Advertisement
| berkeley online master data science: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| berkeley online master data science: Why We Sleep Matthew Walker, 2017-10-03 Sleep is one of the most important but least understood aspects of our life, wellness, and longevity ... An explosion of scientific discoveries in the last twenty years has shed new light on this fundamental aspect of our lives. Now ... neuroscientist and sleep expert Matthew Walker gives us a new understanding of the vital importance of sleep and dreaming--Amazon.com. |
| berkeley online master data science: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| berkeley online master data science: Cognitive Surplus Clay Shirky, 2010-06-10 The author of the breakout hit Here Comes Everybody reveals how new technology is changing us for the better. In his bestselling Here Comes Everybody, Internet guru Clay Shirky provided readers with a much-needed primer for the digital age. Now, with Cognitive Surplus, he reveals how new digital technology is unleashing a torrent of creative production that will transform our world. For the first time, people are embracing new media that allow them to pool their efforts at vanishingly low cost. The results of this aggregated effort range from mind-expanding reference tools like Wikipedia to life-saving Web sites like Ushahidi.com, which allows Kenyans to report acts of violence in real time. Cognitive Surplus explores what's possible when people unite to use their intellect, energy, and time for the greater good. |
| berkeley online master data science: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns. |
| berkeley online master data science: The Promise of Access Daniel Greene, 2021 Based on fieldwork at three distinct sites in Washington, DC, this book finds that the persistent problem of poverty is often framed as a problem of technology-- |
| berkeley online master data science: Business Trends in Practice Bernard Marr, 2021-11-15 WINNER OF THE BUSINESS BOOK OF THE YEAR AWARD 2022! Stay one step ahead of the competition with this expert review of the most impactful and disruptive business trends coming down the pike Far from slowing down, change and transformation in business seems to come only at a more and more furious rate. The last ten years alone have seen the introduction of groundbreaking new trends that pose new opportunities and challenges for leaders in all industries. In Business Trends in Practice: The 25+ Trends That Are Redefining Organizations, best-selling business author and strategist Bernard Marr breaks down the social and technological forces underlying these rapidly advancing changes and the impact of those changes on key industries. Critical consumer trends just emerging today—or poised to emerge tomorrow—are discussed, as are strategies for rethinking your organisation’s product and service delivery. The book also explores: Crucial business operations trends that are changing the way companies conduct themselves in the 21st century The practical insights and takeaways you can glean from technological and social innovation when you cut through the hype Disruptive new technologies, including AI, robotic and business process automation, remote work, as well as social and environmental sustainability trends Business Trends in Practice: The 25+ Trends That Are Redefining Organizations is a must-read resource for executives, business leaders and managers, and business development and innovation leads trying to get – and stay – on top of changes and disruptions that are right around the corner. |
| berkeley online master data science: High-Dimensional Data Analysis with Low-Dimensional Models John Wright, Yi Ma, 2022-01-13 Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès. |
| berkeley online master data science: Born to Be Good: The Science of a Meaningful Life Dacher Keltner, 2009-10-05 “A landmark book in the science of emotions and its implications for ethics and human universals.”—Library Journal, starred review In this startling study of human emotion, Dacher Keltner investigates an unanswered question of human evolution: If humans are hardwired to lead lives that are “nasty, brutish, and short,” why have we evolved with positive emotions like gratitude, amusement, awe, and compassion that promote ethical action and cooperative societies? Illustrated with more than fifty photographs of human emotions, Born to Be Good takes us on a journey through scientific discovery, personal narrative, and Eastern philosophy. Positive emotions, Keltner finds, lie at the core of human nature and shape our everyday behavior—and they just may be the key to understanding how we can live our lives better. Some images in this ebook are not displayed owing to permissions issues. |
| berkeley online master data science: Getting Mentored in Graduate School W. Brad Johnson, Jennifer M. Huwe, 2003 Getting Mentored in Graduate School is the first guide to mentoring relationships written exclusively for graduate students. Research has shown that students who are mentored enjoy many benefits, including better training, greater career success, and a stronger professional identity. Authors Johnson and Huwe draw directly from their own experiences as mentor and protege to advise students on finding a mentor and maintaining the mentor relationship throughout graduate school. Conversational, accessible, and informative, this book offers practical strategies that can be employed not only by students pursuing mentorships but also by professors seeking to improve their mentoring skills. Johnson and Huwe arm readers with the tools they need to anticipate and prevent common pitfalls and to resolve problems that may arise in mentoring relationships. This book is essential reading for students who want to learn and master the unwritten rules that lead to finding a mentor and getting more from graduate school and your career. |
