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| data science networking events: Data Analytics for IT Networks John Garrett, 2018-10-24 Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data |
| data science networking events: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data science networking events: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science networking events: Effective Data Storytelling Brent Dykes, 2019-12-10 Master the art and science of data storytelling—with frameworks and techniques to help you craft compelling stories with data. The ability to effectively communicate with data is no longer a luxury in today’s economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative—to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories. Narratives are more powerful than raw statistics, more enduring than pretty charts. When done correctly, data stories can influence decisions and drive change. Most other books focus only on data visualization while neglecting the powerful narrative and psychological aspects of telling stories with data. Author Brent Dykes shows you how to take the three central elements of data storytelling—data, narrative, and visuals—and combine them for maximum effectiveness. Taking a comprehensive look at all the elements of data storytelling, this unique book will enable you to: Transform your insights and data visualizations into appealing, impactful data stories Learn the fundamental elements of a data story and key audience drivers Understand the differences between how the brain processes facts and narrative Structure your findings as a data narrative, using a four-step storyboarding process Incorporate the seven essential principles of better visual storytelling into your work Avoid common data storytelling mistakes by learning from historical and modern examples Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals is a must-have resource for anyone who communicates regularly with data, including business professionals, analysts, marketers, salespeople, financial managers, and educators. |
| data science networking events: Predictive Analytics Eric Siegel, 2016-01-12 Mesmerizing & fascinating... —The Seattle Post-Intelligencer The Freakonomics of big data. —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a |
| data science networking events: The Ethical Algorithm Michael Kearns, Aaron Roth, 2020 Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. |
| data science networking events: Mastering Data Science Cybellium Ltd, Unleash the Power of Insights from Data Are you ready to embark on a transformative journey into the world of data science? Mastering Data Science is your comprehensive guide to unlocking the full potential of data for extracting valuable insights and driving informed decisions. Whether you're an aspiring data scientist looking to enhance your skills or a business leader seeking to leverage data-driven strategies, this book equips you with the knowledge and tools to master the art of data science. Key Features: 1. Dive into Data Science: Immerse yourself in the realm of data science, understanding its core principles, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Visualization: Master the art of data exploration and visualization. Learn how to analyze datasets, uncover patterns, and create compelling visualizations that reveal hidden trends. 3. Statistical Analysis and Hypothesis Testing: Uncover the power of statistical analysis and hypothesis testing. Explore techniques for making data-driven inferences, validating assumptions, and drawing meaningful conclusions. 4. Machine Learning Fundamentals: Delve into machine learning concepts and techniques. Learn about supervised and unsupervised learning, feature engineering, model selection, and evaluation. 5. Predictive Analytics: Discover the realm of predictive analytics. Learn how to build predictive models that forecast future outcomes, enabling proactive decision-making. 6. Natural Language Processing (NLP) and Text Mining: Explore NLP and text mining techniques. Learn how to process and analyze textual data, extract sentiments, and uncover insights from unstructured content. 7. Time Series Analysis: Master time series analysis for modeling sequential data. Learn how to forecast trends, identify seasonality, and make predictions based on temporal patterns. 8. Big Data and Data Wrangling: Dive into big data analytics and data wrangling. Learn how to handle and preprocess large datasets, ensuring data quality and usability. 9. Deep Learning and Neural Networks: Uncover the world of deep learning and neural networks. Learn how to build and train deep learning models for tasks like image recognition and natural language understanding. 10. Real-World Applications: Gain insights into real-world applications of data science across industries. From healthcare to finance, explore how organizations harness data science for strategic decision-making. Who This Book Is For: Mastering Data Science is an indispensable resource for aspiring data scientists, analysts, and business professionals who want to excel in extracting insights from data. Whether you're new to data science or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data for innovation. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| data science networking events: Blockchain, Internet of Things, and Artificial Intelligence Naveen Chilamkurti, T. Poongodi, Balamurugan Balusamy, 2021-04-02 Blockchain, Internet of Things, and Artificial Intelligence provides an integrated overview and technical description of the fundamental concepts of blockchain, IoT, and AI technologies. State-of-the-art techniques are explored in depth to discuss the challenges in each domain. The convergence of these revolutionized technologies has leveraged several areas that receive attention from academicians and industry professionals, which in turn promotes the book's accessibility more extensively. Discussions about an integrated perspective on the influence of blockchain, IoT, and AI for smart cities, healthcare, and other business sectors illuminate the benefits and opportunities in the ecosystems worldwide. The contributors have focused on real-world examples and applications and highlighted the significance of the strengths of blockchain to transform the readers’ thinking toward finding potential solutions. The faster maturity and stability of blockchain is the key differentiator in artificial intelligence and the Internet of Things. This book discusses their potent combination in realizing intelligent systems, services, and environments. The contributors present their technical evaluations and comparisons with existing technologies. Theoretical explanations and experimental case studies related to real-time scenarios are also discussed. FEATURES Discusses the potential of blockchain to significantly increase data while boosting accuracy and integrity in IoT-generated data and AI-processed information Elucidates definitions, concepts, theories, and assumptions involved in smart contracts and distributed ledgers related to IoT systems and AI approaches Offers real-world uses of blockchain technologies in different IoT systems and further studies its influence in supply chains and logistics, the automotive industry, smart homes, the pharmaceutical industry, agriculture, and other areas Presents readers with ways of employing blockchain in IoT and AI, helping them to understand what they can and cannot do with blockchain Provides readers with an awareness of how industry can avoid some of the pitfalls of traditional data-sharing strategies This book is suitable for graduates, academics, researchers, IT professionals, and industry experts. |
| data science networking events: Neuromorphic Photonics Paul R. Prucnal, Bhavin J. Shastri, 2017-05-08 This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of neuromorphic photonics. It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field. |
| data science networking events: Big Data Analytics in Cybersecurity Onur Savas, Julia Deng, 2017-09-18 Big data is presenting challenges to cybersecurity. For an example, the Internet of Things (IoT) will reportedly soon generate a staggering 400 zettabytes (ZB) of data a year. Self-driving cars are predicted to churn out 4000 GB of data per hour of driving. Big data analytics, as an emerging analytical technology, offers the capability to collect, store, process, and visualize these vast amounts of data. Big Data Analytics in Cybersecurity examines security challenges surrounding big data and provides actionable insights that can be used to improve the current practices of network operators and administrators. Applying big data analytics in cybersecurity is critical. By exploiting data from the networks and computers, analysts can discover useful network information from data. Decision makers can make more informative decisions by using this analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the network processes. Bringing together experts from academia, government laboratories, and industry, the book provides insight to both new and more experienced security professionals, as well as data analytics professionals who have varying levels of cybersecurity expertise. It covers a wide range of topics in cybersecurity, which include: Network forensics Threat analysis Vulnerability assessment Visualization Cyber training. In addition, emerging security domains such as the IoT, cloud computing, fog computing, mobile computing, and cyber-social networks are examined. The book first focuses on how big data analytics can be used in different aspects of cybersecurity including network forensics, root-cause analysis, and security training. Next it discusses big data challenges and solutions in such emerging cybersecurity domains as fog computing, IoT, and mobile app security. The book concludes by presenting the tools and datasets for future cybersecurity research. |
