Exploit the power and potential of Big Data to revolutionize business outcomesBig Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data. In this collaborative work by an IBM Vice President of Big Data Products and an Oxford Research Fellow, this book presents inside stories that demonstrate the power and potential of Big Data within the business realm. Readers are guided through...

Exploit the power and potential of Big Data to revolutionize business outcomesBig Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data. In this collaborative work by an IBM Vice President of Big Data Products and an Oxford Research Fellow, this book presents inside stories that demonstrate the power and potential of Big Data within the business realm. Listeners are guided through tried-and-true...

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective....

Until recently, many people thought big data was a passing fad. "Data science" was an enigmatic term. Today, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you’ll appreciate how data science is fundamentally altering our world, for better and for worse.Barlow paints a picture of the emerging data space in broad strokes. From new techniques and tools to the use of data for social g...

Utilize R to uncover hidden patterns in your Big Data Key Features\[\* \] Perform computational analyses on Big Data to generate meaningful results\[\*\] Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases,\[\*\] Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the marketBook DescriptionBig Data an...

Over 60 practical recipes on data exploration and analysisKey Features\[\] Clean dirty data, extract accurate information, and explore the relationships between variables\[\] Forecast the output of an electric plant and the water flow of American rivers using pandas, NumPy, Statsmodels, and scikit-learn\[\] Find and extract the most important features from your dataset using the most efficient Python librariesBook Description...

Leverage the power of Python to clean, scrape, analyze, and visualize your dataAbout This BookClean, format, and explore your data using the popular Python libraries and get valuable insights from itAnalyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and morePacked with easy-to-follow examples to develop advanced computational skills for the analys...

Master text-taming techniques and build effective text-processing applications with RKey Features\[\*\] Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide\[\*\] Gain in-depth understanding of the text mining process with lucid implementation in the R language\[\*\] Example-rich guide that lets you gain high-quality information from text dataBook DescriptionText Mining (or text ...

Key FeaturesBook DescriptionThis book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions,...

Harvard Business Review has described the data scientist as a high-ranking professional with the training and curiosity to make discoveries in the world of Big Data, and called that role “the sexiest job of the 21st century.” Amy Affelt, author of The Accidental Data Scientist, notes that “Librarians and information professionals have always worked with data in order to meet the information needs of their constituents, thus ‘Big Data’ is not a new concept for them.” With The Accidental Dat...

Explore the world of data science through Python and learn how to make sense of data Key FeaturesMaster data science methods using Python and its librariesCreate data visualizations and mine for patternsAdvanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learningBook DescriptionData science is a relatively new knowledge domain which is used by...

Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over th...