| berkeley online master data science: The Charisma Machine Morgan G. Ames, 2019-11-19 A fascinating examination of technological utopianism and its complicated consequences. In The Charisma Machine, Morgan Ames chronicles the life and legacy of the One Laptop per Child project and explains why—despite its failures—the same utopian visions that inspired OLPC still motivate other projects trying to use technology to “disrupt” education and development. Announced in 2005 by MIT Media Lab cofounder Nicholas Negroponte, One Laptop per Child promised to transform the lives of children across the Global South with a small, sturdy, and cheap laptop computer, powered by a hand crank. In reality, the project fell short in many ways—starting with the hand crank, which never materialized. Yet the project remained charismatic to many who were captivated by its claims of access to educational opportunities previously out of reach. Behind its promises, OLPC, like many technology projects that make similarly grand claims, had a fundamentally flawed vision of who the computer was made for and what role technology should play in learning. Drawing on fifty years of history and a seven-month study of a model OLPC project in Paraguay, Ames reveals that the laptops were not only frustrating to use, easy to break, and hard to repair, they were designed for “technically precocious boys”—idealized younger versions of the developers themselves—rather than the children who were actually using them. The Charisma Machine offers a cautionary tale about the allure of technology hype and the problems that result when utopian dreams drive technology development. |
| berkeley online master data science: Pragmatic AI Noah Gift, 2018-07-12 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| berkeley online master data science: How to Be a High School Superstar Cal Newport, 2010-07-27 Do Less, Live More, Get Accepted What if getting into your reach schools didn’t require four years of excessive A.P. classes, overwhelming activity schedules, and constant stress? In How to Be a High School Superstar, Cal Newport explores the world of relaxed superstars—students who scored spots at the nation’s top colleges by leading uncluttered, low stress, and authentic lives. Drawing from extensive interviews and cutting-edge science, Newport explains the surprising truths behind these superstars’ mixture of happiness and admissions success, including: · Why doing less is the foundation for becoming more impressive. · Why demonstrating passion is meaningless, but being interesting is crucial. · Why accomplishments that are hard to explain are better than accomplishments that are hard to do. These insights are accompanied by step-by-step instructions to help any student adopt the relaxed superstar lifestyle—proving that getting into college doesn’t have to be a chore to survive, but instead can be the reward for living a genuinely interesting life. |
| berkeley online master data science: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| berkeley online master data science: Innovation Engineering Ikhlaq Sidhu, 2019-09-12 Innovation Engineering is a practical guide to creating anything new - whether in a large firm, research lab, new venture or even in an innovative student project. As an executive, are you happy with the return on investment of your innovative projects? As an innovator, do you feel confident that you can navigate obstacles and achieve success with your innovative project? The reality is that most innovation projects fail. The challenge in developing any new technology, application, or venture is that the innovator must be able to execute while also learning. Innovation Engineering, developed and used at UC Berkeley, provides the tactical process, leadership, and behaviors necessary for successful innovation projects. Our validation tests have shown that teams which properly use Innovation Engineering accomplished their innovative projects approximately 4X faster than and with higher quality results. They also on-board new team members faster, they have much fewer unnecessary meetings, and they even report a more positive outlook on the project itself. Inter-woven between the chapters are real-life case studies with some of the world's most successful innovators to provide context, patterns, and playbooks that you can follow. Highly applied, and very realistic, Innovation Engineering builds on 30 years of technology innovation projects within large firms, advanced development labs, and new ventures at UC Berkeley, in Silicon Valley, and globally. If your goal is to create something new and have it successfully used in real life, this book is for you. |
| berkeley online master data science: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
| berkeley online master data science: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| berkeley online master data science: Learning How to Learn Barbara Oakley, PhD, Terrence Sejnowski, PhD, Alistair McConville, 2018-08-07 A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course Learning How to Learn have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid rut think in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun. |
