| business intelligence and analytics stevens: Business Intelligence And Analytics Prof. (Dr.) Sugandha Singh, 2023-12-27 In the fast changing world of modern business, the book Business Intelligence and Analytics serves as a complete guide, unraveling the complexities of strategically using data. As data becomes a critical asset for organizations, this book will become a must-have resource for professionals, executives, and students navigating the intricate interaction of information, technology, and decision-making. Beginning with the foundations of data collection and storage and progressing to advanced subjects such as predictive modelling, machine learning, and artificial intelligence, the book provides a full investigation of business intelligence and analytics. Readers acquire a comprehensive overview of the tools and processes defining the data-driven decision-making environment by covering the whole range. The book incorporates real-world examples and case studies to demonstrate essential topics and is rich in practical insights. The incorporation of theoretical ideas into concrete situations bridges the gap between theory and application, providing readers with a better knowledge of how to implement business intelligence techniques in a variety of organizational contexts. The book is aimed at a wide range of readers, including corporate leaders, data analysts, and students. Whether you are a seasoned business leader looking for a strategic data advantage, an analyst looking for relevant insights, or a student laying the groundwork, this book is a flexible and approachable resource for all levels of experience. |
| business intelligence and analytics stevens: Business Intelligence Career Master Plan Eduardo Chavez, Danny Moncada, 2023-08-31 Learn the foundations of business intelligence, sector trade-offs, organizational structures, and technology stacks while mastering coursework, certifications, and interview success strategies Purchase of the print or Kindle book includes a free PDF eBook Key Features Identify promising job opportunities and ideal entry point into BI Build, design, implement, and maintain BI systems successfully Ace your BI interview with author's expert guidance on certifications, trainings, and courses Book DescriptionNavigating the challenging path of a business intelligence career requires you to consider your expertise, interests, and skills. Business Intelligence Career Master Plan explores key skills like stacks, coursework, certifications, and interview advice, enabling you to make informed decisions about your BI journey. You’ll start by assessing the different roles in BI and matching your skills and career with the tech stack. You’ll then learn to build taxonomy and a data story using visualization types. Additionally, you’ll explore the fundamentals of programming, frontend development, backend development, software development lifecycle, and project management, giving you a broad view of the end-to-end BI process. With the help of the author’s expert advice, you’ll be able to identify what subjects and areas of study are crucial and would add significant value to your skill set. By the end of this book, you’ll be well-equipped to make an informed decision on which of the myriad paths to choose in your business intelligence journey based on your skill set and interests.What you will learn Understand BI roles, roadmap, and technology stack Accelerate your career and land your first job in the BI industry Build the taxonomy of various data sources for your organization Use the AdventureWorks database and PowerBI to build a robust data model Create compelling data stories using data visualization Automate, templatize, standardize, and monitor systems for productivity Who this book is for This book is for BI developers and business analysts who are passionate about data and are looking to advance their proficiency and career in business intelligence. While foundational knowledge of tools like Microsoft Excel is required, having a working knowledge of SQL, Python, Tableau, and major cloud providers such as AWS or GCP will be beneficial. |
| business intelligence and analytics stevens: Handbook of Modeling High-Frequency Data in Finance Frederi G. Viens, Maria Cristina Mariani, Ionut Florescu, 2011-12-20 CUTTING-EDGE DEVELOPMENTS IN HIGH-FREQUENCY FINANCIAL ECONOMETRICS In recent years, the availability of high-frequency data and advances in computing have allowed financial practitioners to design systems that can handle and analyze this information. Handbook of Modeling High-Frequency Data in Finance addresses the many theoretical and practical questions raised by the nature and intrinsic properties of this data. A one-stop compilation of empirical and analytical research, this handbook explores data sampled with high-frequency finance in financial engineering, statistics, and the modern financial business arena. Every chapter uses real-world examples to present new, original, and relevant topics that relate to newly evolving discoveries in high-frequency finance, such as: Designing new methodology to discover elasticity and plasticity of price evolution Constructing microstructure simulation models Calculation of option prices in the presence of jumps and transaction costs Using boosting for financial analysis and trading The handbook motivates practitioners to apply high-frequency finance to real-world situations by including exclusive topics such as risk measurement and management, UHF data, microstructure, dynamic multi-period optimization, mortgage data models, hybrid Monte Carlo, retirement, trading systems and forecasting, pricing, and boosting. The diverse topics and viewpoints presented in each chapter ensure that readers are supplied with a wide treatment of practical methods. Handbook of Modeling High-Frequency Data in Finance is an essential reference for academics and practitioners in finance, business, and econometrics who work with high-frequency data in their everyday work. It also serves as a supplement for risk management and high-frequency finance courses at the upper-undergraduate and graduate levels. |
