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| dba in data science: Business Intelligence Demystified Anoop Kumar V K, 2021-09-25 Clear your doubts about Business Intelligence and start your new journey KEY FEATURES ● Includes successful methods and innovative ideas to achieve success with BI. ● Vendor-neutral, unbiased, and based on experience. ● Highlights practical challenges in BI journeys. ● Covers financial aspects along with technical aspects. ● Showcases multiple BI organization models and the structure of BI teams. DESCRIPTION The book demystifies misconceptions and misinformation about BI. It provides clarity to almost everything related to BI in a simplified and unbiased way. It covers topics right from the definition of BI, terms used in the BI definition, coinage of BI, details of the different main uses of BI, processes that support the main uses, side benefits, and the level of importance of BI, various types of BI based on various parameters, main phases in the BI journey and the challenges faced in each of the phases in the BI journey. It clarifies myths about self-service BI and real-time BI. The book covers the structure of a typical internal BI team, BI organizational models, and the main roles in BI. It also clarifies the doubts around roles in BI. It explores the different components that add to the cost of BI and explains how to calculate the total cost of the ownership of BI and ROI for BI. It covers several ideas, including unconventional ideas to achieve BI success and also learn about IBI. It explains the different types of BI architectures, commonly used technologies, tools, and concepts in BI and provides clarity about the boundary of BI w.r.t technologies, tools, and concepts. The book helps you lay a very strong foundation and provides the right perspective about BI. It enables you to start or restart your journey with BI. WHAT YOU WILL LEARN ● Builds a strong conceptual foundation in BI. ● Gives the right perspective and clarity on BI uses, challenges, and architectures. ● Enables you to make the right decisions on the BI structure, organization model, and budget. ● Explains which type of BI solution is required for your business. ● Applies successful BI ideas. WHO THIS BOOK IS FOR This book is a must-read for business managers, BI aspirants, CxOs, and all those who want to drive the business value with data-driven insights. TABLE OF CONTENTS 1. What is Business Intelligence? 2. Why do Businesses need BI? 3. Types of Business Intelligence 4. Challenges in Business Intelligence 5. Roles in Business Intelligence 6. Financials of Business Intelligence 7. Ideas for Success with BI 8. Introduction to IBI 9. BI Architectures 10. Demystify Tech, Tools, and Concepts in BI |
| dba in data science: Database Administration Craig Mullins, 2002 Giving comprehensive, soup-to-nuts coverage of database administration, this guide is written from a platform-independent viewpoint, emphasizing best practices. |
| dba in data science: Data Science and Big Data Analytics EMC Education Services, 2015-01-05 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! |
| dba in data science: Data Scientist Zacharias Voulgaris, 2014 Learn what a data scientist is and how to become one. As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how. Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist: Learn what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity, and veracity. Explore the different types of Data Scientists and the skillset each one has. Dig into what the role of the Data Scientist requires in terms of the relevant mindset, technical skills, experience, and how the Data Scientist connects with other people. Be a Data Scientist for a day, examining the problems you may encounter and how you tackle them, what programs you use, and how you expand your knowledge and know-how. See how you can become a Data Scientist, based on where you are starting from: a programming, machine learning, or data-related background. Follow step-by-step through the process of landing a Data Scientist job: where you need to look, how you would present yourself to a potential employer, and what it takes to follow a freelancer path. Read the case studies of experienced, senior-level Data Scientists, in an attempt to get a better perspective of what this role is, in practice. At the end of the book, there is a glossary of the most important terms that have been introduced, as well as three appendices - a list of useful sites, some relevant articles on the web, and a list of offline resources for further reading. |
| dba in data science: Developing Analytic Talent Vincent Granville, 2014-03-24 Learn what it takes to succeed in the the most in-demand tech job Harvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code. The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one. Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of terms Shows what companies are looking for and how the growing importance of big data has increased the demand for data scientists Features job interview questions, sample resumes, salary surveys, and examples of job ads Case studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situations Developing Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates. |
| dba in data science: 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! |
| dba in data science: Practitioner’s Guide to Data Science Nasir Ali Mirza, 2022-01-17 Covers Data Science concepts, processes, and the real-world hands-on use cases. KEY FEATURES ● Covers the journey from a basic programmer to an effective Data Science developer. ● Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP. ● Implementation of MLOps using Microsoft Azure DevOps. DESCRIPTION How is the Data Science project to be implemented? has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world's data and how Data Science plays a pivotal role in everything we do. This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects. The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we're at it. By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models. WHAT YOU WILL LEARN ● Organize Data Science projects using CRISP-DM and Microsoft TDSP. ● Learn to acquire and explore data using Python visualizations. ● Get well versed with the implementation of data pre-processing and Feature Engineering. ● Understand algorithm selection, model development, and model evaluation. ● Hands-on with Azure ML Service, its architecture, and capabilities. ● Learn to use Azure ML SDK and MLOps for implementing real-world use cases. WHO THIS BOOK IS FOR This book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions. TABLE OF CONTENTS 1. Data Science for Business 2. Data Science Project Methodologies and Team Processes 3. Business Understanding and Its Data Landscape 4. Acquire, Explore, and Analyze Data 5. Pre-processing and Preparing Data 6. Developing a Machine Learning Model 7. Lap Around Azure ML Service 8. Deploying and Managing Models |