| berkeley online master data science: How I Became a Quant Richard R. Lindsey, Barry Schachter, 2011-01-11 Praise for How I Became a Quant Led by two top-notch quants, Richard R. Lindsey and Barry Schachter, How I Became a Quant details the quirky world of quantitative analysis through stories told by some of today's most successful quants. For anyone who might have thought otherwise, there are engaging personalities behind all that number crunching! --Ira Kawaller, Kawaller & Co. and the Kawaller Fund A fun and fascinating read. This book tells the story of how academics, physicists, mathematicians, and other scientists became professional investors managing billions. --David A. Krell, President and CEO, International Securities Exchange How I Became a Quant should be must reading for all students with a quantitative aptitude. It provides fascinating examples of the dynamic career opportunities potentially open to anyone with the skills and passion for quantitative analysis. --Roy D. Henriksson, Chief Investment Officer, Advanced Portfolio Management Quants--those who design and implement mathematical models for the pricing of derivatives, assessment of risk, or prediction of market movements--are the backbone of today's investment industry. As the greater volatility of current financial markets has driven investors to seek shelter from increasing uncertainty, the quant revolution has given people the opportunity to avoid unwanted financial risk by literally trading it away, or more specifically, paying someone else to take on the unwanted risk. How I Became a Quant reveals the faces behind the quant revolution, offering you?the?chance to learn firsthand what it's like to be a?quant today. In this fascinating collection of Wall Street war stories, more than two dozen quants detail their roots, roles, and contributions, explaining what they do and how they do it, as well as outlining the sometimes unexpected paths they have followed from the halls of academia to the front lines of an investment revolution. |
| berkeley online master data science: Search User Interfaces Marti A. Hearst, 2009-09-21 The truly world-wide reach of the Web has brought with it a new realisation of the enormous importance of usability and user interface design. In the last ten years, much has become understood about what works in search interfaces from a usability perspective, and what does not. Researchers and practitioners have developed a wide range of innovative interface ideas, but only the most broadly acceptable make their way into major web search engines. This book summarizes these developments, presenting the state of the art of search interface design, both in academic research and in deployment in commercial systems. Many books describe the algorithms behind search engines and information retrieval systems, but the unique focus of this book is specifically on the user interface. It will be welcomed by industry professionals who design systems that use search interfaces as well as graduate students and academic researchers who investigate information systems. |
| berkeley online master data science: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| berkeley online master data science: Recent Advances in Information Systems and Technologies Álvaro Rocha, Ana Maria Correia, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, 2017-03-28 This book presents a selection of papers from the 2017 World Conference on Information Systems and Technologies (WorldCIST'17), held between the 11st and 13th of April 2017 at Porto Santo Island, Madeira, Portugal. WorldCIST is a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences and challenges involved in modern Information Systems and Technologies research, together with technological developments and applications. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Software and Systems Modeling; Software Systems, Architectures, Applications and Tools; Multimedia Systems and Applications; Computer Networks, Mobility and Pervasive Systems; Intelligent and Decision Support Systems; Big Data Analytics and Applications; Human–Computer Interaction; Ethics, Computers & Security; Health Informatics; Information Technologies in Education; and Information Technologies in Radiocommunications. |
| berkeley online master data science: Central Park Love Song Stephen Wolf, 2018-02 Through an imaginative blend of personal memoir and meticulous research, Central Park Love Song tells the remarkable story of America's first great public park and the city that needed and created it. |
| berkeley online master data science: Risk Terrain Modeling Joel M. Caplan, Leslie W. Kennedy, 2016-06-28 Imagine using an evidence-based risk management model that enables researchers and practitioners alike to analyze the spatial dynamics of crime, allocate resources, and implement custom crime and risk reduction strategies that are transparent, measurable, and effective. Risk Terrain Modeling (RTM) diagnoses the spatial attractors of criminal behavior and makes accurate forecasts of where crime will occur at the microlevel. RTM informs decisions about how the combined factors that contribute to criminal behavior can be targeted, connections to crime can be monitored, spatial vulnerabilities can be assessed, and actions can be taken to reduce worst effects. As a diagnostic method, RTM offers a statistically valid way to identify vulnerable places. To learn more, visit http://www.riskterrainmodeling.com and begin using RTM with the many free tutorials and resources. |
| berkeley online master data science: The Practice of Reproducible Research Justin Kitzes, Daniel Turek, Fatma Deniz, 2018 The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. In each of the thirty-one case studies in this volume, the author or team describes the workflow that they used to complete a real-world research project. Authors highlight how they utilized particular tools, ideas, and practices to support reproducibility, emphasizing the very practical how, rather than the why or what, of conducting reproducible research. Part 1 provides an accessible introduction to reproducible research, a basic reproducible research project template, and a synthesis of lessons learned from across the thirty-one case studies. Parts 2 and 3 focus on the case studies themselves. The Practice of Reproducible Research is an invaluable resource for students and researchers who wish to better understand the practice of data-intensive sciences and learn how to make their own research more reproducible. |
| berkeley online master data science: Analytics, Data Science, and Artificial Intelligence Ramesh Sharda, Dursun Delen, Efraim Turban, 2020-03-06 For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT. |
