Kirk Borne

Revised and Updated, Featuring a New Case Study How do successful companies create products people can’t put down? Why do some products capture widespread attention while others flop? What makes us engage with certain products out of sheer habit? Is there a pattern underlying how technologies hook us?Nir Eyal answers these questions (and many more) by explaining the Hook Model—a four-step process embedded into the products of many successful companies to subtly encourage customer behavior. Through consecutive “hook cycles,” these products reach their ultimate goal of bringing users back again and again without depending on costly advertising or aggressive messaging. Hooked is based on Eyal’s years of research, consulting, and practical experience. He wrote the book he wished had been available to him as a start-up founder—not abstract theory, but a how-to guide for building better products. Hooked is written for product managers, designers, marketers, start-up founders, and anyone who seeks to understand how products influence our behavior. Eyal provides readers with: • Practical insights to create user habits that stick. • Actionable steps for building products people love. • Fascinating examples from the iPhone to Twitter, Pinterest to the Bible App, and many other habit-forming products.

Accelerate your journey to smarter decision making by mastering the fundamentals of data literacy and developing the mindset to work confidently with data Key Features: Get a solid grasp of data literacy fundamentals to support your next steps in your careerLearn how to work with data and extract meaningful insights to take the right actionsApply your knowledge to real-world business intelligence projects Book Description: Data is more than a mere commodity in our digital world. It is the ebb and flow of our modern existence. Individuals, teams, and enterprises working with data can unlock a new realm of possibilities. And the resultant agility, growth, and inevitable success have one origin-data literacy. This comprehensive guide is written by two data literacy pioneers, each with a thorough footprint within the data and analytics commercial world and lectures at top universities in the US and the Netherlands. Complete with best practices, practical models, and real-world examples, Data Literacy in Practice will help you start making your data work for you by building your understanding of data literacy basics and accelerating your journey to independently uncovering insights. You'll learn the four-pillar model that underpins all data and analytics and explore concepts such as measuring data quality, setting up a pragmatic data management environment, choosing the right graphs for your readers, and questioning your insights. By the end of the book, you'll be equipped with a combination of skills and mindset as well as with tools and frameworks that will allow you to find insights and meaning within your data for data-informed decision making. What You Will Learn: Start your data literacy journey with simple and actionable stepsApply the four-pillar model for organizations to transform data into insightsDiscover which skills you need to work confidently with dataVisualize data and create compelling visual data storiesMeasure, improve, and leverage your data to meet organizational goalsMaster the process of drawing insights, ask critical questions and action your insightsDiscover the right steps to take when you analyze insights Who this book is for: This book is for data analysts, data professionals, and data teams starting or wanting to accelerate their data literacy journey. If you're looking to develop the skills and mindset you need to work independently with data, as well as a solid knowledge base of the tools and frameworks, you'll find this book useful.

Learn the principles of quantum machine learning and how to apply them While focus is on financial use cases, all the methods and techniques are transferable to other fields Purchase of Print or Kindle includes a free eBook in PDF Key FeaturesDiscover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methodsUse methods of analogue and digital quantum computing to build powerful generative modelsCreate the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computersBook DescriptionWith recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware. Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware. This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm. This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun! What you will learnTrain parameterised quantum circuits as generative models that excel on NISQ hardwareSolve hard optimisation problemsApply quantum boosting to financial applicationsLearn how the variational quantum eigensolver and the quantum approximate optimisation algorithms workAnalyse the latest algorithms from quantum kernels to quantum semidefinite programmingApply quantum neural networks to credit approvalsWho this book is forThis book is for Quants and developers, data scientists, researchers, and students in quantitative finance. Although the focus is on financial use cases, all the methods and techniques are transferable to other areas. Table of ContentsThe Principles of Quantum MechanicsAdiabatic Quantum ComputingQuadratic Unconstrained Binary OptimisationQuantum BoostingQuantum Boltzmann MachineQubits and Quantum Logic GatesParameterised Quantum Circuits and Data EncodingQuantum Neural NetworkQuantum Circuit Born MachineVariational Quantum EigensolverQuantum Approximate Optimisation AlgorithmThe Power of Parameterised Quantum CircuitsLooking AheadBibliography