| dba in data science: Data Science Fundamentals and Practical Approaches Dr. Gypsy Nandi, Dr. Rupam Kumar Sharma, 2020-06-02 Learn how to process and analysis data using PythonÊ KEY FEATURESÊ - The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. - The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. - A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. DESCRIPTION This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems.Ê Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic.Ê WHAT WILL YOU LEARNÊ Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. WHO THIS BOOK IS FOR The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. TABLE OF CONTENTS 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics |
| dba in data science: Database Reliability Engineering Laine Campbell, Charity Majors, 2017-10-26 The infrastructure-as-code revolution in IT is also affecting database administration. With this practical book, developers, system administrators, and junior to mid-level DBAs will learn how the modern practice of site reliability engineering applies to the craft of database architecture and operations. Authors Laine Campbell and Charity Majors provide a framework for professionals looking to join the ranks of today’s database reliability engineers (DBRE). You’ll begin by exploring core operational concepts that DBREs need to master. Then you’ll examine a wide range of database persistence options, including how to implement key technologies to provide resilient, scalable, and performant data storage and retrieval. With a firm foundation in database reliability engineering, you’ll be ready to dive into the architecture and operations of any modern database. This book covers: Service-level requirements and risk management Building and evolving an architecture for operational visibility Infrastructure engineering and infrastructure management How to facilitate the release management process Data storage, indexing, and replication Identifying datastore characteristics and best use cases Datastore architectural components and data-driven architectures |
| dba in data science: Encyclopedia of Organizational Knowledge, Administration, and Technology Khosrow-Pour D.B.A., Mehdi, 2020-09-29 For any organization to be successful, it must operate in such a manner that knowledge and information, human resources, and technology are continually taken into consideration and managed effectively. Business concepts are always present regardless of the field or industry – in education, government, healthcare, not-for-profit, engineering, hospitality/tourism, among others. Maintaining organizational awareness and a strategic frame of mind is critical to meeting goals, gaining competitive advantage, and ultimately ensuring sustainability. The Encyclopedia of Organizational Knowledge, Administration, and Technology is an inaugural five-volume publication that offers 193 completely new and previously unpublished articles authored by leading experts on the latest concepts, issues, challenges, innovations, and opportunities covering all aspects of modern organizations. Moreover, it is comprised of content that highlights major breakthroughs, discoveries, and authoritative research results as they pertain to all aspects of organizational growth and development including methodologies that can help companies thrive and analytical tools that assess an organization’s internal health and performance. Insights are offered in key topics such as organizational structure, strategic leadership, information technology management, and business analytics, among others. The knowledge compiled in this publication is designed for entrepreneurs, managers, executives, investors, economic analysts, computer engineers, software programmers, human resource departments, and other industry professionals seeking to understand the latest tools to emerge from this field and who are looking to incorporate them in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to business, management science, organizational development, entrepreneurship, sociology, corporate psychology, computer science, and information technology will benefit from the research compiled within this publication. |
| dba in data science: Methods and Tools for Completing Doctor of Business Administration (DBA) Theses Jacques Digout, Charbel Salloum, Sylvain Senechal, 2019-06-17 This book offers complete and operational methodology guidelines for the entire process of the Doctor of Business Administration (DBA) thesis. It provides insights into theory and practice, both indispensable for the successful completion of the research project. The volume draws on the contributions of major reference works, and offers simplified, clear and applicable standards for DBA participants and supervisors. It illustrates a living experience, because completing a thesis is a human adventure. “Non-classic” students starting a doctoral project are facing an utterly new world with codes and methods they do not recognise. As such, this book brings together many testimonies from DBA scholars, which will help readers to find new formulations and valuable solutions in their own work. |