| data science networking events: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| data science networking events: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| data science networking events: Creativity in Intelligent Technologies and Data Science Alla Kravets, Maxim Shcherbakov, Marina Kultsova, Olga Shabalina, 2015-09-11 This book constitutes the refereed proceedings of the First Conference on Creativity in Intelligent Technologies and Data Science, CIT&DS 2015, held in Volgograd, Russia, in September 2015. The 66 revised full papers and two short papers presented were carefully reviewed and selected from 208 submissions. The papers are organized in topical sections on computational creativity for science and design; knowledge discovery in patent and open sources for creative tasks; software computer-aided design and agent-based systems; conceptual, cognitive and qualitative modeling with application in intelligent decision making; design creativity in CAD/CAM/CAE/PDM; intelligent decision support for continual improvement process; data science in energy management, transportation and urban development; data science in social networks analysis; natural language and image processing and analysis; game-based learning technologies in engineering education and educational games design; personalized learning in Web-based intelligent educational systems; e-inclusion: development of smart mobile applications for people with disabilities. |
| data science networking events: Impact of ICTs on Event Management and Marketing Birdir, Kemal, Birdir, Sevda, Dalgic, Ali, Toksoz, Derya, 2020-12-04 Conferences, symposiums, and other large events that take place at far away hotels require many hours of preparation to plan and need a capable event staff to market. Without the innovative technologies that have changed the face of the tourism industry, many destinations would be unequipped to handle such a task. Impact of ICTs on Event Management and Marketing is a collection of innovative research on the methods and applications of information and communications technologies on almost all facets of hospitality and tourism-related businesses including hotels, restaurants, and other tourism areas. While highlighting topics including digital marketing, artificial intelligence, and event tourism, this book is ideally designed for business managers, event planners, and marketing professionals. |
| data science networking events: Web Metrics Jim Sterne, 2003-05-12 There now exists a wealth of tools and techniques that can determine if and how a Web site is providing business value to its owners. This book is a survey of those metrics and is as important to IT executives as it is to marketing professionals. Jim Sterne is recognized worldwide as a leading Internet business expert and is the author of several Wiley books, including WWW Marketing, Third Edition (0-471-41621-5) Explains the criteria for building a successful site, surveying the tools, services, techniques, and standards for Web measurement, and fully integrating those metrics with the customer experience Companion Web site contains links to online tools, resources, and white papers |
| data science networking events: Drawdown Paul Hawken, 2017-04-18 • New York Times bestseller • The 100 most substantive solutions to reverse global warming, based on meticulous research by leading scientists and policymakers around the world “At this point in time, the Drawdown book is exactly what is needed; a credible, conservative solution-by-solution narrative that we can do it. Reading it is an effective inoculation against the widespread perception of doom that humanity cannot and will not solve the climate crisis. Reported by-effects include increased determination and a sense of grounded hope.” —Per Espen Stoknes, Author, What We Think About When We Try Not To Think About Global Warming “There’s been no real way for ordinary people to get an understanding of what they can do and what impact it can have. There remains no single, comprehensive, reliable compendium of carbon-reduction solutions across sectors. At least until now. . . . The public is hungry for this kind of practical wisdom.” —David Roberts, Vox “This is the ideal environmental sciences textbook—only it is too interesting and inspiring to be called a textbook.” —Peter Kareiva, Director of the Institute of the Environment and Sustainability, UCLA In the face of widespread fear and apathy, an international coalition of researchers, professionals, and scientists have come together to offer a set of realistic and bold solutions to climate change. One hundred techniques and practices are described here—some are well known; some you may have never heard of. They range from clean energy to educating girls in lower-income countries to land use practices that pull carbon out of the air. The solutions exist, are economically viable, and communities throughout the world are currently enacting them with skill and determination. If deployed collectively on a global scale over the next thirty years, they represent a credible path forward, not just to slow the earth’s warming but to reach drawdown, that point in time when greenhouse gases in the atmosphere peak and begin to decline. These measures promise cascading benefits to human health, security, prosperity, and well-being—giving us every reason to see this planetary crisis as an opportunity to create a just and livable world. |