| business intelligence and analytics stevens: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| business intelligence and analytics stevens: The Big Shift in IT Leadership Hunter Muller, 2015-05-22 Learn the unique leadership strategies of the effective, essential CIO Beyond the Cloud provides a blueprint for leadership in an era of high volatility, rapid transformation, and amazing growth. An effective CIO is essential to the successful navigation of turbulent and uncertain times, and this insightful guide gives you the actionable framework you need to execute the leadership strategies that work. Focused on the major factors that are critical to modern global enterprise, this book delves into communication, collaboration, relationships, technology, innovation, talent management, and more to provide well-rounded guidance toward principled leadership. Simple, straightforward language explains the basics of each area, and is expanded upon by real-world stories and revealing anecdotes gathered from the author's exclusive interviews with visionary thought leaders from major organizations worldwide. You get perspective from the top on established and emerging leadership strategies, helping you put these ideas to work right away. CIOs are perfectly positioned to provide the leadership required to stay ahead of the competition in complex, rapidly shifting markets. They break down walls, align resources, and facilitate collaboration to drive business value and spur growth amidst an atmosphere of hyper-competition. This guide is your practical handbook for becoming the leader that you need to be. Establish trust and cooperation across the enterprise Recruit and retain the top talent in your field Leverage new technology for continuous business growth Inspire loyalty and optimal performance from everyone on the team The CIO has never been more crucial to the enterprise than right now. With clear guidance toward the unique leadership skills the role requires, Beyond the Cloud is the foundational executive guide for transformational leaders in the new business era. |
| business intelligence and analytics stevens: Business Analytics Jay Liebowitz, 2013-12-19 Together, Big Data, high-performance computing, and complex environments create unprecedented opportunities for organizations to generate game-changing insights that are based on hard data. Business Analytics: An Introduction explains how to use business analytics to sort through an ever-increasing amount of data and improve the decision-making capabilities of an organization. Covering the key areas of business analytics, the book explores the concepts, techniques, applications, and emerging trends that professionals across a wide range of industries need to be aware of. Better detection of fraud through visual analytics or better prediction of the likelihood of someone getting an infection while in the hospital are just a few examples of where analytics can play a positive role. As the field of business analytics continues to emerge rapidly, there is a need for a reliable textbook and reference on the subject. Filling this need, this book is suitable for graduate-level students and undergraduate seniors. It maintains a focus on only the key areas so the material can be covered adequately in a one-semester or one-quarter course. Each chapter includes software-generic exercises, labs, and associated answers to the exercises/labs. Author Jay Liebowitz recently had an article published in The World Financial Review. www.worldfinancialreview.com/?p=1904 |
| business intelligence and analytics stevens: The Birth of a Building Ben Stevens, 2019-10-09 Part One of this book focuses on the birds and the bees, explaining the economic story which motivates people to create new buildings in the first place. Part Two focuses on the longer pregnancy and delivery process. Here we meet the developers, architects, engineers, urban planners, lawyers, lenders, and investors who play a part in the story. |
| business intelligence and analytics stevens: Business Intelligence and Human Resource Management Deepmala Singh, Anurag Singh, Amizan Omar, SB Goyal, 2022-08-31 Business Intelligence (BI) is a solution to modern business problems. This book discusses the relationship between BI and Human Resource Management (HRM). In addition, it discusses how BI can be used as a strategic decision-making tool for the sustainable growth of an organization or business. BI helps organizations generate interactive reports with clear and reliable data for making numerous business decisions. This book covers topics spanning the important areas of BI in the context of HRM. It gives an overview of the aspects, tools, and techniques of BI and how it can assist HRM in creating a successful future for organizations. Some of the tools and techniques discussed in the book are analysis, data preparation, BI-testing, implementation, and optimization on GR and management disciplines. It will include a chapter on text mining as well as a section of case studies for practical use. This book will be useful for business professionals, including but not limited to, HR professionals, and budding business students. |
| business intelligence and analytics stevens: Global Business Analytics Models Hokey Min, 2016-03-05 THE COMPLETE GUIDE TO USING ANALYTICS TO MANAGE RISK AND UNCERTAINTY IN COMPLEX GLOBAL BUSINESS ENVIRONMENTS Practical techniques for developing reliable, actionable intelligence–and using it to craft strategy Analytical opportunities to solve key managerial problems in global enterprises Written for working managers: packed with realistic, useful examples This guide helps global managers use modern analytics to gain reliable, actionable, and timely business intelligence–and use it to manage risk, build winning strategies, and solve urgent problems. Dr. Hokey Min offers a practical, easy-to-understand overview of business analytics in a global context, focusing especially on managerial and strategic implications. After demystifying today’s core quantitative tools, he demonstrates them at work in a wide spectrum of global applications. You’ll build models to help segment global markets, forecast demand, assess risk, plan financing, optimize supply chains, and more. Along the way, you’ll find practical guidance