| dba in data science: Data Science for Transport Charles Fox, 2018-02-27 The quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of 'big data', 'Data Science', and 'smart cities' changing the world, with the Harvard Business Review describing Data Science as the sexiest job of the 21st century. Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues. Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds. - Tom van Vuren, Divisional Director, Mott MacDonald WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you too can now study via this book. - Leighton Cardwell, Technical Director, WSP. From processing and analysing large datasets, to automation of modelling tasks sometimes requiring different software packages to talk to each other, to data visualization, SYSTRA employs a range of techniques and tools to provide our clients with deeper insights and effective solutions. This book does an excellent job in giving you the skills to manage, interrogate and analyse databases, and develop powerful presentations. Another important publication from ITS Leeds. - Fitsum Teklu, Associate Director (Modelling & Appraisal) SYSTRA Ltd Urban planning has relied for decades on statistical and computational practices that have little to do with mainstream data science. Information is still often used as evidence on the impact of new infrastructure even when it hardly contains any valid evidence. This book is an extremely welcome effort to provide young professionals with the skills needed to analyse how cities and transport networks actually work. The book is also highly relevant to anyone who will later want to build digital solutions to optimise urban travel based on emerging data sources. - Yaron Hollander, author of Transport Modelling for a Complete Beginner |
| dba in data science: Analytics in a Big Data World Bart Baesens, 2014-04-15 The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities. |
| dba in data science: Profit Driven Business Analytics Wouter Verbeke, Bart Baesens, Cristian Bravo, 2017-10-09 Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. Reinforce basic analytics to maximize profits Adopt the tools and techniques of successful integration Implement more advanced analytics with a value-centric approach Fine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques. |
| dba in data science: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| dba in data science: Introduction to Biomedical Data Science Robert Hoyt, Robert Muenchen, 2019-11-24 Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises. |
| dba in data science: Oracle Database 10g DBA Handbook Kevin Loney, Bob Bryla, 2005-04-14 Everything a DBA needs to know in one volume--this is the must-have reference for anyone working with the Oracle database, and it’s been fully revised and updated for Oracle Database 10g. Co-author Kevin Loney is the all-time, best-selling Oracle Press author. |
| dba in data science: Hadoop Blueprints Anurag Shrivastava, Tanmay Deshpande, 2016-09-30 Use Hadoop to solve business problems by learning from a rich set of real-life case studies About This Book Solve real-world business problems using Hadoop and other Big Data technologies Build efficient data lakes in Hadoop, and develop systems for various business cases like improving marketing campaigns, fraud detection, and more Power packed with six case studies to get you going with Hadoop for Business Intelligence Who This Book Is For If you are interested in building efficient business solutions using Hadoop, this is the book for you This book assumes that you have basic knowledge of Hadoop, Java, and any scripting language. What You Will Learn Learn about the evolution of Hadoop as the big data platform Understand the basics of Hadoop architecture Build a 360 degree view of your customer using Sqoop and Hive Build and run classification models on Hadoop using BigML Use Spark and Hadoop to build a fraud detection system Develop a churn detection system using Java and MapReduce Build an IoT-based data collection and visualization system Get to grips with building a Hadoop-based Data Lake for large enterprises Learn about the coexistence of NoSQL and In-Memory databases in the Hadoop ecosystem In Detail If you have a basic understanding of Hadoop and want to put your knowledge to use to build fantastic Big Data solutions for business, then this book is for you. Build six real-life, end-to-end solutions using the tools in the Hadoop ecosystem, and take your knowledge of Hadoop to the next level. Start off by understanding various business problems which can be solved using Hadoop. You will also get acquainted with the common architectural patterns which are used to build Hadoop-based solutions. Build a 360-degree view of the customer by working with different types of data, and build an efficient fraud detection system for a financial institution. You will also develop a system in Hadoop to improve the effectiveness of marketing campaigns. Build a churn detection system for a telecom company, develop an Internet of Things (IoT) system to monitor the environment in a factory, and build a data lake – all making use of the concepts and techniques mentioned in this book. The book covers other technologies and frameworks like Apache Spark, Hive, Sqoop, and more, and how they can be used in conjunction with Hadoop. You will be able to try out the solutions explained in the book and use the knowledge gained to extend them further in your own problem space. Style and approach This is an example-driven book where each chapter covers a single business problem and describes its solution by explaining the structure of a dataset and tools required to process it. Every project is demonstrated with a step-by-step approach, and explained in a very easy-to-understand manner. |
| dba in data science: Why Data Science Projects Fail Douglas Gray, Evan Shellshear, 2024-09-05 The field of artificial intelligence, data science, and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects, and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven. This book aims to fix this by countering the AI hype with a dose of realism. Written by two experts in the field, the authors firmly believe in the power of mathematics, computing, and analytics, but if false expectations are set and practitioners and leaders don’t fully understand everything that really goes into data science projects, then a stunning 80% (or more) of analytics projects will continue to fail, costing enterprises and society hundreds of billions of dollars, and leading to non-experts abandoning one of the most important data-driven decision-making capabilities altogether. For the first time, business leaders, practitioners, students, and interested laypeople will learn what really makes a data science project successful. By illustrating with many personal stories, the authors reveal the harsh realities of implementing AI and analytics. |
