| data science syracuse university: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
| data science syracuse university: Data Science for Business With R Jeffrey S. Saltz, Jeffrey M. Stanton, 2021-02-03 Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available. Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field. |
| data science syracuse university: Data Analysis for Social Science Elena Llaudet, Kosuke Imai, 2022-11-29 Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors-- |
| data science syracuse university: Spark: The Definitive Guide Bill Chambers, Matei Zaharia, 2018-02-08 Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation |
| data science syracuse university: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students coming from a wide range of backgrounds into the world of data science. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using the R programming language and RStudio® from the ground up. Short chapters allow instructors to group concepts together for a semester course and provide students with manageable amounts of information for each concept. By taking students systematically through the R programming environment, the book takes the fear out of data science and familiarizes students with the environment so they can be successful when performing advanced functions. The authors cover statistics from a conceptual standpoint, focusing on how to use and interpret statistics, rather than the math behind the statistics. This text then demonstrates how to use data effectively and efficiently to construct models, predict outcomes, visualize data, and make decisions. Accompanying digital resources provide code and datasets for instructors and learners to perform a wide range of data science tasks. |
| data science syracuse university: C++ and Data Analytics 2 Bundle Manuscript Essential Beginners Guide on Enriching Your C++ Programming Skills and Learn Practical Data Analytics, Data Science, and Predictive Analytics for Beginners Isaac D. Cody, 2017-03-07 This is a 2 book bundle related to C++ programming and Data Analytics! Two manuscripts for the price of one! Whats included in this 2 book bundle manuscript: C++: Learn C++ Like a Boss. A Beginners Guide in Coding Programming And Dominating C++. Novice to Expert Guide To Learn and Master C++ Fast Data Analytics: Practical Data Analysis and Statistical Guide to Transform and Evolve Any Business In C++ programming, you will learn the basics about: Compliers, syntax, class, objects, and variables Identifiers, trigraphs, data types, lines, and characters Boolean and functions Arrays, loops, and conditions Various types of operators Decision statements, if else statements Constants and literals Quick follow up quizzes and answers Guided examples and much more! In the Data Analytics portion of this bundle, you will learn: Why your business should be using data analytics Issues with using big data Effective data management Examples of data management in the real-world The different kinds of data analytics and their definitions How data management, data mining, data integration and data warehousing work together A step-by-step guide for conducting data analysis for your business An organizational guide to data analytics Tools for data visualization Get your copy today! Scroll up and learn how to program in both C++ and Data Analytics! |
| data science syracuse university: Presidential Campaigning in the Internet Age Jennifer Stromer-Galley, 2019 As the plugged-in presidential campaign has arguably reached maturity, Presidential Campaigning in the Internet Age challenges popular claims about the democratizing effect of Digital Communication Technologies (DCTs). Analyzing campaign strategies, structures, and tactics from the past six presidential election cycles, Stromer-Galley reveals how, for all their vaunted inclusivity and tantalizing promise of increased two-way communication between candidates and the individuals who support them, DCTs have done little to change the fundamental dynamics of campaigns. The expansion of new technologies has presented candidates with greater opportunities to micro-target potential voters, cheaper and easier ways to raise money, and faster and more innovative ways to respond to opponents. The need for communication control and management, however, has made campaigns slow and loathe to experiment with truly interactive internet communication technologies. Citizen involvement in the campaign historically has been and, as this book shows, continues to be a means to an end: winning the election for the candidate. For all the proliferation of apps to download, polls to click, videos to watch, and messages to forward, the decidedly undemocratic view of controlled interactivity is how most campaigns continue to operate. In the fully revised second edition, Presidential Campaigning in the Internet Age examines election cycles from 1996, when the World Wide Web was first used for presidential campaigning, through 2016 when campaigns had the full power of advertising on social media sites. As the book charts changes in internet communication technologies, it shows how, even as campaigns have moved from a mass mediated to a networked paradigm, the possibilities these shifts in interactivity seem to promise for citizen input and empowerment remain farther than a click away. |
| data science syracuse university: Grow a Greener Data Center Douglas Alger, 2010 Alger presents technologies, design strategies, and operational approaches that can help any company improve the energy efficiency and eco-friendliness of their IT facilities. He walks step-by-step through greening physical construction, power, cooling, and servers; then covers equipment consolidation, virtualization, and much more. |
| data science syracuse university: Data Science for Business with R Jeffrey S. Saltz, Jeffrey Morgan Stanton, 2021-03-02 Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business's customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available. Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field. |