| berkeley online master data science: Big Data Science & Analytics Arshdeep Bahga, Vijay Madisetti, 2016-04-15 Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. We have written this textbook to meet this need at colleges and universities, and also for big data service providers. |
| berkeley online master data science: Python for DevOps Noah Gift, Kennedy Behrman, Alfredo Deza, Grig Gheorghiu, 2019-12-12 Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform. Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to get stuff done in Python? This is your guide. Python foundations, including a brief introduction to the language How to automate text, write command-line tools, and automate the filesystem Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing Cloud computing, infrastructure as code, Kubernetes, and serverless Machine learning operations and data engineering from a DevOps perspective Building, deploying, and operationalizing a machine learning project |
| berkeley online master data science: Algorithms Sanjoy Dasgupta, Christos H. Papadimitriou, Umesh Virkumar Vazirani, 2006 This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. Features include:The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated. Carefully chosen advanced topics that can be skipped in a standard one-semester course but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text DasGupta also offers a Solutions Manual which is available on the Online Learning Center.Algorithms is an outstanding undergraduate text equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel it is a joy to read. Tim Roughgarden Stanford University |
| berkeley online master data science: The 9 Pitfalls of Data Science Gary Smith, Jay Cordes, 2019 The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. |
| berkeley online master data science: Human Compatible Stuart Jonathan Russell, 2019 A leading artificial intelligence researcher lays out a new approach to AI that will enable people to coexist successfully with increasingly intelligent machines. |
| berkeley online master data science: Theoretical Statistics Robert W. Keener, 2010-09-08 Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential. The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis. The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix. |
| berkeley online master data science: Practical Data Science with R Nina Zumel, John Mount, 2014-04-10 Summary Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed. What's Inside Data science for the business professional Statistical analysis using the R language Project lifecycle, from planning to delivery Numerous instantly familiar use cases Keys to effective data presentations About the Authors Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com. Table of Contents PART 1 INTRODUCTION TO DATA SCIENCE The data science process Loading data into R Exploring data Managing data PART 2 MODELING METHODS Choosing and evaluating models Memorization methods Linear and logistic regression Unsupervised methods Exploring advanced methods PART 3 DELIVERING RESULTS Documentation and deployment Producing effective presentations |
| berkeley online master data science: Machine Learning and AI for Healthcare Arjun Panesar, 2019-02-04 Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings. |
| berkeley online master data science: Quicksort Robert Sedgewick, 1980 |
| berkeley online master data science: Elegant SciPy Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 2017-08-11 Welcome to Scientific Python and its community. If you’re a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You’ll learn how to write elegant code that’s clear, concise, and efficient at executing the task at hand. Throughout the book, you’ll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you’ll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries. Explore the NumPy array, the data structure that underlies numerical scientific computation Use quantile normalization to ensure that measurements fit a specific distribution Represent separate regions in an image with a Region Adjacency Graph Convert temporal or spatial data into frequency domain data with the Fast Fourier Transform Solve sparse matrix problems, including image segmentations, with SciPy’s sparse module Perform linear algebra by using SciPy packages Explore image alignment (registration) with SciPy’s optimize module Process large datasets with Python data streaming primitives and the Toolz library |
| berkeley online master data science: How to Make Data Work Jenny Grant Rankin, 2016-01-22 Educators are increasingly responsible for using data to improve teaching and learning in their schools. This helpful guide provides leaders with simple steps for facilitating accurate analysis and interpretation of data, while avoiding common errors and pitfalls. How to Make Data Work provides clear strategies for getting data into workable shape and creating an environment that supports understanding, analysis, and successful use of data, no matter what data system or educational technology tools are in place in your district. This accessible resource makes data easy to understand and use so that educators can better evaluate and maximize their systems to help their staff, students, and school succeed. With this tried-and-true guidance, you’ll be prepared to advocate for tools that adhere to data reporting standards, avoid misinterpretation of data, and improve the data use climate in your school. |