| data science networking events: Data Science Jobs Ann Rajaram, Want a high-paying $$$ career in the exciting field of DataScience? This is the ONLY book that will help you land a lucrative Analytics job in 90 days or less! This book is the perfect guide for you, if you fall into any of these categories: * You recently completed a masters degree (or online course or bootcamp) and want to get hired quickly as a Data Scientist, Data Analyst, Data Engineer, Machine learning engineer or BI developer. * Looking to start a career in data science, but unsure where to start. * You are an experienced tech professional, but looking to pivot into analytics to boost your salary potential. * Tired of applying to dozens of jobs without getting a positive response and/or final job offer . * F1 visa, STEM OPT/ CPT students will also find this book helpful to land a job in this lucrative field. The book will teach you proven successful strategies on: * Winning Profiles Turbocharge your resume and LinkedIn profile and start receiving interview calls from hiring managers. Let JOBS CHASE YOU, instead of the other way around! * LinkedIn - A dedicated chapter on LinkedIn that teaches you some creative (and SECRET) ways to leverage the site and identify high-paying jobs with low competition. * Niche sites - A full list of niche job boards that other candidates have overlooked. These sites have high-$ jobs but lesser competition than the popular job search sites. Upwork - Contrary to popular opinion, Upwork can help you make $$$ in data science jobs. Learn proven techniques to help you bag contracts and start earning, as quickly as next week. * 100+ interview questions asked in real-life data scientist interviews. * Other learner resources and much more... Author is a practicing analytics professional who has worked in Fortune500 Firms like NASDAQ , BlackRock, etc. Unlike most job search books that are written by recruiters or professors, this book is written by a senior professional, who rose quickly from analyst to managerial roles. She has attended interviews of her own, and knows clearly the frustrations (and at times, hopelessness) of the job search process. The systems in this book have successfully helped dozens of job seekers and will work effectively for you too! Read on to launch your dream career! Note, this book is deliberately kept short and precise, so you can quickly read through and start applying these principles, instead of sifting through 500 pages of fluff. This book includes: Data Science interview questions and answers; Help preparing for Machine Learning Interviews; Top 25 Interview Questions for Data Analyst/Scientist roles; An in-depth overview of Data Science Interview Process; How to ace your interview even if you are an Entry level Data Analyst / Data Scientist; Data Science Interview questions for freshers; How and Where to look for jobs; and much more! |
| data science networking events: Machine Learning Design Patterns Valliappa Lakshmanan, Sara Robinson, Michael Munn, 2020-10-15 The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly |
| data science networking events: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| data science networking events: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| data science networking events: R for Everyone Jared P. Lander, 2017-06-13 Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available. |
| data science networking events: Responsible Artificial Intelligence Virginia Dignum, 2019-11-04 In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens. |
| data science networking events: Cracking the Data Science Interview Leondra R. Gonzalez, Aaren Stubberfield, 2024-02-29 Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more Key Features Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning Gain the confidence to explain complex statistical, machine learning, and deep learning theory Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learn Explore data science trends, job demands, and potential career paths Secure interviews with industry-standard resume and portfolio tips Practice data manipulation with Python and SQL Learn about supervised and unsupervised machine learning models Master deep learning components such as backpropagation and activation functions Enhance your productivity by implementing code versioning through Git Streamline workflows using shell scripting for increased efficiency Who this book is for Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews. |
| data science networking events: Artificial Intelligence and the Legal Profession Michael Legg, Felicity Bell, 2020-11-26 How are new technologies changing the practice of law? With examples and explanations drawn from the UK, US, Canada, Australia and other common law countries, as well as from China and Europe, this book considers the opportunities and implications for lawyers as artificial intelligence systems become commonplace in legal service delivery. It examines what lawyers do in the practice of law and where AI will impact this work. It also explains the important continuing role of the lawyer in an AI world. This book is divided into three parts: Part A provides an accessible explanation of AI, including diagrams, and contrasts this with the role and work of lawyers. Part B focuses on six different aspects of legal work (litigation, transactional, dispute resolution, regulation and compliance, criminal law and legal advice and strategy) where AI is making a considerable impact and looks at how this is occurring. Part C discusses how lawyers and law firms can best utilise the promise of AI, while also acknowledging its limitations. It also discusses ethical and regulatory issues, including the lawyer's role in upholding the rule of law. |