| data science syracuse university: Data Science for Business With R Jeffrey S. Saltz, Jeffrey M. Stanton, 2021-02-14 Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available. Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field. |
| data science syracuse university: Going Viral Karine Nahon, Jeff Hemsley, 2013-11-25 In Going Viral, Nahon and Hemsley uncover the factors that make things go viral online. They analyze the characteristics of networks that shape virality, including the crucial role of gatekeepers who control the flow of information and connect networks to one another. They also explore the role of human attention, showing how phenomena like word of mouth, bandwagon effects, homophily and interest networks help to explain the patterns of individual behavior that make viral events. |
| data science syracuse university: Qualitative and Mixed Methods Data Analysis Using Dedoose Michelle Salmona, Eli Lieber, Dan Kaczynski, 2019-08-16 Qualitative and Mixed Methods Data Analysis Using Dedoose®: A Practical Approach for Research Across the Social Sciences provides both new and experienced researchers with a guided introduction to dealing with the methodological complexity of mixed methods and qualitative inquiry using Dedoose® software. The authors use their depth of experience designing and updating Dedoose® as well as their published research to give the reader practical strategies for using Dedoose® from a wide range of research studies. Case study contributions by outside researchers provide readers with rich examples of how to use Dedoose® in practical, applied social science and health settings. |
| data science syracuse university: 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 syracuse university: Data Science Doug Rose, 2016-11-17 Learn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business. Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story. Insight!: How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts: You should mine your own company for talent. You can’t change your organization by hiring a few data science superheroes. You should form small, agile-like data teams that focus on delivering valuable insights early and often. You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry. What Your Will Learn: Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organization’s data Understand key data science terms and concepts Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering value Use sprints and storytelling to help your team stay on track and adapt to new knowledge Who This Book Is For Data science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists. |
| data science syracuse university: An Introduction to Data Science With Python Jeffrey S. Saltz, Jeffrey M. Stanton, 2024-05-29 An Introduction to Data Science with Python by Jeffrey S. Saltz and Jeffery M. Stanton provides readers who are new to Python and data science with a step-by-step walkthrough of the tools and techniques used to analyze data and generate predictive models. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using Python-based Jupyter Notebooks. The techniques include making tables and data frames, computing statistics, managing data, creating data visualizations, and building machine learning models. Each chapter breaks down the process into simple steps and components so students with no more than a high school algebra background will still find the concepts and code intelligible. Explanations are reinforced with linked practice questions throughout to check reader understanding. The book also covers advanced topics such as neural networks and deep learning, the basis of many recent and startling advances in machine learning and artificial intelligence. With their trademark humor and clear explanations, Saltz and Stanton provide a gentle introduction to this powerful data science tool. Included with this title: LMS Cartridge: Import this title’s instructor resources into your school’s learning management system (LMS) and save time. Don′t use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site. |
| data science syracuse university: Operations Management and Data Analytics Modelling Lalit Kumar Awasthi, Sushendra Kumar Misra, Dilbagh Panchal, Mohit Tyagi, 2021-12-30 Operations Management and Data Analytics Modelling: Economic Crises Perspective addresses real operation management problems in thrust areas like the healthcare and energy management sectors and Industry 4.0. It discusses recent advances and trends in developing data-driven operation management-based methodologies, big data analysis, application of computers in industrial engineering, optimization techniques, development of decision support systems for industrial operation, the role of a multiple-criteria decision-making (MCDM) approach in operation management, fuzzy set theory-based operation management modelling and Lean Six Sigma. Features Discusses the importance of data analytics in industrial operations to improve economy Provides step-by-step implementation of operation management models to identify best practices Covers in-depth analysis using data-based operation management tools and techniques Discusses mathematical modelling for novel operation management models to solve industrial problems This book is aimed at graduate students and professionals in the field of industrial and production engineering, mechanical engineering and materials science. |
| data science syracuse university: Who Gets In and Why Jeffrey Selingo, 2020-09-15 From award-winning higher education journalist and New York Times bestselling author Jeffrey Selingo comes a revealing look from inside the admissions office—one that identifies surprising strategies that will aid in the college search. Getting into a top-ranked college has never seemed more impossible, with acceptance rates at some elite universities dipping into the single digits. In Who Gets In and Why, journalist and higher education expert Jeffrey Selingo dispels entrenched notions of how to compete and win at the admissions game, and reveals that teenagers and parents have much to gain by broadening their notion of what qualifies as a “good college.” Hint: it’s not all about the sticker on the car window. Selingo, who was embedded in three different admissions offices—a selective private university, a leading liberal arts college, and a flagship public campus—closely observed gatekeepers as they made their often agonizing and sometimes life-changing decisions. He also followed select students and their parents, and he traveled around the country meeting with high school counselors, marketers, behind-the-scenes consultants, and college rankers. While many have long believed that admissions is merit-based, rewarding the best students, Who Gets In and Why presents a more complicated truth, showing that “who gets in” is frequently more about the college’s agenda than the applicant. In a world where thousands of equally qualified students vie for a fixed number of spots at elite institutions, admissions officers often make split-second decisions based on a variety of factors—like diversity, money, and, ultimately, whether a student will enroll if accepted. One of the most insightful books ever about “getting in” and what higher education has become, Who Gets In and Why not only provides an unusually intimate look at how admissions decisions get made, but guides prospective students on how to honestly assess their strengths and match with the schools that will best serve their interests. |
| data science syracuse university: Quantitative Research Methods in Communication Erica Scharrer, Srividya Ramasubramanian, 2021-05-13 This textbook is an advanced introduction to quantitative methods for students in communication and allied social science disciplines that focuses on why and how to conduct research that contributes to social justice. Today’s researchers are inspired by the potential for scholarship to make a difference for society, to push toward more just and equitable ends, and to engage in dialogue with members of the public so that they can make decisions about how to navigate the social, cultural, and political world equipped with accurate, fair, and up-to-date knowledge. This book illustrates the mechanics and the meaning behind quantitative research methods by illustrating each step in the research design process with research addressing questions of social justice. It provides practical guidance for researchers who wish to engage in the transformation of structures, practices, and understandings in society through community and civic engagement and policy formation. It contains step-by-step guidance in quantitative methods—from conceptualization through all the stages of execution of a study, including providing a detailed guide for statistical analysis—and demonstrates how researchers can engage with social justice issues in systematic, rigorous, ethical, and meaningful ways. This text serves as a core or supplementary textbook for graduate and advanced undergraduate courses in research methods for communication and social sciences and fills a gap for a methods text that is responsive to the desire of scholars to conduct socially impactful research. |
| data science syracuse university: The Value of Academic Libraries Megan J. Oakleaf, 2010 This report provides Association of College and Research Libraries (ACRL) leaders and the academic community with a clear view of the current state of the literature on value of libraries within an institutional context, suggestions for immediate Next Steps in the demonstration of academic library value, and a Research Agenda for articulating academic library value. Its focus is to help librarians understand, based on professional literature, the current answer to the question, How does the library advance the missions of the institution? This report is also of interest to higher educational professionals external to libraries, including senior leaders, administrators, faculty, and student affairs professionals. |
| data science syracuse university: 50 Ways to Get a Job Dev Aujla, 2018-04-03 A new personalized way to find the perfect job—while staying calm during the process. You are so much more than a resume or job application, but how can you communicate that to your potential employer? You need to learn to ask the right questions, stop using job sites, and start doing the work that actually counts. Based on information gained from over 400,000 individuals who have used these exercises, this book reveals career expert Dev Aujla’s tried-and-tested method for job seekers at every stage of their career. Filled with anecdotes and advice from professionals ranging from a wilderness guide to an architect, it includes quick-step exercises that help you avoid the common pitfalls of navigating a modern career. Whether you've just decided to start the hunt or you're gearing up for a big interview, 50 Ways to Get a Job will keep you poised, on-track, and motivated right up to landing your dream career. |
| data science syracuse university: The Infographic Murray Dick, 2020-04-21 An exploration of infographics and data visualization as a cultural phenomenon, from eighteenth-century print culture to today's data journalism. Infographics and data visualization are ubiquitous in our everyday media diet, particularly in news—in print newspapers, on television news, and online. It has been argued that infographics are changing what it means to be literate in the twenty-first century—and even that they harmonize uniquely with human cognition. In this first serious exploration of the subject, Murray Dick traces the cultural evolution of the infographic, examining its use in news—and resistance to its use—from eighteenth-century print culture to today's data journalism. He identifies six historical phases of infographics in popular culture: the proto-infographic, the classical, the improving, the commercial, the ideological, and the professional. Dick describes the emergence of infographic forms within a wider history of journalism, culture, and communications, focusing his analysis on the UK. He considers their use in the partisan British journalism of late eighteenth and early nineteenth-century print media; their later deployment as a vehicle for reform and improvement; their mass-market debut in the twentieth century as a means of explanation (and sometimes propaganda); and their use for both ideological and professional purposes in the post–World War II marketized newspaper culture. Finally, he proposes best practices for news infographics and defends infographics and data visualization against a range of criticism. Dick offers not only a history of how the public has experienced and understood the infographic, but also an account of what data visualization can tell us about the past. |