| dba in data science: Secure Data Science Bhavani Thuraisingham, Murat Kantarcioglu, Latifur Khan, 2022-04-27 Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science. |
| dba in data science: Big Data Analytics with SAS David Pope, 2017-11-23 Leverage the capabilities of SAS to process and analyze Big Data About This Book Combine SAS with platforms such as Hadoop, SAP HANA, and Cloud Foundry-based platforms for effecient Big Data analytics Learn how to use the web browser-based SAS Studio and iPython Jupyter Notebook interfaces with SAS Practical, real-world examples on predictive modeling, forecasting, optimizing and reporting your Big Data analysis with SAS Who This Book Is For SAS professionals and data analysts who wish to perform analytics on Big Data using SAS to gain actionable insights will find this book to be very useful. If you are a data science professional looking to perform large-scale analytics with SAS, this book will also help you. A basic understanding of SAS will be helpful, but is not mandatory. What You Will Learn Configure a free version of SAS in order do hands-on exercises dealing with data management, analysis, and reporting. Understand the basic concepts of the SAS language which consists of the data step (for data preparation) and procedures (or PROCs) for analysis. Make use of the web browser based SAS Studio and iPython Jupyter Notebook interfaces for coding in the SAS, DS2, and FedSQL programming languages. Understand how the DS2 programming language plays an important role in Big Data preparation and analysis using SAS Integrate and work efficiently with Big Data platforms like Hadoop, SAP HANA, and cloud foundry based systems. In Detail SAS has been recognized by Money Magazine and Payscale as one of the top business skills to learn in order to advance one's career. Through innovative data management, analytics, and business intelligence software and services, SAS helps customers solve their business problems by allowing them to make better decisions faster. This book introduces the reader to the SAS and how they can use SAS to perform efficient analysis on any size data, including Big Data. The reader will learn how to prepare data for analysis, perform predictive, forecasting, and optimization analysis and then deploy or report on the results of these analyses. While performing the coding examples within this book the reader will learn how to use the web browser based SAS Studio and iPython Jupyter Notebook interfaces for working with SAS. Finally, the reader will learn how SAS's architecture is engineered and designed to scale up and/or out and be combined with the open source offerings such as Hadoop, Python, and R. By the end of this book, you will be able to clearly understand how you can efficiently analyze Big Data using SAS. Style and approach The book starts off by introducing the reader to SAS and the SAS programming language which provides data management, analytical, and reporting capabilities. Most chapters include hands on examples which highlights how SAS provides The Power to Know©. The reader will learn that if they are looking to perform large-scale data analysis that SAS provides an open platform engineered and designed to scale both up and out which allows the power of SAS to combine with open source offerings such as Hadoop, Python, and R. |
| dba in data science: Data Science with Java Michael R. Brzustowicz, PhD, 2017-06-06 Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You’ll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you’ll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest form Understand the matrix structure that your data should take Learn basic concepts for testing the origin and validity of data Transform your data into stable and usable numerical values Understand supervised and unsupervised learning algorithms, and methods for evaluating their success Get up and running with MapReduce, using customized components suitable for data science algorithms |
| dba in data science: 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. |
| dba in data science: The Next Wave of Sociotechnical Design Leona Chandra Kruse, Stefan Seidel, Geir Inge Hausvik, 2021-07-27 This book constitutes the thoroughly refereed proceedings of the 16th International Conference on Design Science Research in Information Systems and Technology, DESRIST 2021, held in Kristiansand, Norway, in August 2021.* The 24 revised full research papers, included in the volume together with 6 short contributions and 7 prototype papers, were carefully reviewed and selected from 78 submissions. They are organized in the following topical sections: impactful sociotechnical design; problem and contribution articulation; design knowledge for reuse; emerging methods and frameworks for DSR; DSR and governance; the new boundaries of DSR. *Apart from the planned on-site event, the hybrid conference model was explored due to the Covid-19 pandemic. |
| dba in data science: Smarter Data Science Neal Fishman, Cole Stryker, 2020-04-09 Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations. |
