Jesus Rogel-Salazar – författare

This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.

This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.Features:Targets readers with a background in programming, who are interested in the tools used in data analytics and data scienceUses Python throughoutPresents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needsFocuses on the practical use of the tools rather than on lengthy explanationsProvides the reader with the opportunity to use the book whenever needed rather than following a sequential pathThe book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book.Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app.About the AuthorDr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.

Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.

Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.

Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.

The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today's AI-driven world.Jesus Rogel-Salazar is a lead data scientist, founder of RogueLoop, working for companies such as Ortus Technologies, Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Imperial College Business School, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today's AI-driven world.Jesus Rogel-Salazar is a lead data scientist, founder of RogueLoop, working for companies such as Ortus Technologies, Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Imperial College Business School, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Learn Two Popular Programming Languages in a Single VolumeWidely used by scientists and engineers, well-established MATLAB and open-source Octave are similar software programs providing excellent capabilities for data analysis, visualization, and more. By means of straightforward explanations and examples from different areas in mathematics, engineering, finance, and physics, Essential MATLAB and Octave explains how MATLAB and Octave are powerful tools applicable to a variety of problems. This text provides an introduction that reveals basic structures and syntax, demonstrates the use of functions and procedures, outlines availability in various platforms, and highlights the most important elements for both programs. Effectively Implement Models and Prototypes Using Computational ModelsThis text requires no prior knowledge. Self-contained, it allows the reader to use the material whenever needed rather than follow a particular order. Compatible with both languages, the book material incorporates commands and structures that allow the reader to gain a greater awareness of MATLAB and Octave, write their own code, and implement their scripts and programs within a variety of applicable fields. It is always made clear when particular examples apply only to MATLAB or only to Octave, allowing the book to be used flexibly depending on readers requirements.Includes brief, simple code that works in both MATLAB and Octave Provides exercise sections at the end of each chapter Introduces framed examples and discussions with a scientific twist Exercises are provided at the end of each chapterEssential MATLAB and Octave offers an introductory course in MATLAB and Octave programming and is an authoritative resource for students in phy

Learn Two Popular Programming Languages in a Single VolumeWidely used by scientists and engineers, well-established MATLAB® and open-source Octave are similar software programs providing excellent capabilities for data analysis, visualization, and more. By means of straightforward explanations and examples from different areas in mathematics, engineering, finance, and physics, Essential MATLAB and Octave explains how MATLAB and Octave are powerful tools applicable to a variety of problems. This text provides an introduction that reveals basic structures and syntax, demonstrates the use of functions and procedures, outlines availability in various platforms, and highlights the most important elements for both programs. Effectively Implement Models and Prototypes Using Computational ModelsThis text requires no prior knowledge. Self-contained, it allows the reader to use the material whenever needed rather than follow a particular order. Compatible with both languages, the book material incorporates commands and structures that allow the reader to gain a greater awareness of MATLAB and Octave, write their own code, and implement their scripts and programs within a variety of applicable fields. It is always made clear when particular examples apply only to MATLAB or only to Octave, allowing the book to be used flexibly depending on readers’ requirements. Includes brief, simple code that works in both MATLAB and OctaveProvides exercise sections at the end of each chapterIntroduces framed examples and discussions with a scientific twistExercises are provided at the end of each chapter Essential MATLAB and Octaveoffers an introductory course in MATLAB and Octave programming and is an authoritative resource for students in physics, mathematics, statistics, engineering, and any other subjects that require the use of computers to solve numerical problems.

Learn Two Popular Programming Languages in a Single VolumeWidely used by scientists and engineers, well-established MATLAB and open-source Octave are similar software programs providing excellent capabilities for data analysis, visualization, and more. By means of straightforward explanations and examples from different areas in mathematics, engine

Learn Two Popular Programming Languages in a Single VolumeWidely used by scientists and engineers, well-established MATLAB and open-source Octave are similar software programs providing excellent capabilities for data analysis, visualization, and more. By means of straightforward explanations and examples from different areas in mathematics, engine

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike.The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book.Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book.About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.Features:Targets readers with a background in programming, who are interested in the tools used in data analytics and data scienceUses Python throughoutPresents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needsFocuses on the practical use of the tools rather than on lengthy explanationsProvides the reader with the opportunity to use the book whenever needed rather than following a sequential pathThe book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book.Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app.About the AuthorDr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.