| data science syracuse university: Graph Algorithms Mark Needham, Amy E. Hodler, 2019-05-16 Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark |
| data science syracuse university: Reasoning with Data Jeffrey M. Stanton, 2017-05-22 Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources. ÿ Pedagogical Features *Playful, conversational style and gradual approach; suitable for students without strong math backgrounds. *End-of-chapter exercises based on real data supplied in the free R package. *Technical explanation and equation/output boxes. *Appendices on how to install R and work with the sample datasets.ÿ |
| data science syracuse university: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| data science syracuse university: Information Systems John Gallaugher, 2016 |
| data science syracuse university: Building Analytics Teams John K. Thompson, Douglas B. Laney, 2020-06-30 Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI Key FeaturesLearn to create an operationally effective advanced analytics team in a corporate environmentSelect and undertake projects that have a high probability of success and deliver the improved top and bottom-line resultsUnderstand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your teamBook Description In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learnAvoid organizational and technological pitfalls of moving from a defined project to a production environmentEnable team members to focus on higher-value work and tasksBuild Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organizationOutsource certain projects to competent and capable third partiesSupport the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analyticsAnalyze the operational area, the processes, the data, and the organizational resistanceWho this book is for This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful. |
| data science syracuse university: Discovering the Deep Jeffrey A. Karson, Deborah S. Kelley, Daniel J. Fornari, Michael R. Perfit, Timothy M. Shank, 2015-04-23 A beautifully illustrated reference providing fascinating insights into the hidden world of the seafloor using the latest deep-sea imaging. |
| data science syracuse university: Strategic Diversity Leadership Damon A. Williams, 2023-07-03 In today’s world – whether viewed through a lens of educational attainment, economic development, global competitiveness, leadership capacity, or social justice and equity – diversity is not just the right thing to do, it is the only thing to do! Following the era of civil rights in the 1960s and ‘70s, the 1990s and early 21st century have seen both retrenchment and backlash years, but also a growing recognition, particularly in business and the military, that we have to educate and develop the capacities of our citizens from all levels of society and all demographic and social groups to live fulfilling lives in an inter-connected globe.For higher education that means not only increasing the numbers of diverse students, faculty, and staff, but simultaneously pursuing excellence in student learning and development, as well as through research and scholarship – in other words pursuing what this book defines as strategic diversity leadership. The aim is to create systems that enable every student, faculty, and staff member to thrive and achieve to maximum potential within a diversity framework. This book is written from the perspective that diversity work is best approached as an intellectual endeavor with a pragmatic focus on achieving results that takes an evidence-based approach to operationalizing diversity. It offers an overarching conceptual framework for pursuing diversity in a national and international context; delineates and describes the competencies, knowledge and skills needed to take effective leadership in matters of diversity; offers new data about related practices in higher education; and presents and evaluates a range of strategies, organizational structures and models drawn from institutions of all types and sizes. It covers such issues as the reorganization of the existing diversity infrastructure, building accountability systems, assessing the diversity process, and addressing legal threats to implementation. Its purpose is to help strategic diversity leaders combine big-picture thinking with an on-the-ground understanding of organizational reality and work strategically with key stakeholders and allies. This book is intended for presidents, provosts, chief diversity officers or diversity professionals, and anyone who wants to champion diversity and embed its objectives on his or her campus, whether at the level of senior administration, as members of campus organizations or committees, or as faculty, student affairs professionals or students taking a leadership role in making and studying the process of change.This title is also available in a set with its companion volume, The Chief Diversity Officer. |