| dba in data science: Data Science and Business Intelligence Heverton Anunciação, 2023-12-04 A professional, no matter what area he belongs to, I believe, should never think that his truth is definitive or that his way of doing or solving something is the best. And, logically, I had to get it right and wrong to reach this simple conclusion. Now, what does that have to do with the purpose of this book? This book that I have gathered important tips and advice from an elite of data science professionals from various sectors and reputable experience? After I've worked on hundreds of consulting projects and implementation of best practices in Relationship Marketing (CRM), Business Intelligence (BI) and Customer Experience (CX), as well as countless Information Technology projects, one truth is absolute: We need data! Most companies say they do everything perfect, but it is not shown in the media or the press the headache that the areas of Information Technology suffer to join the right data. And when they do manage to unite and make it available, the time to market has already been lost and possible opportunities. Therefore, if a company wants to be considered excellence in corporate governance and satisfy the legal, marketing, sales, customer service, technology, logistics, products, among other areas, this company must start as soon as possible to become a data driven and real-time company. For this, I recommend companies to look for their digital intuitions, and digital inspirations. So, with this book, I am proposing that all the employees and companies will arrive one day that they will know how to use, from their data, their sixth sense. The sixth sense is an extrasensory perception, which goes beyond our five basic senses, vision, hearing, taste, smell, touch. It is a sensation of intuition, which in a certain way allows us to have sensations of clairvoyance and even visions of future events. A company will only achieve this ability if it immediately begins to apply true data governance. And the illustrious data scientists who are part of this book will show you the way to take the first step: - Eric Siegel, Predictive Analytics World, USA - Bill Inmon, The Father of Datawarehouse, Forest Rim Technology, USA - Bram Nauts, ABN AMRO Bank, Netherlands - Jim Sterne, Digital Analytics Association, USA - Terry Miller, Siemens, USA - Shivanku Misra, Hilton Hotels, USA - Caner Canak, Turkcell, Turkey - Dr. Kirk Borne, Booz Allen Hamilton, USA - Dr. Bülent Kızıltan, Harvard University, USA - Kate Strachnyi, Story by Data, USA - Kristen Kehrer, Data Moves Me, USA - Marie Wallace, IBM Watson Health, Ireland - Timothy Kooi, DHL, Singapore - Jesse Anderson, Big Data Institute, USA - Charles Givre, JPMorgan Chase & Co, USA - Anne Buff, Centene Corporation, USA - Bala Venkatesh, AIBOTS, Malaysia - Mauro Damo, Hitachi Vantara, USA - Dr. Rajkumar Bondugula, Equifax, USA - Waldinei Guimaraes, Experian, Brazil - Michael Ferrari, Atlas Research Innovations, USA - Dr. Aviv Gruber, Tel-Aviv University, Israel - Amit Agarwal, NVIDIA, India This book is part of the CRM and Customer Experience Trilogy called CX Trilogy which aims to unite the worldwide community of CX, Customer Service, Data Science and CRM professionals. I believe that this union would facilitate the contracting of our sector and profession, as well as identifying the best professionals in the market. The CX Trilogy consists of 3 books and a dictionary: 1st) 30 Advice from 30 greatest professionals in CRM and customer service in the world; 2nd) The Book of all Methodologies and Tools to Improve and Profit from Customer Experience and Service; 3rd) Data Science and Business Intelligence - Advice from reputable Data Scientists around the world; and plus, the book: The Official Dictionary for Internet, Computer, ERP, CRM, UX, Analytics, Big Data, Customer Experience, Call Center, Digital Marketing and Telecommunication: The Vocabulary of One New Digital World |
| dba in data science: The Data Science Framework Juan J. Cuadrado-Gallego, Yuri Demchenko, 2020-10-01 This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines. |
| dba in data science: Enabling the New Era of Cloud Computing: Data Security, Transfer, and Management Shen, Yushi, 2013-11-30 Cloud computing is becoming the next revolution in the IT industry; providing central storage for internet data and services that have the potential to bring data transmission performance, security and privacy, data deluge, and inefficient architecture to the next level. Enabling the New Era of Cloud Computing: Data Security, Transfer, and Management discusses cloud computing as an emerging technology and its critical role in the IT industry upgrade and economic development in the future. This book is an essential resource for business decision makers, technology investors, architects and engineers, and cloud consumers interested in the cloud computing future. |
| dba in data science: Statistical Computing with R Maria L. Rizzo, 2007-11-15 Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditiona |
| dba in data science: Data Science and Analytics Strategy Kailash Awati, Alexander Scriven, 2023-04-05 This book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the authors provide actionable advice on data technologies, processes, and governance structures so that readers can make choices that are appropriate to their organisational contexts and requirements. The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness. Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls. |
| dba in data science: Recent Developments in Data Science and Business Analytics Madjid Tavana, Srikanta Patnaik, 2018-03-27 This edited volume is brought out from the contributions of the research papers presented in the International Conference on Data Science and Business Analytics (ICDSBA- 2017), which was held during September 23-25 2017 in ChangSha, China. As we all know, the field of data science and business analytics is emerging at the intersection of the fields of mathematics, statistics, operations research, information systems, computer science and engineering. Data science and business analytics is an interdisciplinary field about processes and systems to extract knowledge or insights from data. Data science and business analytics employ techniques and theories drawn from many fields including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition, visualization, descriptive analytics, predictive analytics, prescriptive analytics, uncertainty modeling, big data, data warehousing, data compression, computer programming, business intelligence, computational intelligence, and high performance computing among others. The volume contains 55 contributions from diverse areas of Data Science and Business Analytics, which has been categorized into five sections, namely: i) Marketing and Supply Chain Analytics; ii) Logistics and Operations Analytics; iii) Financial Analytics. iv) Predictive Modeling and Data Analytics; v) Communications and Information Systems Analytics. The readers shall not only receive the theoretical knowledge about this upcoming area but also cutting edge applications of this domains. |