for developing analytic thinking, operationalizing Big Data in global environments, and preparing for future analytical innovations. Whether you’re a global executive, strategist, analyst, marketer, supply chain professional, student or researcher, this book will help you drive real value from analytics–in smarter decisions, improved strategy, and better management. In today’s global business environments characterized by growing complexity, volatility, and uncertainty, business analytics has become an indispensable tool for managing these challenges. Specifically, global managers need analytics expertise to solve problems, identify opportunities, shape strategy, mitigate risk, and improve their day-to-day operational efficiency. Now, for the first time, there’s an analytics guide designed specifically for decision-makers in global organizations. Leveraging his experience teaching a number of students and training hundreds of managers and executives, Dr. Hokey Min demystifies the principles and tools of modern business analytics, and demonstrates their real-world use in global business. First, Dr. Min identifies key success factors and mindsets, helping you establish the preconditions for effective analysis. Next, he walks you through the practicalities of collecting, organizing, and analyzing Big Data, and developing models to transform them into actionable insight. Building on these foundations, he illustrates core analytical applications in finance, healthcare, and global supply chains. He concludes by previewing emerging trends in analytics, including the newest tools for automated decision-making. Compare today’s key quantitative tools Stats, data mining, OR, and simulation: how they work, when to use them Get the right data... ...and get the data right Predict the future... ...and sense its arrival sooner than others can |
| business intelligence and analytics stevens: Applying Business Intelligence Initiatives in Healthcare and Organizational Settings Miah, Shah J., Yeoh, William, 2018-07-13 Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings incorporates emerging concepts, methods, models, and relevant applications of business intelligence systems within problem contexts of healthcare and other organizational boundaries. Featuring coverage on a broad range of topics such as rise of embedded analytics, competitive advantage, and strategic capability, this book is ideally designed for business analysts, investors, corporate managers, and entrepreneurs seeking to advance their understanding and practice of business intelligence. |
| business intelligence and analytics stevens: AI Crash Course Hadelin de Ponteves, 2019-11-29 Unlock the power of artificial intelligence with top Udemy AI instructor Hadelin de Ponteves. Key FeaturesLearn from friendly, plain English explanations and practical activitiesPut ideas into action with 5 hands-on projects that show step-by-step how to build intelligent softwareUse AI to win classic video games and construct a virtual self-driving carBook Description Welcome to the Robot World ... and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. What you will learnMaster the basics of AI without any previous experienceBuild fun projects, including a virtual-self-driving car and a robot warehouse workerUse AI to solve real-world business problemsLearn how to code in PythonDiscover the 5 principles of reinforcement learningCreate your own AI toolkitWho this book is for If you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level). |
| business intelligence and analytics stevens: Big Data Analytics in Supply Chain Management Iman Rahimi, Amir H. Gandomi, Simon James Fong, M. Ali Ülkü, 2020-12-20 In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems. |
| business intelligence and analytics stevens: How Everything Can Collapse Pablo Servigne, Raphaël Stevens, 2020-03-27 What if our civilization were to collapse? Not many centuries into the future, but in our own lifetimes? Most people recognize that we face huge challenges today, from climate change and its potentially catastrophic consequences to a plethora of socio-political problems, but we find it hard to face up to the very real possibility that these crises could produce a collapse of our entire civilization. Yet we now have a great deal of evidence to suggest that we are up against growing systemic instabilities that pose a serious threat to the capacity of human populations to maintain themselves in a sustainable environment. In this important book, Pablo Servigne and Raphaël Stevens confront these issues head-on. They examine the scientific evidence and show how its findings, often presented in a detached and abstract way, are connected to people’s ordinary experiences – joining the dots, as it were, between the Anthropocene and our everyday lives. In so doing they provide a valuable guide that will help everyone make sense of the new and potentially catastrophic situation in which we now find ourselves. Today, utopia has changed sides: it is the utopians who believe that everything can continue as before, while realists put their energy into making a transition and building local resilience. Collapse is the horizon of our generation. But collapse is not the end – it’s the beginning of our future. We will reinvent new ways of living in the world and being attentive to ourselves, to other human beings and to all our fellow creatures. |
| business intelligence and analytics stevens: Everything Bad is Good for You Steven Johnson, 2006-05-02 From the New York Times bestselling author of How We Got To Now and Farsighted Forget everything you’ve ever read about the age of dumbed-down, instant-gratification culture. In this provocative, unfailingly intelligent, thoroughly researched, and surprisingly convincing big idea book, Steven Johnson draws from fields as diverse as neuroscience, economics, and media theory to argue that the pop culture we soak in every day—from Lord of the Rings to Grand Theft Auto to The Simpsons—has been growing more sophisticated with each passing year, and, far from rotting our brains, is actually posing new cognitive challenges that are actually making our minds measurably sharper. After reading Everything Bad is Good for You, you will never regard the glow of the video game or television screen the same way again. With a new afterword by the author. |
| business intelligence and analytics stevens: Analytics and Data Science Amit V. Deokar, Ashish Gupta, Lakshmi S. Iyer, Mary C. Jones, 2017-10-05 This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science. |
| business intelligence and analytics stevens: Briefly, A Delicious Life Nell Stevens, 2022-07-19 *A Cosmopolitan Best Book of Summer * One of BuzzFeed’s Most Anticipated LGBTQ+ Books* An “exquisite…too lovely to bear” (The New York Times Book Review) debut novel from an award-winning writer: a playful and daring tale about a teenage ghost who falls in love with the writer George Sands. In 1473, fourteen-year-old Blanca dies in a hilltop monastery in Mallorca. Nearly four hundred years later, when George Sand, her two children, and her lover Frederic Chopin arrive in the village, Blanca is still there: a spirited, funny, righteous ghost, she’s been hanging around the monastery since her accidental death, spying on the monks and the townspeople and keeping track of her descendants. Blanca is enchanted the moment she sees George, and the magical novel unfolds as a story of deeply felt, unrequited longing—a teenage ghost pining for a woman who can’t see her and doesn’t know she exists. As George and Chopin, who wear their unconventionality, in George’s case, literally on their sleeves, find themselves in deepening trouble with the provincial, 19th-century villagers, Blanca watches helplessly and reflects on the circumstances of her own death (which involved an ill-advised love affair with a monk-in-training). Charming, original, and emotionally moving, this “deeply wild debut follows the unconventional love triangle” (Cosmopolitan) between George, Chopin, and Blanca—a gorgeous and surprising exploration of artistry, desire, and life after death. |
| business intelligence and analytics stevens: Teaching Data Analytics Susan A Vowels, Katherine Leaming Goldberg, 2019-06-17 The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area. The very size and continuing growth of this need means that there is still space for new program development. Schools wishing to pursue business analytics programs intentionally assess the maturity level of their programs and take steps to close the gap. Teaching Data Analytics: Pedagogy and Program Design is a reference for faculty and administrators seeking direction about adding or enhancing analytics offerings at their institutions. It provides guidance by examining best practices from the perspectives of faculty and practitioners. By emphasizing the connection of data analytics to organizational success, it reviews the position of analytics and decision science programs in higher education, and to review the critical connection between this area of study and career opportunities. The book features: A variety of perspectives ranging from the scholarly theoretical to the practitioner applied An in-depth look into a wide breadth of skills from closely technology-focused to robustly soft human connection skills Resources for existing faculty to acquire and maintain additional analytics-relevant skills that can enrich their current course offerings. Acknowledging the dichotomy between data analytics and data science, this book emphasizes data analytics rather than data science, although the book does touch upon the data science realm. Starting with industry perspectives, the book covers the applied world of data analytics, covering necessary skills and applications, as well as developing compelling visualizations. It then dives into pedagogical and program design approaches in data analytics education and concludes with ideas for program design tactics. This reference is a launching point for discussions about how to connect industry’s need for skilled data analysts to higher education’s need to design a rigorous curriculum that promotes student critical thinking, communication, and ethical skills. It also provides insight into adding new elements to existing data analytics courses and for taking the next step in adding data analytics offerings, whether it be incorporating additional analytics assignments into existing courses, offering one course designed for undergraduates, or an integrated program designed for graduate students. |
| business intelligence and analytics stevens: Visualization Analysis and Design Tamara Munzner, 2014-12-01 Learn How to Design Effective Visualization SystemsVisualization Analysis and Design provides a systematic, comprehensive framework for thinking about visualization in terms of principles and design choices. The book features a unified approach encompassing information visualization techniques for abstract data, scientific visualization techniques |
| business intelligence and analytics stevens: Applied Artificial Intelligence Mariya Yao, Adelyn Zhou, Marlene Jia, 2018-04-30 This bestselling book gives business leaders and executives a foundational education on how to leverage artificial intelligence and machine learning solutions to deliver ROI for your business. |
| business intelligence and analytics stevens: Applied Insurance Analytics Patricia L. Saporito, 2015 Data is the insurance industry's single greatest asset. Yet many insurers radically underutilize their data assets, and are failing to fully leverage modern analytics. This makes them vulnerable to traditional and non-traditional competitors alike. Today, insurers largely apply analytics in important but stovepiped operational areas like underwriting, claims, marketing and risk management. By and large, they lack an enterprise analytic strategy -- or, if they have one, it is merely an architectural blueprint, inadequately business-driven or strategically aligned. Now, writing specifically for insurance industry professionals and leaders, Patricia Saporito uncovers immense new opportunities for driving competitive advantage from analytics -- and shows how to overcome the obstacles that stand in your way. Drawing on 25+ years of insurance industry experience, Saporito introduces proven best practices for developing, maturing, and profiting from your analytic capabilities. This user-friendly handbook advocates an enterprise strategy approach to analytics, presenting a common framework you can quickly adapt based on your unique business model and current capabilities. Saporito reviews common analytic applications by functional area, offering specific case studies and examples, and helping you build upon the analytics you're already doing. She presents data governance models and models proven to help you organize and deliver trusted data far more effectively. Finally, she provides tools and frameworks for improving the analytic IQ of your entire enterprise, from IT developers to business users. |
| business intelligence and analytics stevens: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| business intelligence and analytics stevens: Theory and Practice of Business Intelligence in Healthcare Khuntia, Jiban, Ning, Xue, Tanniru, Mohan, 2019-12-27 Business intelligence supports managers in enterprises to make informed business decisions in various levels and domains such as in healthcare. These technologies can handle large structured and unstructured data (big data) in the healthcare industry. Because of the complex nature of healthcare data and the significant impact of healthcare data analysis, it is important to understand both the theories and practices of business intelligence in healthcare. Theory and Practice of Business Intelligence in Healthcare is a collection of innovative research that introduces data mining, modeling, and analytic techniques to health and healthcare data; articulates the value of big volumes of data to health and healthcare; evaluates business intelligence tools; and explores business intelligence use and applications in healthcare. While highlighting topics including digital health, operations intelligence, and patient empowerment, this book is ideally designed for healthcare professionals, IT consultants, hospital directors, data management staff, data analysts, hospital administrators, executives, managers, academicians, students, and researchers seeking current research on the digitization of health records and health systems integration. |
| business intelligence and analytics stevens: Cyber Security and Business Intelligence Mohammad Zoynul Abedin, Petr Hajek, 2023-12-11 To cope with the competitive worldwide marketplace, organizations rely on business intelligence to an increasing extent. Cyber security is an inevitable practice to protect the entire business sector and its customer. This book presents the significance and application of cyber security for safeguarding organizations, individuals’ personal information, and government. The book provides both practical and managerial implications of cyber security that also supports business intelligence and discusses the latest innovations in cyber security. It offers a roadmap to master degree students and PhD researchers for cyber security analysis in order to minimize the cyber security risk and protect customers from cyber-attack. The book also introduces the most advanced and novel machine learning techniques including, but not limited to, Support Vector Machine, Neural Networks, Extreme Learning Machine, Ensemble Learning, and Deep Learning Approaches, with a goal to apply those to cyber risk management datasets. It will also leverage real-world financial instances to practise business product modelling and data analysis. The contents of this book will be useful for a wide audience who are involved in managing network systems, data security, data forecasting, cyber risk modelling, fraudulent credit risk detection, portfolio management, and data regulatory bodies. It will be particularly beneficial to academics as well as practitioners who are looking to protect their IT system, and reduce data breaches and cyber-attack vulnerabilities. |
| business intelligence and analytics stevens: Deep Learning with PyTorch Luca Pietro Giovanni Antiga, Eli Stevens, Thomas Viehmann, 2020-07-01 “We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production |
| business intelligence and analytics stevens: Big Data and Health Analytics Katherine Marconi, Harold Lehmann, 2014-12-20 This book provides frameworks, use cases, and examples that illustrate the role of big data and analytics in modern health care, including how public health information can inform health delivery. Written for health care professionals and executives, this book presents the current thinking of academic and industry researchers and leaders from around the world. Using non-technical language, it includes case studies that illustrate the business processes that underlie the use of big data and health analytics to improve health care delivery. |
| business intelligence and analytics stevens: Quantitative Research in Economics and Management Sciences Agnieszka Zakrzewska-Bielawska, Anna M. Lis, Anna Ujwary-Gil, 2022-01-01 In this thematic issue of the Journal of Entrepreneurship, Management and Innovation, entitled Qualitative Research in Economics and Management Sciences, the authors used many quantitative methods and research models, e.g. SEM, PLS-SEM, or probit models (Table 1). Each of these approaches is characterized by methodological rigor and an assessment of the reliability and validity of the research instruments used. Pini and Tchorek (2022) analyze the determinants of exports in two European, culturally related countries, such as Italy and Poland, using an econometric and probit model, which implies a normal distribution of errors and is adapted to binary responses (excluding size and age variables). The authors investigate the influence of many independent variables (size, age, management by family members or external managers) on the dependent variable (export), controlling the research model by product and process innovation, location in a less developed region, operations in a high/medium-high technology-intensive sector or cooperation with many banks. The results confirm the authors' initial assumptions that the size of companies influences the exports of the surveyed countries; the age of companies exporting their goods is more important in Italy than in Poland, where no such impact has been recorded. In addition, management by an external manager increases the likelihood of exports for younger family businesses in Italy and smaller family businesses in Poland. The authors also showed that product innovation is the engine of exports in Italy and Poland, and geographic location affects the likelihood of exports in Italy, but not in Poland. In other studies, Paulino (2022) presents the growing business analytics and business intelligence in the Philippines, their impact on organizational performance, and marketing, financial, and business process performance indicators. Retail companies were selected for the study, focusing on advanced data management used in business operations. The author mainly used the well-known PLS-SEM model, and his research instrument was assessed in terms of content validity, construct validity, and reliability. The results of the measurement and structural model evaluation were also subject to verification. The results indicate the impact of business analytics capabilities (including the ability of the decision support system (DSS), business process improvement (BPM), data dashboard (DD), and financial analysis (FA) on the business intelligence level. In addition, it has been empirically verified that organizational performance influences marketing, financial, and business process performance. Overall, business intelligence is an essential predictor of a retail company's organizational performance. The assumption that the level of readiness to implement business analytics can be treated as a moderating factor between business analytics and organizational performance has not been confirmed. The next article by Klimontowicz and Majewska (2022) presents the positive impact of intellectual capital (IC), especially its three components, such as process capital, human capital and relational capital, on the competitiveness of banks and market efficiency. The authors used the following methods and tools: Principal Axis Factor Analysis, PLS-SEM, PAPI, and CAWI. As a result of their application, they emphasize that, in contrast to previous research, the process capital dominates the bank's potential to create a competitive advantage, not human capital, proving the vital role of technology and innovation. They found that competitive performance moderates the relationship between IC and market efficiency; the environment positively moderates the relationship between IC and competitor performance as well as the relationship between competitor performance and market efficiency. The size of the bank and the length of its market activity affect the market efficiency measured by the average rate of changes in ROA and ROE. The study expands the existing evidence, mainly from well-developed countries, on the intellectual capital of Polish banks, emphasizing the process capital to a much greater extent as a modern and so far little exposed component of IC in other research. The last two articles refer to human resource management. Hassan's study (2022) explores the impact of human resource management (HRM) practices on employee retention. In addition, he moderates the role of performance evaluation, training and development in the relationship between HRM practices and employee retention. Using SEM and questionnaires validated by other researchers, the author proves the originality of research in the retail sector in the Maldives on improving employee retention, a complementary approach to the impact of rewards and compensations, training and employee development, as well as assessing their results in human capital management, recommending practical solutions for the sector retail Maldives. In another study on workers’ adaptive performance, Tan and Antonio (2022) using PLS-SEM prove that the new form of remote work and the so-called e-leadership forced by the COVID-19 pandemic have changed the way employers and employees interact. Organizational commitment, teleworking and a sense of purpose affect the adaptive performance of employees directly, while the perception of e-leadership indirectly. It is also one of the first studies to capture intrinsic motivation as the antecedent of employee adaptive performance, along with perceived e-leadership and teleworking results. |
| business intelligence and analytics stevens: Advances in Data Science and Analytics M. Niranjanamurthy, Hemant Kumar Gianey, Amir H. Gandomi, 2022-11-01 ADVANCES in DATA SCIENCE and ANALYTICS Presenting the concepts and advances of data science and analytics, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across multiple disciplines and industries, for both the engineer and the student, focusing on machining learning, big data, business intelligence, and analytics. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning, and big data. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. For the purposes of this volume, data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. Although data mining and other related areas have been around for a few decades, data science and analytics are still quickly evolving, and the processes and technologies change, almost on a day-to-day basis. This volume provides an overview of some of the most important advances in these areas today, including practical coverage of the daily applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library. |
| business intelligence and analytics stevens: Thaddeus Stevens Bruce Levine, 2022-03 A “powerful” (The Wall Street Journal) biography of one of the 19th century’s greatest statesmen, encompassing his decades-long fight against slavery and his postwar struggle to bring racial justice to America. Thaddeus Stevens was among the first to see the Civil War as an opportunity for a second American revolution—a chance to remake the country as a genuine multiracial democracy. As one of the foremost abolitionists in Congress in the years leading up to the war, he was a leader of the young Republican Party’s radical wing, fighting for anti-slavery and anti-racist policies long before party colleagues like Abraham Lincoln endorsed them. These policies—including welcoming black men into the Union’s armies—would prove crucial to the Union war effort. During the Reconstruction era that followed, Stevens demanded equal civil and political rights for Black Americans—rights eventually embodied in the 14th and 15th amendments. But while Stevens in many ways pushed his party—and America—towards equality, he also championed ideas too radical for his fellow Congressmen ever to support, such as confiscating large slaveholders’ estates and dividing the land among those who had been enslaved. In Thaddeus Stevens, acclaimed historian Bruce Levine has written a “vital” (The Guardian), “compelling” (James McPherson) biography of one of the most visionary statesmen of the 19th century and a forgotten champion for racial justice in America. |