| data science syracuse university: Sales Engagement Manny Medina, Max Altschuler, Mark Kosoglow, 2019-03-12 Engage in sales—the modern way Sales Engagement is how you engage and interact with your potential buyer to create connection, grab attention, and generate enough interest to create a buying opportunity. Sales Engagement details the modern way to build the top of the funnel and generate qualified leads for B2B companies. This book explores why a Sales Engagement strategy is so important, and walks you through the modern sales process to ensure you’re effectively connecting with customers every step of the way. • Find common factors holding your sales back—and reverse them through channel optimization • Humanize sales with personas and relevant information at every turn • Understand why A/B testing is so incredibly critical to success, and how to do it right • Take your sales process to the next level with a rock solid, modern Sales Engagement strategy This book is essential reading for anyone interested in up-leveling their game and doing more than they ever thought possible. |
| data science syracuse university: Postrevolutionary Iran Mehrzad Boroujerdi, Kourosh Rahimkhani, 2018-06-05 The 1979 revolution fundamentally altered Iran’s political landscape as a generation of inexperienced clerics who did not hail from the ranks of the upper class—and were not tainted by association with the old regime—came to power. The actions and intentions of these truculent new leaders and their lay allies caused major international concern. Meanwhile, Iran’s domestic and foreign policy and its nuclear program have loomed large in daily news coverage. Despite global consternation, however, our knowledge about Iran’s political elite remains skeletal. Nearly four decades after the clergy became the state elite par excellence, there has been no empirical study of the recruitment, composition, and circulation of the Iranian ruling members after 1979. Postrevolutionary Iran: A Political Handbook provides the most comprehensive collection of data on political life in postrevolutionary Iran, including coverage of 36 national elections, more than 400 legal and outlawed political organizations, and family ties among the elite. It provides biographical sketches of more than 2,300 political personalities ranging from cabinet ministers and parliament deputies to clerical, judicial, and military leaders, much of this information previously unavailable in English. Providing a cartography of the complex structure of power in postrevolutionary Iran, this volume offers a window not only into the immediate years before and after the Iranian Revolution but also into what has happened during the last four turbulent decades. This volume and the data it contains will be invaluable to policymakers, researchers, and scholars of the Middle East alike. |
| data science syracuse university: Fission-track Thermochronology and Its Application to Geology Marco G. Malusà, Paul G. Fitzgerald, 2019 This book is focused on the basics of applying thermochronology to geological and tectonic problems, with the emphasis on fission-track thermochronology. It is conceived for relatively new practitioners to thermochronology, as well as scientists experienced in the various methods. The book is structured in two parts. Part I is devoted to the fundamentals of the fission-track method, to its integration with other geochronologic methods, and to the basic principles of statistics for fission-track dating and sedimentology applied to detrital thermochronology. Part I also includes the historical development of the technique and thoughts on future directions. Part II is devoted to the geological interpretation of the thermochronologic record. The thermal frame of reference and the different approaches for the interpretation of fission-track data within a geological framework of both basement and detrital studies are discussed in detail. Separate chapters demonstrate the application of fission-track thermochronology from various perspectives (e.g., tectonics, petrology, stratigraphy, hydrocarbon exploration, geomorphology), with other chapters on the application to basement rocks in orogens, passive continental margins and cratonic interiors, as well as various applications of detrital thermochronology. |
| data science syracuse university: Numsense! Data Science for the Layman Annalyn Ng, 2017-03-24 Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science? Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks Features: Intuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions. |
| data science syracuse university: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data science syracuse university: Representations of Algebras Graham J. Leuschke, Frauke Bleher, Ralf Schiffler, Dan I. Zacharia, 2018 Contains the proceedings of the 17th Workshop and International Conference on Representations of Algebras (ICRA 2016), held in August 2016, at Syracuse University. This volume includes three survey articles based on short courses in the areas of commutative algebraic groups, modular group representation theory, and thick tensor ideals of bounded derived categories. |
| data science syracuse university: Geospatial Health Data Paula Moraga, 2019-11-26 Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners. |
| data science syracuse university: Network Neutrality and Digital Dialogic Communication Alison N. Novak, Melinda Sebastian, 2018-10-03 In the months after the Federal Communications Commission’s (FCC) 2017 decision to repeal network neutrality as US policy, it is easy to forget the decades of public, organizational, media and governmental struggle to control digital policy and open access to the internet. Using dialogic communication tactics, the public, governmental actors and organizations impacted the ruling through YouTube comments, the FCC online system and social network communities. Network neutrality, which requires that all digital sites can be accessed with equal speed and ability, is an important example of how dialogic communication facilitates public engagement in policy debates. However, the practice and ability of the public, organizations and media to engage in dialogic communication are also greatly impacted by the FCC’s decision. This book reflects on decades of global engagement in the network neutrality debate and the evolution of dialogic communication techniques used to shape one of the most relevant and critical digital policies in history. |