| dba in data science: Data Teams Jesse Anderson, 2020 |
| dba in data science: Advanced Oracle PL/SQL Developer's Guide Saurabh K. Gupta, 2016-02-15 Master the advanced concepts of PL/SQL for professional-level certification and learn the new capabilities of Oracle Database 12c About This Book Learn advanced application development features of Oracle Database 12c and prepare for the 1Z0-146 examination Build robust and secure applications in Oracle PL/SQL using the best practices Packed with feature demonstrations and illustrations that will help you learn and understand the enhanced capabilities of Oracle Database 12c Who This Book Is For This book is for Oracle developers responsible for database management. Readers are expected to have basic knowledge of Oracle Database and the fundamentals of PL/SQL programming. Certification aspirants can use this book to prepare for 1Z0-146 examination in order to be an Oracle Certified Professional in Advanced PL/SQL. What You Will Learn Learn and understand the key SQL and PL/SQL features of Oracle Database 12c Understand the new Multitenant architecture and Database In-Memory option of Oracle Database 12c Know more about the advanced concepts of the Oracle PL/SQL language such as external procedures, securing data using Virtual Private Database (VPD), SecureFiles, and PL/SQL code tracing and profiling Implement Virtual Private Databases to prevent unauthorized data access Trace, analyze, profile, and debug PL/SQL code while developing database applications Integrate the new application development features of Oracle Database 12c with the current concepts Discover techniques to analyze and maintain PL/SQL code Get acquainted with the best practices of writing PL/SQL code and develop secure applications In Detail Oracle Database is one of the most popular databases and allows users to make efficient use of their resources and to enhance service levels while reducing the IT costs incurred. Oracle Database is sometimes compared with Microsoft SQL Server, however, Oracle Database clearly supersedes SQL server in terms of high availability and addressing planned and unplanned downtime. Oracle PL/SQL provides a rich platform for application developers to code and build scalable database applications and introduces multiple new features and enhancements to improve development experience. Advanced Oracle PL/SQL Developer's Guide, Second Edition is a handy technical reference for seasoned professionals in the database development space. This book starts with a refresher of fundamental concepts of PL/SQL, such as anonymous block, subprograms, and exceptions, and prepares you for the upcoming advanced concepts. The next chapter introduces you to the new features of Oracle Database 12c, not limited to PL/SQL. In this chapter, you will understand some of the most talked about features such as Multitenant and Database In-Memory. Moving forward, each chapter introduces advanced concepts with the help of demonstrations, and provides you with the latest update from Oracle Database 12c context. This helps you to visualize the pre- and post-applications of a feature over the database releases. By the end of this book, you will have become an expert in PL/SQL programming and will be able to implement advanced concepts of PL/SQL for efficient management of Oracle Database. Style and approach The book follows the structure of the Oracle Certification examination but doesn't restrict itself to the exam objectives. Advanced concepts have been explained in an easy-to-understand style, supported with feature demonstrations and case illustrations. |
| dba in data science: Data Wrangling M. Niranjanamurthy, Kavita Sheoran, Geetika Dhand, Prabhjot Kaur, 2023-07-20 DATA WRANGLING Written and edited by some of the world’s top experts in the field, this exciting new volume provides state-of-the-art research and latest technological breakthroughs in data wrangling, its theoretical concepts, practical applications, and tools for solving everyday problems. Data wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis. This process typically includes manually converting and mapping data from one raw form into another format to allow for more convenient consumption and organization of the data. Data wrangling is increasingly ubiquitous at today’s top firms. Data cleaning focuses on removing inaccurate data from your data set whereas data wrangling focuses on transforming the data’s format, typically by converting “raw” data into another format more suitable for use. Data wrangling is a necessary component of any business. Data wrangling solutions are specifically designed and architected to handle diverse, complex data at any scale, including many applications, such as Datameer, Infogix, Paxata, Talend, Tamr, TMMData, and Trifacta. This book synthesizes the processes of data wrangling into a comprehensive overview, with a strong focus on recent and rapidly evolving agile analytic processes in data-driven enterprises, for businesses and other enterprises to use to find solutions for their everyday problems and practical applications. Whether for the veteran engineer, scientist, or other industry professional, this book is a must have for any library. |
| dba in data science: Research Anthology on Big Data Analytics, Architectures, and Applications Management Association, Information Resources, 2021-09-24 Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians. |
| dba in data science: Teaching Data Analytics Susan 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. |
| dba in data science: Think Like a Data Scientist Brian Godsey, 2017-03-09 Summary Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Table of Contents PART 1 - PREPARING AND GATHERING DATA AND KNOWLEDGE Philosophies of data science Setting goals by asking good questions Data all around us: the virtual wilderness Data wrangling: from capture to domestication Data assessment: poking and prodding PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS Developing a plan Statistics and modeling: concepts and foundations Software: statistics in action Supplementary software: bigger, faster, more efficient Plan execution: putting it all together PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP Delivering a product After product delivery: problems and revisions Wrapping up: putting the project away |