| berkeley online master data science: Privacy on the Ground Kenneth A. Bamberger, Deirdre K. Mulligan, 2024-05-28 An examination of corporate privacy management in the United States, Germany, Spain, France, and the United Kingdom, identifying international best practices and making policy recommendations. Barely a week goes by without a new privacy revelation or scandal. Whether by hackers or spy agencies or social networks, violations of our personal information have shaken entire industries, corroded relations among nations, and bred distrust between democratic governments and their citizens. Polls reflect this concern, and show majorities for more, broader, and stricter regulation—to put more laws “on the books.” But there was scant evidence of how well tighter regulation actually worked “on the ground” in changing corporate (or government) behavior—until now. This intensive five-nation study goes inside corporations to examine how the people charged with protecting privacy actually do their work, and what kinds of regulation effectively shape their behavior. And the research yields a surprising result. The countries with more ambiguous regulation—Germany and the United States—had the strongest corporate privacy management practices, despite very different cultural and legal environments. The more rule-bound countries—like France and Spain—trended instead toward compliance processes, not embedded privacy practices. At a crucial time, when Big Data and the Internet of Things are snowballing, Privacy on the Ground helpfully searches out the best practices by corporations, provides guidance to policymakers, and offers important lessons for everyone concerned with privacy, now and in the future. |
| berkeley online master data science: Cybersecurity Data Science Scott Mongeau, Andrzej Hajdasinski, 2021-10-01 This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography. |
| berkeley online master data science: The Algorithm Design Manual Steven S Skiena, 2009-04-05 This newly expanded and updated second edition of the best-selling classic continues to take the mystery out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW war stories relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java |
Home - University of California, Berkeley
Berkeley is home to some of the world’s greatest minds leading more than 130 academic departments and 80 interdisciplinary research units and addressing the world’s most pertinent …
Admissions - University of California, Berkeley
The University of California, Berkeley, is the No. 1 public university in the world. Over 40,000 students attend classes in 15 colleges and schools, offering over 300 degree programs. Set …
Real-Time Interactive Earthquake Map - University of California, …
Legend. Earthquakes are shown as circles sized by magnitude (red, < 1 hour; blue, < 1 day, yellow, < 1 week). Click or tap on a circle to view more details about an earthquake, such as …
Home - Office of Undergraduate Admissions
Start your UC Berkeley journey by learning more about the basic admissions requirements that make Berkeley students stand out.
2024-25 Berkeley Academic Guide | Berkeley Academic Guide
Compare programs, find detailed degree requirements, discover faculty research specialties, and learn more about the unparalleled academic opportunities available to you at UC Berkeley.
Welcome to the UC Berkeley Library
The UC Berkeley Library helps current and future users find, evaluate, use and create knowledge to better the world.
Academics - University of California, Berkeley
Berkeley offers over 300 degree programs through its academic departments. Search with our A to Z guide.
Research - University of California, Berkeley
From expeditions to Egypt in the late 1800s to stem cell research and artificial intelligence today, Berkeley has been at the forefront of research throughout its history. Here students can work …
Schools & colleges - University of California, Berkeley
The Berkeley School of Education includes master’s and doctoral programs, teacher preparation, undergraduate minor program and leadership training.
Berkeley Global | UC Berkeley - University of California, Berkeley
Study abroad at UC Berkeley. Immerse yourself in campus life for a semester or academic year.
Home - University of California, Berkeley
Berkeley is home to some of the world’s greatest minds leading more than 130 academic departments and 80 interdisciplinary research units and addressing the world’s most pertinent …
Admissions - University of California, Berkeley
The University of California, Berkeley, is the No. 1 public university in the world. Over 40,000 students attend classes in 15 colleges and schools, offering over 300 degree programs. Set the …
Real-Time Interactive Earthquake Map - University of California, …
Legend. Earthquakes are shown as circles sized by magnitude (red, < 1 hour; blue, < 1 day, yellow, < 1 week). Click or tap on a circle to view more details about an earthquake, such as location, …
Home - Office of Undergraduate Admissions
Start your UC Berkeley journey by learning more about the basic admissions requirements that make Berkeley students stand out.
2024-25 Berkeley Academic Guide | Berkeley Academic Guide
Compare programs, find detailed degree requirements, discover faculty research specialties, and learn more about the unparalleled academic opportunities available to you at UC Berkeley.
Welcome to the UC Berkeley Library
The UC Berkeley Library helps current and future users find, evaluate, use and create knowledge to better the world.
Academics - University of California, Berkeley
Berkeley offers over 300 degree programs through its academic departments. Search with our A to Z guide.
Research - University of California, Berkeley
From expeditions to Egypt in the late 1800s to stem cell research and artificial intelligence today, Berkeley has been at the forefront of research throughout its history. Here students can work …
Schools & colleges - University of California, Berkeley
The Berkeley School of Education includes master’s and doctoral programs, teacher preparation, undergraduate minor program and leadership training.
Berkeley Global | UC Berkeley - University of California, Berkeley
Study abroad at UC Berkeley. Immerse yourself in campus life for a semester or academic year.