NEW YORK TIMES BESTSELLER “In our uncertain age, which can so often feel so dark and disturbing, Steven Pinker has distinguished himself as a voice of positivity.” – New York Times Can reading a book make you more rational? Can it help us understand why there is so much irrationality in the world? Steven Pinker, author of Enlightenment Now (Bill Gates’s "new favorite book of all time”) answers all the questions here Today humanity is reaching new heights of scientific understanding--and also appears to be losing its mind. How can a species that developed vaccines for Covid-19 in less than a year produce so much fake news, medical quackery, and conspiracy theorizing? Pinker rejects the cynical cliché that humans are simply irrational--cavemen out of time saddled with biases, fallacies, and illusions. After all, we discovered the laws of nature, lengthened and enriched our lives, and set out the benchmarks for rationality itself.  We actually think in ways that are sensible in the low-tech contexts in which we spend most of our lives, but fail to take advantage of the powerful tools of reasoning we’ve discovered over the millennia: logic, critical thinking, probability, correlation and causation, and optimal ways to update beliefs and commit to choices individually and with others. These tools are not a standard part of our education, and have never been presented clearly and entertainingly in a single book--until now. Rationality also explores its opposite: how the rational pursuit of self-interest, sectarian solidarity, and uplifting mythology can add up to crippling irrationality in a society. Collective rationality depends on norms that are explicitly designed to promote objectivity and truth. Rationality matters. It leads to better choices in our lives and in the public sphere, and is the ultimate driver of social justice and moral progress. Brimming with Pinker’s customary insight and humor, Rationality will enlighten, inspire, and empower.

From a bestselling geospatial author and data analyst, a bold framework for accessing the power of Python and  integrating insights from emergent datasets into agency around evolving questions about our built environment. Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Large Language Models are helpful with contextual basic programming but your task is to query the edges of ecology, economics, sustainability, infrastructure, social change, climate change, and the world writ large. The human element is tasked with stewardship of finite resources against exponential demands— perhaps it is why we evolved. In this no nonsense skill building book across a wide array of open source libraries, platforms and tools, let's work toward asking better questions. This book helps you:Explore geospatial integration with PythonUnderstand the importance of applying spatial relationships in data scienceSelect and apply data layering of both raster and vector graphicsApply location data to leverage spatial analyticsDesign informative and accurate mapsAutomate geographic data with Python scriptsExplore Python packages for additional functionalityWork with atypical data types such as polygons, shape files, and projectionsUnderstand the graphical syntax of spatial data science to stimulate curiosity

Explore supercharged machine learning techniques to take care of your data laundry loads Key Features: Learn how to prepare data for machine learning processesUnderstand which algorithms are based on prediction objectives and the properties of the dataExplore how to interpret and evaluate the results from machine learning Book Description: Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results. As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You'll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you'll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You'll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book. By the end of this book, you'll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering. What You Will Learn: Explore essential data cleaning and exploration techniques to be used before running the most popular machine learning algorithmsUnderstand how to perform preprocessing and feature selection, and how to set up the data for testing and validationModel continuous targets with supervised learning algorithmsModel binary and multiclass targets with supervised learning algorithmsExecute clustering and dimension reduction with unsupervised learning algorithmsUnderstand how to use regression trees to model a continuous target Who this book is for: This book is for professional data scientists, particularly those in the first few years of their career, or more experienced analysts who are relatively new to machine learning. Readers should have prior knowledge of concepts in statistics typically taught in an undergraduate introductory course as well as beginner-level experience in manipulating data programmatically.

As with the previous editions, this fourth edition relies on teaching by example and the Karplus Learning Cycle to convey the ideas of game theory in a way that is approachable, intuitive, and interdisciplinary. Noncooperative equilibrium concepts such as Nash equilibrium, mixed strategy equilibria, and subgame perfect equilibrium are systematically introduced in the first half of the book. Bayesian Nash equilibrium is briefly introduced. The subsequent chapters discuss cooperative solutions with and without side payments, rationalizable strategies and correlated equilibria, and applications to elections, social mechanism design, and larger-scale games. New examples include panic buying, supply-chain shifts in the pandemic, and global warming.

The first true account of computer espionage tells of a year-long single-handed hunt for a computer thief who sold information from American computer files to Soviet intelligence agents

Many researchers jump from data collection directly into testing hypothesis without realizing these tests can go profoundly wrong without clean data. This book provides a clear, accessible, step-by-step process of important best practices in preparing for data collection, testing assumptions, and examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of the handbook Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are evidence-based and will motivate change in practice by empirically demonstrating―for each topic―the benefits of following best practices and the potential consequences of not following these guidelines.

THE WALL STREET JOURNAL BESTSELLER with a foreword by the CIO of the NFL. “No matter how experienced you are, you will learn something important from this book!” —SPRINT Chief Digital Officer, Rob Roy Customers today expect the brands they deal with to deliver an increasingly outstanding and seamless digital experience. Those that do are thriving. Those that don't are becoming increasingly irrelevant. Executives charged with leading any aspect of digital face many challenges, which often include: • Organizational resistance, • Outdated technology, • Inadequate funding, • The wrong talent, and • Lack of alignment on what the vision for the future should be. All these challenges have solutions. Winning Digital Customers lays out a proven formula for transforming any company to thrive in this digital age. Howard Tiersky has been named one of the Top 10 Digital Transformation Influencers to follow today by IDG.As an entrepreneur, he has launched two successful companies that help large brands transform to thrive in the digital age. His dozens of Fortune 1000 clients have included Verizon, NBC, Viacom, Avis, Universal Studios, JPMC, Facebook, Spotify, and Amazon. In this new book, Tiersky lays out a simple but detailed five-step methodology that any company can follow to align their teams around a vision for the customer experience that will: • Maximize their competitiveness in the market, • Identify the quick wins that will help them out of the gate, and • Ultimately drive the transformation needed to bring their company into alignment with today's digital world. As part of that methodology, he shares a proven approach to integrating Design Thinking and Journey Mapping to more predictably drive business results. In the book's Foreword, written by Michelle McKenna, former Disney Executive, the technology leader behind The Wizarding World of Harry Potter, and the current CIO of the National Football League, McKenna says this about Tiersky and his approach: “Howard Tiersky has been my secret weapon every place I've been because he is, I think, one of the brightest, most collaborative and best creative thinkers I've ever worked with. I'm happy that he's now writing it all down and that others can now know what up until now has been known only to his clients. This book provides a very readable, but detailed, pragmatic guide to how to drive digital transformation in the ‘real world,' including both specific steps to follow and many anecdotes to put them in context. You will find methodologies, techniques and formerly top-secret tricks that can make a huge difference for you. Even if you've already hired the best agency or consultancy in the field, reading this book and applying its principles will help you understand and manage your transformation in a way that you get real sustainable change that can survive and thrive long after the last consultant leaves the building.”

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain. Quickly learn basic Python syntax, data types, and language conceptsWork with both machine-readable and human-consumable dataScrape websites and APIs to find a bounty of useful informationClean and format data to eliminate duplicates and errors in your datasetsLearn when to standardize data and when to test and script data cleanupExplore and analyze your datasets with new Python libraries and techniquesUse Python solutions to automate your entire data-wrangling process

BRAND NEW, Exactly same ISBN as listed, Please double check ISBN carefully before ordering.

Discover deep learning and computer vision with SAS!Deep Learning for Computer Vision with SAS®: An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations. Readers will learn how to: Define and understand deep learning Build models using deep learning techniques and SAS Viya Apply models to score (inference) new data Modify data for better analysis results Search the hyperparameter space of a deep learning model Leverage transfer learning using supervised and unsupervised methods

Jump-start your career as a data scientist―learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntaxDevelop queries to construct datasets for use in applications like interactive reports and machine learning algorithmsReview strategies and approaches so you can design analytical datasetsPractice your techniques with the provided database and SQL codeIn this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!

Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.

This book will make the link between data cleaning and preprocessing to help you design effective data analytic solutions Key Features: Develop the skills to perform data cleaning, data integration, data reduction, and data transformationGet ready to make the most of your data with powerful data transformation and massaging techniquesPerform thorough data cleaning, such as dealing with missing values and outliers Book Description: Data preprocessing is the first step in data visualization, data analytics, and machine learning, where data is prepared for analytics functions to get the best possible insights. Around 90% of the time spent on data analytics, data visualization, and machine learning projects is dedicated to performing data preprocessing. This book will equip you with the optimum data preprocessing techniques from multiple perspectives. You'll learn about different technical and analytical aspects of data preprocessing - data collection, data cleaning, data integration, data reduction, and data transformation - and get to grips with implementing them using the open source Python programming environment. This book will provide a comprehensive articulation of data preprocessing, its whys and hows, and help you identify opportunities where data analytics could lead to more effective decision making. It also demonstrates the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques; and handle outliers or missing values to effectively prepare data for analytic tools. What You Will Learn: Use Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformation Who this book is for: Junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data will find this book useful. Basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are assumed.

Develop skills in Python and Quantum Computing by implementing exciting algorithms, mathematical functions, classical searching, data analysis, plotting data, machine learning techniques, and quantum circuits. Key Features: Create quantum circuits and algorithms using Qiskit and run them on quantum computing hardware and simulatorsLearn the Pythonic way to write elegant and efficient codeDelve into Python's advanced features, including machine learning, analyzing data, and searching Book Description: Dancing with Python helps you learn Python and quantum computing in a practical way. It will help you explore how to work with numbers, strings, collections, iterators, and files. The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends traditional techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools, such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning. By the end of the book, you will be proficient in programming the latest and most powerful quantum computers, the Pythonic way. What You Will Learn: Explore different quantum gates and build quantum circuits with Qiskit and PythonWrite succinct code the Pythonic way using magic methods, iterators, and generatorsAnalyze data, build basic machine learning models, and plot the resultsSearch for information using the quantum Grover Search AlgorithmOptimize and test your code to run efficiently Who this book is for: The book will help you get started with coding for Python and Quantum Computing. Basic familiarity with algebra, geometry, trigonometry, and logarithms is required as the book does not cover the detailed mathematics and theory of quantum computing. You can check out the author's Dancing with Qubits book, also published by Packt, for an approachable and comprehensive introduction to quantum computing.

Why was this book written? Machine learning is a vast topic if you look at the various disciplines originating from it. You will also hear buzzwords such as AI, Neural Networks, Deep learning, AI Engineering being associated with machine learning. Our aim in this book is to demystify these concepts and provide clarity on how machine learning is different from conventional programming. And further, how machine learning can be used to gain an edge in the trading domain. We have structured the book in such a way that initially, you will learn about the various tasks carried out by a machine learning algorithm. When it is appropriate, you will be introduced to the code which is required to run these tasks. If you are well versed with Python programming, you will be able to breeze through these sections and understand the concepts easily. What’s in this book? The material presented here is an elementary introduction to the world of machine learning. You can think of it as a book telling you about the foundations of machine learning and how it is applied in real life. From the outset, we believe that only theory is not enough to retain knowledge. You need to know how you can apply this knowledge in the real world. Thus, our book contains lots of real-world examples, especially in the field of trading. But rest assured that these concepts can be transferred to any other discipline which requires data analysis.

[from the Preface] This introductory textbook in undergraduate probability emphasizes the inseparability between data (computing) and probability (theory) in our time. It examines the motivation, intuition, and implication of the probabilistic tools used in science and engineering: Motivation: In the ocean of mathematical definitions, theorems, and equations, why should we spend our time on this particular topic but not another?Intuition: When going through the deviations, is there a geometric interpretation or physics beyond those equations?Implication: After we have learned a topic, what new problems can we solve?