| data science networking events: Never Enough Mike Hayes, 2021-02-09 In Never Enough, Mike Hayes—former Commander of SEAL Team TWO—helps readers apply high-stakes lessons about excellence, agility, and meaning across their personal and professional lives. Mike Hayes has lived a lifetime of once-in-a-lifetime experiences. He has been held at gunpoint and threatened with execution. He’s jumped out of a building rigged to explode, helped amputate a teammate’s leg, and made countless split-second life-and-death decisions. He’s written countless emails to his family, telling them how much he loves them, just in case those were the last words of his they’d ever read. Outside of the SEALs, he’s run meetings in the White House Situation Room, negotiated international arms treaties, and developed high-impact corporate strategies. Over his many years of leadership, he has always strived to be better, to contribute more, and to put others first. That’s what makes him an effective leader, and it’s the quality that he’s identified in all of the great leaders he’s encountered. That continual striving to lift those around him has filled Mike’s life with meaning and purpose, has made him secure in the knowledge that he brings his best to everything he does, and has made him someone others can rely on. In Never Enough, Mike Hayes recounts dramatic stories and offers battle- and boardroom-tested advice that will motivate readers to do work of value, live lives of purpose, and stretch themselves to reach their highest potential. |
| data science networking events: 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. |
| data science networking events: Provenance in Data Science Leslie F. Sikos, Oshani W. Seneviratne, Deborah L. McGuinness, 2021-04-26 RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack maps that aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues. This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic. |
| data science networking events: The Last Safe Investment Bryan Franklin, Michael Ellsberg, 2016-01-05 Myth: If you save for decades and invest in 401(k)s, IRAs, and a home, these investments will grow steadily, allowing twenty to thirty years of secure, peaceful retirement. Reality: Though this might have been true at some point in the last century, it is not true any longer. If you want to get ahead and enjoy a life of prosperity, you must invest in the last safe investment: yourself, and your own skills, value to others, relationships, and overall happiness. Business strategist Bryan Franklin and author Michael Ellsberg (The Education of Millionaires) team up here to present a blueprint for building “True Wealth”: the ability to generate not just financial value but also the experiences you cherish most—security, freedom, creative expression, and love. Discarding traditional advice, Franklin and Ellsberg propose the Self-Amplifying Financial Ecosystem (SAFE) plan. This plan teaches you: ·Small investments you can make for a big impact on your value to other people, multiplying your earning potential ·The secret to accumulating savings without willpower or deprivation ·How to invest in life’s richest experiences (which money can’t buy) ·The “Super Skills”—the most valuable, sought-after, rewarded, and universally beneficial human skills ·Why most people are throwing away huge sums of money in the quest for happiness, and how to spend that money more effectively This bold manifesto will change the way you think about money, wealth, investment, and spending forever. |
| data science networking events: Foundations of Machine Learning, second edition Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 2018-12-25 A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. |
| data science networking events: Resilience Andrew Zolli, Ann Marie Healy, 2013-07-09 All systems break down. Some bounce back, others do not. This is a book about why. Covering business, economic, geographic and social systems, Zolli uncovers a wealth of absorbing examples--from the link between US oil prices and the recent 'tortilla riots' in Mexico to what was really happening when the U.S. government decided not to bail out Lehman Bros. |
| data science networking events: Networking for Big Data Shui Yu, Xiaodong Lin, Jelena Misic, Xuemin (Sherman) Shen, 2015-07-28 Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications.The book is divided into four sections: introduction to Big Data, |
| data science networking events: The Mathematics of Data Michael W. Mahoney, John C. Duchi, Anna C. Gilbert, 2018-11-15 Nothing provided |
| data science networking events: Docker for Data Science Joshua Cook, 2017-08-23 Learn Docker infrastructure as code technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller. It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. As a solution to this problem, Docker for Data Science proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies—Python, Jupyter, Postgres—as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenes and Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms. What You'll Learn Master interactive development using the Jupyter platform Run and build Docker containers from scratch and from publicly available open-source images Write infrastructure as code using the docker-compose tool and its docker-compose.yml file type Deploy a multi-service data science application across a cloud-based system Who This Book Is For Data scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developers |