| business intelligence and analytics stevens: Big-Data Analytics and Cloud Computing Marcello Trovati, Richard Hill, Ashiq Anjum, Shao Ying Zhu, Lu Liu, 2016-01-12 This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets. |
| business intelligence and analytics stevens: Computational Intelligence, Communications, and Business Analytics J. K. Mandal, Paramartha Dutta, Somnath Mukhopadhyay, 2017-09-22 The two volume set CCIS 775 and 776 constitutes the refereed proceedings of the First International Conference on Computational Intelligence, Communications, and Business Analytics, CICBA 2017, held in Kolkata, India, in March 2017. The 90 revised full papers presented in the two volumes were carefully reviewed and selected from 276 submissions. The papers are organized in topical sections on data science and advanced data analytics; signal processing and communications; microelectronics, sensors, intelligent networks; computational forensics (privacy and security); computational intelligence in bio-computing; computational intelligence in mobile and quantum computing; intelligent data mining and data warehousing; computational intelligence. |
| business intelligence and analytics stevens: Modeling and Visualization of Complex Systems and Enterprises William B. Rouse, 2015-07-27 Explains multi-level models of enterprise systems and covers modeling methodology This book addresses the essential phenomena underlying the overall behaviors of complex systems and enterprises. Understanding these phenomena can enable improving these systems. These phenomena range from physical, behavioral, and organizational, to economic and social, all of which involve significant human components. Specific phenomena of interest and how they are represented depend on the questions of interest and the relevant domains or contexts. Modeling and Visualization of Complex Systems and Enterprises examines visualization of phenomena and how understanding the relationships among phenomena can provide the basis for understanding where deeper exploration is warranted. The author also reviews mathematical and computational models, defined very broadly across disciplines, which can enable deeper understanding. Presents a 10 step methodology for addressing questions associated with the design or operation of complex systems and enterprises Examines six archetypal enterprise problems including two from healthcare, two from urban systems, and one each from financial systems and defense systems Provides an introduction to the nature of complex systems, historical perspectives on complexity and complex adaptive systems, and the evolution of systems practice Modeling and Visualization of Complex Systems and Enterprises is written for graduate students studying systems science and engineering and professionals involved in systems science and engineering, those involved in complex systems such as healthcare delivery, urban systems, sustainable energy, financial systems, and national security. |
| business intelligence and analytics stevens: Analytics in Healthcare Christo El Morr, Hossam Ali-Hassan, 2019-01-21 This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field. |
| business intelligence and analytics stevens: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| business intelligence and analytics stevens: Management Information Systems Kenneth C. Laudon, Jane Price Laudon, 2004 Management Information Systems provides comprehensive and integrative coverage of essential new technologies, information system applications, and their impact on business models and managerial decision-making in an exciting and interactive manner. The twelfth edition focuses on the major changes that have been made in information technology over the past two years, and includes new opening, closing, and Interactive Session cases. |
| business intelligence and analytics stevens: Business Intelligence: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2015-12-29 Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Business Intelligence: Concepts, Methodologies, Tools, and Applications presents a comprehensive examination of business data analytics along with case studies and practical applications for businesses in a variety of fields and corporate arenas. Focusing on topics and issues such as critical success factors, technology adaptation, agile development approaches, fuzzy logic tools, and best practices in business process management, this multivolume reference is of particular use to business analysts, investors, corporate managers, and entrepreneurs in a variety of prominent industries. |
| business intelligence and analytics stevens: Analytics and Big Data: The Davenport Collection (6 Items) Thomas H. Davenport, Jeanne G. Harris, 2014-08-12 The Analytics and Big Data collection offers a “greatest hits” digital compilation of ideas from world-renowned thought leader Thomas Davenport, who helped popularize the terms analytics and big data in the workplace. An agile and prolific thinker, Davenport has written or coauthored more than a dozen bestselling books. Several of these titles are offered together for the first time in this curated digital bundle, including: Big Data at Work, Competing on Analytics, Analytics at Work, and Keeping Up with the Quants. The collection also includes Davenport’s popular Harvard Business Review articles, “Data Scientist: The Sexiest Job of the 21st Century” (2012) and “Analytics 3.0” (2013). Combined, these works cover all the bases on analytics and big data: what each term means; the ramifications of each from a technical, consumer, and management perspective; and where each can have the biggest impact on your business. Whether you’re an executive, a manager, or a student wanting to learn more, Analytics and Big Data is the most comprehensive collection you’ll find on the ever-growing phenomenon of digital data and analysis—and how you can make this rising business trend work for you. Named one of the ten “Masters of the New Economy” by CIO magazine, Thomas Davenport has helped hundreds of companies revitalize their management practices. He combines his interests in research, teaching, and business management as the President’s Distinguished Professor of Information Technology & Management at Babson College. Davenport has also taught at Harvard Business School, the University of Chicago, Dartmouth’s Tuck School of Business, and the University of Texas at Austin and has directed research centers at Accenture, McKinsey & Company, Ernst & Young, and CSC. He is also an independent Senior Advisor to Deloitte Analytics. |
| business intelligence and analytics stevens: Refining the Concept of Scientific Inference When Working with Big Data National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, 2017-02-24 The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. In June 2016 the National Academies of Sciences, Engineering, and Medicine convened a workshop to examine critical challenges and opportunities in performing scientific inference reliably when working with big data. Participants explored new methodologic developments that hold significant promise and potential research program areas for the future. This publication summarizes the presentations and discussions from the workshop. |
| business intelligence and analytics stevens: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| business intelligence and analytics stevens: Integration of Data Mining in Business Intelligence Systems Azevedo, Ana, 2014-09-30 Uncovering and analyzing data associated with the current business environment is essential in maintaining a competitive edge. As such, making informed decisions based on this data is crucial to managers across industries. Integration of Data Mining in Business Intelligence Systems investigates the incorporation of data mining into business technologies used in the decision making process. Emphasizing cutting-edge research and relevant concepts in data discovery and analysis, this book is a comprehensive reference source for policymakers, academicians, researchers, students, technology developers, and professionals interested in the application of data mining techniques and practices in business information systems. |
| business intelligence and analytics stevens: Data Analytics and Decision Support for Cybersecurity Iván Palomares Carrascosa, Harsha Kumara Kalutarage, Yan Huang, 2017-08-01 The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic. |
BUSINESS | English meaning - Cambridge Dictionary
BUSINESS definition: 1. the activity of buying and selling goods and services: 2. a particular company that buys and….
VENTURE | English meaning - Cambridge Dictionary
VENTURE definition: 1. a new activity, usually in business, that involves risk or uncertainty: 2. to risk going….
ENTERPRISE | English meaning - Cambridge Dictionary
ENTERPRISE definition: 1. an organization, especially a business, or a difficult and important plan, especially one that….
INCUMBENT | English meaning - Cambridge Dictionary
INCUMBENT definition: 1. officially having the named position: 2. to be necessary for someone: 3. the person who has or….
AD HOC | English meaning - Cambridge Dictionary
AD HOC definition: 1. made or happening only for a particular purpose or need, not planned before it happens: 2. made….
LEVERAGE | English meaning - Cambridge Dictionary
LEVERAGE definition: 1. the action or advantage of using a lever: 2. power to influence people and get the results you….
ENTREPRENEUR | English meaning - Cambridge Dictionary
ENTREPRENEUR definition: 1. someone who starts their own business, especially when this involves seeing a new opportunity….
CULTIVATE | English meaning - Cambridge Dictionary
CULTIVATE definition: 1. to prepare land and grow crops on it, or to grow a particular crop: 2. to try to develop and….
EQUITY | English meaning - Cambridge Dictionary
EQUITY definition: 1. the value of a company, divided into many equal parts owned by the shareholders, or one of the….
LIAISE | English meaning - Cambridge Dictionary
LIAISE definition: 1. to speak to people in other organizations, etc. in order to work with them or exchange….
BUSINESS | English meaning - Cambridge Dictionary
BUSINESS definition: 1. the activity of buying and selling goods and services: 2. a particular company that buys and….
VENTURE | English meaning - Cambridge Dictionary
VENTURE definition: 1. a new activity, usually in business, that involves risk or uncertainty: 2. to risk going….
ENTERPRISE | English meaning - Cambridge Dictionary
ENTERPRISE definition: 1. an organization, especially a business, or a difficult and important plan, especially one that….
INCUMBENT | English meaning - Cambridge Dictionary
INCUMBENT definition: 1. officially having the named position: 2. to be necessary for someone: 3. the person who has or….
AD HOC | English meaning - Cambridge Dictionary
AD HOC definition: 1. made or happening only for a particular purpose or need, not planned before it happens: 2. made….
LEVERAGE | English meaning - Cambridge Dictionary
LEVERAGE definition: 1. the action or advantage of using a lever: 2. power to influence people and get the results you….
ENTREPRENEUR | English meaning - Cambridge Dictionary
ENTREPRENEUR definition: 1. someone who starts their own business, especially when this involves seeing a new opportunity….
CULTIVATE | English meaning - Cambridge Dictionary
CULTIVATE definition: 1. to prepare land and grow crops on it, or to grow a particular crop: 2. to try to develop and….
EQUITY | English meaning - Cambridge Dictionary
EQUITY definition: 1. the value of a company, divided into many equal parts owned by the shareholders, or one of the….
LIAISE | English meaning - Cambridge Dictionary
LIAISE definition: 1. to speak to people in other organizations, etc. in order to work with them or exchange….