| data science syracuse university: Wicked Winnie Holzman, 2010-10 Each title in The Applause Libretto Library Series presents a Broadway musical with fresh packaging in a 6 x 9 trade paperback format. Each Complete Book and Lyrics is approved by the writers and attractively designed with color photo inserts from the Broadway production. All titles include introduction and foreword by renowned Broadway musical experts. Long before Dorothy dropped in, two other girls meet in the Land of Oz. One, born with emerald green skin, is smart, fiery, and misunderstood. The other is beautiful, ambitious, and very popular. The story of how these two unlikely friends end up as the Wicked Witch of the West and Glinda the Good Witch makes for the most spellbinding new musical in years. |
| data science syracuse university: The Visible Employee Jeffrey M. Stanton, Kathryn R. Stam, 2006 The misuse of an organization's information systems by employees, whether through error or by intent, can result in leaked and corrupted data, crippled networks, lost productivity, legal problems, and public embarrassment. As organizations turn to technology to monitor employee use of network resources, they are finding themselves at odds with workers who instinctively feel their privacy is being invaded. The Visible Employee reports the results of an extensive four-year research project, covering a range of security solutions for at-risk organizations as well as the perceptions and attitudes of employees toward monitoring and surveillance. The result is a wake-up call for business owners, managers, and IT staff, as well as an eye-opening dose of reality for employees. |
| data science syracuse university: Libraries, Archives, and Museums Suzanne M. Stauffer, 2021-08-17 This is the first book to consider the development of all three cultural heritage institutions – libraries, archives, and museums – and their interactions with society and culture from ancient history to the present day in Western Europe, the United Kingdom, and the United States. The text explores the social and cultural role of these institutions in the societies that created them, as well as the political, economic and social influences on their mission, philosophy, and services and how those changed throughout time. The work provides a thorough background in the topic for graduate students and professionals in the fields of library and information science, archival studies, and museum resource management, preservation, and administration. Arranged chronologically, the story begins with the temple libraries of ancient Sumer, followed the growth and development of governmental and private libraries in ancient Greece and Rome, the influence of Asia and Islam on Western library development, the role of Christianity in the preservation of ancient literature as well as the skills of reading and writing during the Middle Ages, and the coming of the Renaissance and the rise of the university library. It continues by tracing the gradual division between archives and libraries and the growth of governmental and private libraries as independent institutions during and after the Renaissance and through the Enlightenment, and the development of public and private museums from the “cabinets of curiousities” of private collectors beginning in the 17th century. Individual chapters explore the further growth and development of libraries, archives, and museums in the 19th and 20th centuries, exploring the public library and public museum movements of those centuries, as well as the rise of the governmental and institutional archive. The final chapter discusses the growing collaboration between and even convergence of these institutions in the 21st century and the impact of modern information technology, and makes predictions about the future of all three institutions. |
| data science syracuse university: Contemporary Issues in Sport Management Dr. Terri Byers, 2015-11-30 ‘Contemporary Issues in Sport Management presents an extensive array of absorbing contemporary issues relevant to managing sport. Internationally recognised scholars have contributed thought-provoking chapters on current global and local issues that are challenging traditional ways of thinking about and delivering sport. This exciting new book is rich in theory and stimulates readers to really think through the associated implications for sport management practice.’ -Tracy Taylor, Professor and Deputy Dean, University of Technology Sydney Business School Contemporary Issues in Sports Management offers a rich and dynamic introduction to the management of sport. An essential companion for all students embarking on a sports management course or module, it draws on research expertise from around the world and integrates these perspectives into engaging and accessible chapters. This book offers: Contemporary and international case studies followed by ‘Tools for analysis’ so students can put the methodology into practice. A range of pedagogical features including learning objectives, learning outcomes and short ‘Thinking points’ Useful websites and further reading at the end of each chapter A discussion of up-to-date issues including corruption in sport, sport policy, doping, athlete transgressions and much more. This book will lead students on a comprehensive exploration of global, national issues, and organizational issues in sport management. The authors encourage critical thought, investigation and the questioning of current practice in order to improve the leadership and management of sport in today’s society. Ideal for Undergraduate students of sport management, sport business, sport policy, sport marketing and sport development. |
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