| data science networking events: Nazis on the Potomac Robert K. Sutton, 2022-01-07 “A fascinating account” of the secret Virginia facility code-named PO Box 1142, where the US gathered intelligence and interrogated German prisoners (Journal of Counterterrorism & Homeland Security International). About fifteen miles south of Washington, DC, Fort Hunt, Virginia is a green open space enjoyed by residents. But not so long ago, it was the site of one of the highest-level clandestine operations of World War II. Shortly after the US entered the war, the military realized it had to work on exploiting any advantages it might gain on the Axis Powers. One part of this endeavor was to establish a secret facility not too close to—but also not too far from—the Pentagon, which would interrogate and eavesdrop on the highest-level Nazi prisoners and also translate and analyze captured German war documents. That complex was established at Fort Hunt, known by the code name: PO Box 1142. The American servicemen who did the interrogating and translating were young, bright, hardworking, and absolutely dedicated to their work. Many of them were Jews who’d escaped Nazi Germany as children—some had come to America with their parents, others had escaped alone, but their experiences, and what they’d been forced to leave behind, meant they had personal motivation to do whatever they could to defeat Nazi Germany. They were perfect for the difficult and complex job at hand. They never used corporal punishment in interrogations of German soldiers but developed and deployed dozens of tricks to gain information. The Allies won the war against Hitler for a host of reasons, discussed in hundreds of volumes. This is the first book to describe the intelligence operations at PO Box 1142 and their part in that success. It will never be known how many American lives were spared, or whether the war ended sooner with the programs at Fort Hunt, but it’s doubtless that they made a difference—and gave the young Jewish men stationed there the chance to combat the evil that had befallen them and their families. “Fills a gap in World War II intelligence history by documenting the origins of a number of European Theater intelligence successes thanks to the work of Ft. Hunt interrogators.” —Studies in Intelligence Includes photographs |
| data science networking events: Data Science Xiaohui Cheng, Weipeng Jing, Xianhua Song, Zeguang Lu, 2019-09-13 This two volume set (CCIS 1058 and 1059) constitutes the refereed proceedings of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019 held in Guilin, China, in September 2019. The 104 revised full papers presented in these two volumes were carefully reviewed and selected from 395 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including data mining; data base; net work; security; machine learning; bioinformatics; natural language processing; software engineering; graphic images; system; education; application. |
| data science networking events: Big Data in Education Ben Williamson, 2017-07-24 Big data has the power to transform education and educational research. Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research. This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future. Key topics include: · The role of learning analytics and educational data science in schools · A critical appreciation of code, algorithms and infrastructures · The rise of ‘cognitive classrooms’, and the practical application of computational algorithms to learning environments · Important digital research methods issues for researchers This is essential reading for anyone studying or working in today′s education environment! |
| data science networking events: 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. |
| data science networking events: Data Science and Big Data Computing Zaigham Mahmood, 2016-07-05 This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis. |
| data science networking events: Advanced Analytics with Spark Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills, 2015-04-02 In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder |
| data science networking events: Data Management Technologies and Applications Slimane Hammoudi, Christoph Quix, Jorge Bernardino, 2021-07-22 This book constitutes the thoroughly refereed proceedings of the 9th International Conference on Data Management Technologies and Applications, DATA 2020, which was supposed to take place in Paris, France, in July 2020. Due to the Covid-19 pandemic the event was held virtually. The 14 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: datamining; decision support systems; data analytics; data and information quality; digital rights management; big data; knowledge management; ontology engineering; digital libraries; mobile databases; object-oriented database systems; data integrity. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes …
