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The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation.  The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects r

Data Mining and Knowledge Discovery for Big Data

Transforming data into revenue generating strategies and actions Organizations are swamped with data--collected from web traffic, point of sale systems, enterprise resource planning systems, and more, but what to do with it? Monetizing your Data provides a framework and path for business managers to convert ever-increasing volumes of data into revenue generating actions through three disciplines: decision architecture, data science, and guided analytics. There are large gaps between understanding a business problem and knowing which data is relevant to the problem and how to leverage that data to drive significant financial performance. Using a proven methodology developed in the field through delivering meaningful solutions to Fortune 500 companies, this book gives you the analytical tools, methods, and techniques to transform data you already have into information into insights that drive winning decisions. Beginning with an explanation of the analytical cycle, this book guides you t

Monetizing Your Data

Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today’s game changer and should be a key building block in every company’s strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set. Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the p

Machine Learning with Microsoft Technologies

This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business. 

Data Science and Digital Business

At a time characterised by constant progress and digital transformation, the business world is experiencing a revolution. Artificial intelligence (AI) is on the rise. The fascinating developments in the field of artificial intelligence have not only changed the way companies do business, but have also redefined the dynamics of commercial processes. As a key player in this development, AI offers a wealth of opportunities to optimise processes, increase efficiency and open up new horizons for data-driven decision-making.1 The term "artificial intelligence" may still sound like science fiction to many, but it has long since become reality and is shaping the business landscape. AI refers to systems that can imitate human intelligence, learn complex tasks, solve problems and even make decisions independently. In a business context, this technology is increasingly seen as an indispensable resource that is revolutionising the traditional working day and enabling companies to adapt to the dema

AI in the commercial area

Business users familiar with Base SAS programming can now learn Python by example. You will learn via examples that map SAS programming constructs and coding patterns into their Python equivalents. Your primary focus will be on pandas and data management issues related to analysis of data. It is estimated that there are three million or more SAS users worldwide today. As the data science landscape shifts from using SAS to open source software such as Python, many users will feel the need to update their skills. Most users are not formally trained in computer science and have likely acquired their skills programming SAS as part of their job. As a result, the current documentation and plethora of books and websites for learning Python are technical and not geared for most SAS users.  Python for SAS Users provides the most comprehensive set of examples currently available. It contains over 200 Python scripts and approximately 75 SAS programs that are analogs to the Python scripts. The fir

Python for SAS Users

Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of 

Automating the Design of Data Mining Algorithms

ENbook.pl

pWritten by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.ppBased on an MBA course Provost has taught at New York University over the past ten years, iData Science for Businessi provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.pulliUnderstand how data science fits in your organization--and how you can use it for competitive advanta

Data Science for Business | Provost Foster | Twarda

See how data science can answer the questions your business faces  p iApplying Data Science Business Case Studies Using SASi, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions. In eight entertaining and real-world case studies, Svolba combines data science and advanced analytics with business questions, illustrating them with data and SAS code. p The case studies range from a variety of fields, including performing headcount survival analysis for employee retention, forecasting the demand for new projects, using Monte Carlo simulation to understand outcome distribution, among other topics. The data science methods covered include Kaplan-Meier estimates, Cox Proportional Hazard Regression, ARIMA models, Poisson regression, imputation of missing values, variable clustering, and much more  p Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, iApplying Data Sciencei

Applying Data Science | Svolba Gerhard | Twarda

bIf you want to learn about data science and big data, then keep reading...bbrbTwo manuscripts in one book bbrulliData Science What You Need to Know About Data Analytics, Data Mining, Regression Analysis, Artificial Intelligence, Big Data for Business, Data Visualization, Database Querying, and Machine LearningliliBig Data A Guide to Big Data Trends, Artificial Intelligence, Machine Learning, Predictive Analytics, Internet of Things, Data Science, Data Analytics, Business Intelligence, and Data MiningliulThis book will discuss everything that you need to know when it comes to working in the field of data science. This world has changed, and with the modern technology that we have, it is easier than ever for companies to amass a large amount of data on the industry, on their competition, on their products, and their customers. Gathering the data is the easy part, though. Being able to sort through this data and understand what it is saying is going to be a unique challenge all on its ow

Data Science | Hurley Richard | Twarda

pThis book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development how does data science transform existing science, technology, industry, economy, profession and education How does one remain competitive in the data science field What is responsible for shaping the mindset and skillset of data scientistspp ppiData Science Thinkingi paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.ppbrppbrp

Data Science Thinking | Cao Longbing | Twarda

pDo you want to expand your skills from being a basic Data Scientist to becoming an expert Data Scientist ready to solve real-world data centric issuesppstrongExploring this book could be a step in the right direction...strongbr  br  strong2 comprehensive manuscripts in 1 bookstrongp ul liData Science What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don'tli liData Science for Business Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for Beginnersli ul pstrongPart one of this book will cover topics such as strongp ul liWhat Data Science isli liWhat it takes to become an expert in Data Scienceli liBest Data Mining techniques to apply in datali liData visualizationli liLogistic regressionli liData engineeringli liMachine Learningli liBig Data Analyticsli liAnd much more li ul pstrongPart 2 of this book will discuss t

Data Science | Jones Herbert | Twarda

Data science and machine learning - two of the world's hottest fields - are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's 1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. p Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. p Throughout, ibFoundational Python for Data Sciencebi presents hands-on exercises, learning assessments, case studies, and more - all created with co

Foundational Python for Data Science | Behrman Kennedy | Paperback | Twarda

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming.Finally, you'll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them. The need for reproducible report writing is increasing dramatically as data analyses become more comp