| dba in data science: Emerging Perspectives in Big Data Warehousing Taniar, David, Rahayu, Wenny, 2019-06-28 The concept of a big data warehouse appeared in order to store moving data objects and temporal data information. Moving objects are geometries that change their position and shape continuously over time. In order to support spatio-temporal data, a data model and associated query language is needed for supporting moving objects. Emerging Perspectives in Big Data Warehousing is an essential research publication that explores current innovative activities focusing on the integration between data warehousing and data mining with an emphasis on the applicability to real-world problems. Featuring a wide range of topics such as index structures, ontology, and user behavior, this book is ideally designed for IT consultants, researchers, professionals, computer scientists, academicians, and managers. |
| dba in data science: Predictive Intelligence Using Big Data and the Internet of Things Gupta, P.K., Ören, Tuncer, Singh, Mayank, 2018-12-28 With the recent growth of big data and the internet of things (IoT), individuals can now upload, retrieve, store, and collect massive amounts of information to help drive decisions and optimize processes. Due to this, a new age of predictive computing is taking place, and data can now be harnessed to predict unknown occurrences or probabilities based on data collected in real time. Predictive Intelligence Using Big Data and the Internet of Things highlights state-of-the-art research on predictive intelligence using big data, the IoT, and related areas to ensure quality assurance and compatible IoT systems. Featuring coverage on predictive application scenarios to discuss these breakthroughs in real-world settings and various methods, frameworks, algorithms, and security concerns for predictive intelligence, this book is ideally designed for academicians, researchers, advanced-level students, and technology developers. |
| dba in data science: Data Science Programming All-in-One For Dummies John Paul Mueller, Luca Massaron, 2020-01-09 Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s! |
Database Modeling and Design - University of Michigan
database administrator (DBA) -- person or group responsible for the effective use of database technology in an organization or enterprise. Motivation: control over all phases of the lifecycle. …
College of Business - Westcliff University
The DBA Program offers four (4) concentrations: Business Intelligence and Data Analytics (BIDA) Strategic Leadership for the 21st Century Information Technology Management (ITM) Applied …
The Role of a DBA - Knight Foundation School of Computing …
• The DBA must ensure data integrity across the database so that accurate data is queried and maintained, such as using referential integrity, triggers, and constraints. • DBAs must conduct …
Doctor of Business Administration (DBA) - Washington …
The Olin Business School Doctor of Business Administration (DBA) degree program offers a collaborative atmosphere centered on industry-relevant applied research in finance, marketing …
At Ottawa University
Ottawa University’s Doctor of Business Administration (DBA) degree program is an application-based doctoral program that combines a rigorous approach to scholarship with a focus on …
Doctor in Business Administration (DBA) in Operations
After successfully passing the DBA qualifying exam the student will write an extended research paper under the guidance of a faculty advisor. Prior to proposing the doctoral thesis, the …
(DBA) - Westcliff University
DBA Completion (Degree earned) Earn your Doctoral Certificate in Business Administration and take away practical key skills in leadership, marketing, high level problem solving, and …
DATABASE ADMINISTRATOR (DBA) ROLES AND …
A database administrator, or DBA for short, designs, implements, administers, and monitors data management systems and ensures consistency, quality, security, and compliance with rules …
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25M31CA116 Introduction to Data Science 3 0 0 3 PCC Total Credits 20 Semester II Course Codes Courses L T P Credits XXXXXXXXX Research Methods in Business 3 1 0 4 PCC …
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Figure 1-3: Simplified design for a three-step DBA thesis Figure 1-4: Evaluation process for DBA thesis projects Figure 1-5: The three-step method for the DBA project
Executive DBA - Business Science Institute
Business Science Institute’s DBA is a true doctoral programme for practising managers that meets the international standards recommended by EQUAL1. Our DBA is also one of the few …
Data Science - University of Florida
The Data Science major enables students to achieve proficiency in the fundamentals of programming, databases, and statistical reasoning. Through coursework and projects, …
DBA in Finance and Business Analytics - olin.wustl.edu
Olin’s Doctorate in Business Administration (DBA) in Finance and Business Analytics is a prestigious, STEM-designated degree that prepares exceptional individuals for advanced …
MITIGATION STRATEGIES FOR CHALLENGES IN ADOPTION …
Though it is not new, adaptation of data science in the digital transformation journey of a manufacturing industry is facing many challenges like change management, human / social …
DBA Handbook 04012022 - Westcliff University
The DBA Program offers four (4) concentrations: Business Intelligence and Data Analytics (BIDA) Strategic Leadership for the 21st Century, Information Technology Management (ITM), and …
The Doctor of Business Administration (D.B.A.) Program …
1.3 ACCEPTABLE TYPES OF RESEARCH (DBA) The following examples are types of DBA research projects that could be carried out in alignment with DSP standards at Trident at AIU. …
RESEARCH METHODS FOR THE DBA - puq.ca
The 30 chapters of this book have been prepared to the attention of DBA candidates, to orient and guide them in choosing research methods and tools.
