by in-depth coverage of essential mathematics including multivariable calculus, linear algebra, probability and statistics, numerical methods, and optimization. Accompanied online by instructor lecture slides, downloadable Python code and additional appendices, this is an excellent introduction to machine learning for senior undergraduate and graduate students in Engineering and Computer Science.
This book provides a clear and accessible introduction to ring theory for undergraduate students. Aligned with standard curricula, it simplifies abstract concepts through structured explanations, practical examples, and real-world applications. Ideal for both students and instructors, it serves as a valuable resource for mastering fundamental concepts in ring theory with ease. The text begins with an introduction to rings and goes on to cover subrings, integral domains, ideals, and factor rings. It also discusses ring homomorphisms and polynomial rings. The book concludes with topics such as polynomial factorization and divisibility in integral domains. Each chapter is supplemented with solved examples to foster a deeper understanding of the subject. A set of practice questions is also provided to sharpen problem-solving skills.
Designed for undergraduate students of computer science, mathematics, and engineering, this book provides the tools and understanding needed to master graph theory and algorithms. It offers a strong theoretical foundation, detailed pseudocodes, and a range of real-world and illustrative examples to bridge the gap between abstract concepts and practical applications. Clear explanations and chapter-wise exercises support ease of comprehension for learners. The text begins with the basic properties of graphs and progresses to topics such as trees, connectivity, and distances in graphs. It also covers Eulerian and Hamiltonian graphs, matchings, planar graphs, and graph colouring. The book concludes with discussions on independent sets, the Ramsey theorem, directed graphs and networks. Concepts are introduced in a structured manner, with appropriate context and support from mathematical language and diagrams. Algorithms are explained through rules, reasoning, pseudocode, and relevant examples.
This textbook offers students who have no prior background in biblical studies an understanding of the lasting contribution of Israel's scriptures. Bringing a literary approach to the topic, it strikes a balance between historical reconstructions, comparative religions, and theology. Among several distinctive features, It traces the legacy of monotheism first emerging in the pages of Israel's scriptures as an enduring contribution for twenty-first century readers. Monotheism gives the volume an immediate relevance because the so-called Abrahamic religions are rooted in this concept. Whether one is Jewish, Christian, Muslim, or secularist, students will gain a new understanding of the origins of monotheism as their common heritage. The Second Edition of this textbook includes expanded discussions within the text and in sidebars, notably on the history of biblical scholarship, modern methods of interpretation, and wisdom literature.
This undergraduate biological psychology textbook offers a critical introduction to brain and behavior. Psychology lectures open with 'the brain is the most complex and mysterious object in the universe', only to quickly reduce that complexity by teaching simplified models. This textbook challenges these narratives by focusing on the latest neurotechnological advances, to clarify the limits of current models, and to inspire the development of safe and accessible technologies for human use. Its central aim is to promote critical thinking and inspire students to pose novel research questions that build from current advances. It is an ideal textbook for instructors who are eager to push beyond a conventional introductory curriculum. Beautifully illustrated and full of practical applications, it is accompanied by teaching slides and a test bank.
This chapter introduces venture debt (VD), a little-known yet critical source of funding for a specific group of expanding and later-stage companies. Throughout a company’s life cycle, a myriad of funding options is accessible, ranging from equity to debt-based sources. In the nascent stages of startups, innovative funding sources such as business angel funding, crowdfunding, and initial coin offerings have gained prominence in recent years. Despite these advancements, a noticeable funding gap persists during the critical scale-up phase, when startups require capital to grow and internationalize their venture. Venture debt has emerged as a tailored solution, specifically designed to bridge this gap in scale-up financing and offering a lifeline to companies striving for growth but not yet eligible for traditional bank financing. One VD fund manager focused on European companies said: ‘What we have seen over the last decades is that entrepreneurs are getting more educated about how to start businesses and, more importantly, how to grow businesses. The more sophisticated entrepreneurs prefer VD because it is less dilutive, and they do not have to grant board seats to us like they do with the venture capitalists.’
This chapter considers three sources of early funding and support for new ventures. As will be apparent, these sources of early funding have only presented themselves since the early 2000s; judging from their adoption, they have resonated well, especially in the European context.
Entrepreneurs tend to recognize market opportunities on a fairly regular basis and are optimistic about their market potential. If they are serious about pursuing a perceived opportunity, capital is needed to transform the opportunity into a proposition that can be brought to the market. The challenge to obtain this capital is to generate proof of the added value of the proposition early on. That is where the sources addressed in this chapter come in.
This chapter will provide an overview of impact investing and how to finance social entrepreneurs. Social entrepreneurship is emerging at the intersection of three sectors: the public sector, the private sector, and the third sector (including non-profit organizations and civil society). It challenges the perceived boundaries between sectors and provides innovative solutions for social needs that are not adequately dealt with by public authorities, businesses, or traditional non-profit organizations. We view social entrepreneurship as a field of practice and social entrepreneurs as the individuals who set up and manage social enterprises. Social enterprises can adopt various legal forms and can be characterized by pursuing a social mission while competing in the market economy. In this chapter we refer to social enterprises as the organizations in which impact investors invest. They can pursue social and/or environmental objectives.
Investments in early-stage ventures are characterized by being private and by their ‘equity nature’, as discussed in the previous chapters, stressing the mutual dependence between investor and entrepreneur. Moreover, the investment is typically temporary and done between so-called ‘perfect strangers’ – that is, both parties have large information asymmetry and are faced with agency challenges yet will be condemned to one another because of the illiquid nature of the investment.
Investors, such as venture capitalists and business angels, should have the exit and return on their investment in mind from the outset. The main goal of this chapter is to discuss how investors in entrepreneurial firms time the exit of their investments and choose between different exit routes.
This chapter will provide insights on an alternative path to entrepreneurship: entrepreneurship through acquisition. Rather than starting a company from scratch, you can acquire an existing company through a management buy-in or management buyout, or even buy a company in distress and use that company as a platform to pursue your entrepreneurial ambitions. Given the number of companies that change ownership every year and the risks involved in starting from scratch, for many individuals this might be a worthwhile route to consider when aspiring to entrepreneurship. The process of buying a company requires a special skill set and mindset that differ from the traditional startup method. We also describe the situation whereby the entrepreneur would acquire a company in a distress situation, having experienced hard times, whether self-inflicted or the result of external conditions not entirely managed or manageable. The main goal of this chapter is to provide you with a step-by-step guide to the key questions to consider when buying a company.
Corporate venture capital (CVC) is defined as equity investment by an established corporation in entrepreneurial ventures. CVC investors play an important role in the entrepreneurial ecosystem, accounting for one-fifth of total funding disbursed and a substantially higher fraction in specific sectors and geographies. As the name suggests, the practice consists of established corporations that set out to act as venture capitalists and fund entrepreneurs who develop and scale innovative technologies and solutions. Similar to venture capital (VC) investors, CVC investors receive an equity stake in return for the provision of capital. A major distinction concerns the motivation and structure of corporate investors. Financial returns are an essential consideration for CVC investors, yet often strategic objectives motivate the parent corporation and its CVC activities. Moreover, there is substantial variation in the structure of corporate investors. While most VC investors follow a limited partner–general partner (LP–GP) structure, different corporate investors have different structures.