Learn Data Analytics For Beginners | Kumar Sathish | Twarda

Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. pWhile data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - iData Science Ethicsi addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques. pReal-life cautionary tales further illustrat

Data Science Ethics | Martens David | Twarda

pbData Science for Infectious Disease Data Analytics An Introduction with Rem embprovides an overview of modern data science tools and methods that have been developed specifically to analyze infectious disease data. With a quick start guide to epidemiological data visualization and analysis in R, this book spans the gulf between academia and practices providing many lively, instructive data analysis examples using the most up-to-date data, such as the newly discovered coronavirus disease COVID-19.ppThe primary emphasis of this book is the data science procedures in epidemiological studies, including data wrangling, visualization, interpretation, predictive modeling, and inference, which is of immense importance due to increasingly diverse and nonexperimental data across a wide range of fields. The knowledge and skills readers gain from this book are also transferable to other areas, such as public health, business analytics, environmental studies, or spatio-temporal data visualization

Data Science for Infectious Disease Data Analytics | Wang Lily | Twarda

bA hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.b pPython is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. iPython for Data Science iintroduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You'll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support. pYou will discover Python's rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go i

Python for Data Science | Vasiliev Yuli | Twarda

pstrongDesigned with beginners in mind, this workshop helps you make the most of Python libraries and the Jupyter Notebook's functionality to understand how data science can be applied to solve real-world data problems.strongppstrongKey Featuresstrongp ul liGain useful insights into data science and machine learningli liExplore the different functionalities and features of a Jupyter Notebookli liDiscover how Python libraries are used with Jupyter for data analysisli ul pstrongBook Descriptionstrongp pFrom banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security.p pTaking an interactive approach to learning the fundamentals, this book is ideal for beginners. You'll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples.p pStarting with

The Applied Data Science Workshop, Second Edition | Galea Alex | Twarda

iData Science and Big Data Analyticsi 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. pThis book will help you p ul liBecome a contributor on a data science teamli liDeploy a structured lifecycle approach to data analytics problemsli liApply appropriate analytic techniques and tools to analyzing big datali liLearn how to tell a compelling story with data to drive business actionli liPrepare for EMC Proven Professional Data Science Certificationli ul pGet started discovering, analyzing, visualizing, and presenting data in a meaningful way today p

Data Science and Big Data Analytics | Emc Education Services | Twarda

This book covers some introductory steps in using R programming language as a data science tool. The data science field has evolved so much recently with incredible quantities of generated data. To extract value from those data, one needs to be trained in the proper data science skills like statistical analysis, data cleaning, data visualization, and machine learning. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all the previous skills, but also, has additional packages that was developed by different scientists in diverse fields. These fields include, but are not limited to, business, marketing, microbiology, social science, geography, genomics, environmental science, etc. Furthermore, R is free software and can run on all major platforms Windows, Mac Os, and UNIXLinux. The first two chapters involve installing and using R and RStudio. RStudio is an IDE integrated development environm

Introduction to R Programming Language | Nady Mohsen | Twarda | Twarda

Data analytics involves inspecting, transforming and modeling data with the objective of discovering useful information and conclusions. Techniques of data analytics can divulge trends and metrics that would otherwise be lost in the mass of information. This information can then be utilized to optimize processes to increase the overall efficacy of a business or system. Data analysis has multiple facets and approaches, encompassing varied techniques under a diverse range of names, and is used in different business, science, and social science domains. It is a process used for obtaining raw data, and converting it into information. This information is then employed in decision-making processes by the users. Data is collected for the purpose of analysis, hypotheses testing, or to disapprove theories. Data analysis plays a pivotal role in making decisions more scientific and helping businesses operate in a more effective manner. This book elucidates the concepts and innovative models aroun

Data Analytics | Bolton Julio | Twarda

strongThe Complete 3 Book Series on Data SciencestrongpstrongBook 1strongpThis book will introduce you to the digital world. Data science is one of the most amazing and trending fields in the digital era. Data science is what makes us humans what we are today. Not limited to computer-driven technologies, this book will guide you to visualize the digital facts and connections of our brain with data science, how to draw conclusions from simple information, and how to develop patterns for understanding different solutions for a similar problem.ppstrongBook 2strongpData scientists are needed in virtually all fields and careers. Platforms like Facebook, Twitter, and even more professional site like LinkedIn are made effective by data scientists. The service of a data scientist is needed in professions such as business and finance organizations, banks, health care centers, and even law firms.pThis book provides a detailed explanation of the theories, algorithms, statistics, and analysis appl

Data Science | Vance William | Twarda

pIn spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions. p Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python. p This book helps you ul liUnderstand the importance of applying spatial relationships in data science liSelect and apply data layering of both raster and vector graphics liApply location data to leverage spatial analytics liDesign informative and accura

Python for Geospatial Data Analysis | McClain Bonny | Twarda

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pHow can music withstand the death and destruction brought on by war Global conflicts of the 20th century fundamentally transformed not only national boundaries, power relations, and global economies, but also the arts and culture of every nation involved. An important, unacknowledged aspect of these conflicts is that they have unique musical soundtracks. iMusic in World War IIi explores how music and sound took on radically different dimensions in the United States and Europe before, during, and after World War II. Additionally, the collection examines the impact of radio and film as the disseminators of the war's musical soundtrack. Contributors contend that the European and American soundtrack of World War II was largely one of escapism rather than the lofty, solemn, heroic, and celebratory mode of war music in the past. Furthermore, they explore the variety of experiences of populations forced from their homes and interned in civilian and POW camps in Europe and the United States, 

Music in World War II | Potter Pamela M. | Twarda

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