Impact of the Intersection of Artificial Intelligence and the …
DBA-Data Science, European School of Data Science and Technology C/O Rushford Business School, Blegistrasse 15, 6340 Baar, Switzerland ala.boubaker@gmail.com Abstract— This …
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Goal: The course covers about: principles of DBA Roles, DB backup, restoration and recovery, Tuning of database and overall DB administration which could be useful for administrator in …
Pallavi Chitturi - Fox School of Business and Management
Pallavi Chitturi is Professor in the Department of Statistics, Operations, and Data Science and Academic Director of the Executive Doctorate in Business Administration program. Dr. Chitturi …
Database Modeling and Design - University of Michigan
database administrator (DBA) -- person or group responsible for the effective use of database technology in an organization or enterprise. Motivation: control over all phases of the lifecycle. …
College of Business - Westcliff University
The DBA Program offers four (4) concentrations: Business Intelligence and Data Analytics (BIDA) Strategic Leadership for the 21st Century Information Technology Management (ITM) Applied …
The Role of a DBA - Knight Foundation School of Computing …
• The DBA must ensure data integrity across the database so that accurate data is queried and maintained, such as using referential integrity, triggers, and constraints. • DBAs must conduct …
Doctor of Business Administration (DBA) - Washington …
The Olin Business School Doctor of Business Administration (DBA) degree program offers a collaborative atmosphere centered on industry-relevant applied research in finance, marketing …
At Ottawa University
Ottawa University’s Doctor of Business Administration (DBA) degree program is an application-based doctoral program that combines a rigorous approach to scholarship with a focus on …
Doctor in Business Administration (DBA) in Operations
After successfully passing the DBA qualifying exam the student will write an extended research paper under the guidance of a faculty advisor. Prior to proposing the doctoral thesis, the …
(DBA) - Westcliff University
DBA Completion (Degree earned) Earn your Doctoral Certificate in Business Administration and take away practical key skills in leadership, marketing, high level problem solving, and …
DATABASE ADMINISTRATOR (DBA) ROLES AND …
A database administrator, or DBA for short, designs, implements, administers, and monitors data management systems and ensures consistency, quality, security, and compliance with rules …
JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY
25M31CA116 Introduction to Data Science 3 0 0 3 PCC Total Credits 20 Semester II Course Codes Courses L T P Credits XXXXXXXXX Research Methods in Business 3 1 0 4 PCC …
Methods and Tools for Completing Doctor of Business …
Figure 1-3: Simplified design for a three-step DBA thesis Figure 1-4: Evaluation process for DBA thesis projects Figure 1-5: The three-step method for the DBA project
Executive DBA - Business Science Institute
Business Science Institute’s DBA is a true doctoral programme for practising managers that meets the international standards recommended by EQUAL1. Our DBA is also one of the few …
Data Science - University of Florida
The Data Science major enables students to achieve proficiency in the fundamentals of programming, databases, and statistical reasoning. Through coursework and projects, …
DBA in Finance and Business Analytics - olin.wustl.edu
Olin’s Doctorate in Business Administration (DBA) in Finance and Business Analytics is a prestigious, STEM-designated degree that prepares exceptional individuals for advanced …
MITIGATION STRATEGIES FOR CHALLENGES IN …
Though it is not new, adaptation of data science in the digital transformation journey of a manufacturing industry is facing many challenges like change management, human / social …
DBA Handbook 04012022 - Westcliff University
The DBA Program offers four (4) concentrations: Business Intelligence and Data Analytics (BIDA) Strategic Leadership for the 21st Century, Information Technology Management (ITM), and …
The Doctor of Business Administration (D.B.A.) Program …
1.3 ACCEPTABLE TYPES OF RESEARCH (DBA) The following examples are types of DBA research projects that could be carried out in alignment with DSP standards at Trident at AIU. …
RESEARCH METHODS FOR THE DBA - puq.ca
The 30 chapters of this book have been prepared to the attention of DBA candidates, to orient and guide them in choosing research methods and tools.
Impact of the Intersection of Artificial Intelligence and the …
DBA-Data Science, European School of Data Science and Technology C/O Rushford Business School, Blegistrasse 15, 6340 Baar, Switzerland ala.boubaker@gmail.com Abstract— This …
Tribhuvan University Institute of Science and Technology B.Sc.
Goal: The course covers about: principles of DBA Roles, DB backup, restoration and recovery, Tuning of database and overall DB administration which could be useful for administrator in …
Pallavi Chitturi - Fox School of Business and Management
Pallavi Chitturi is Professor in the Department of Statistics, Operations, and Data Science and Academic Director of the Executive Doctorate in Business Administration program. Dr. Chitturi …
