الصفحة 3
الصفحة 3
Deep Data Analytics for New Product Development
The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole.
Data visualization and analysis in second language research
This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages.
Data science, AI, and machine learning in drug development
The confluence of big data, AI, and machine learning has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R&D, emerging applications of big data, AI and machine learning in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations
Data science on the Google cloud platform : Implementing end-to-end real-time data pipelines : From ingest to machine learning
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. You'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines
Data science in theory and practice : Techniques for big data analytics and complex data sets
Delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. Readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets
Data science for economics and finance : Methodologies and applications
The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis.
Data science and data analytics : Opportunities and challenges
Gives the concept of data science, tools, and algorithms that exist for many useful applications / Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems / Identifies many areas and uses of data science in the smart era / Applies data science to agriculture, healthcare, graph mining, education, security, etc.
Data science and analytics ; 5th International conference on recent developments in science, engineering and technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part II
This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics
Data science and analytics ; 5th International conference on recent developments in science, engineering and technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part I
This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.
Data Privacy and Trust in Cloud Computing : Building trust in the cloud through assurance and accountability
This book brings together perspectives from multiple disciplines including psychology, law, IS, and computer science on data privacy and trust in the cloud. Cloud technology has fueled rapid, dramatic technological change, enabling a level of connectivity that has never been seen before in human history.
Data mining and machine learning applications
Elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data.
Data Management Technologies and Applications ; 8th International Conference, DATA 2019, Prague, Czech Republic, July 26–28, 2019, Revised Selected Papers
This book constitutes the thoroughly refereed proceedings of the 8th International Conference on Data Management Technologies and Applications, DATA 2019, held in Prague, Czech Republic, in July 2019. The 8 revised full papers were carefully reviewed and selected from 90 submissions. The papers deal with the following topics: decision support systems, data analytics, data and information quality, digital rights management, big data, knowledge management, ontology engineering, digital libraries, mobile databases, object-oriented database systems, and data integrity.
Data collection in fragile states : Innovations from Africa and beyond
This book addresses an urgent issue on which little organized information exists. It reflects experience in Africa but is highly relevant to other fragile states as well. —Constantine Michalopoulos, John Hopkins University, USA and former Director of Economic Policy and Co-ordination at the World Bank
Data and applications security and privacy XXXIV ; 34th Annual IFIP WG 11.3 Conference, DBSec 2020, Regensburg, Germany, June 25–26, 2020, Proceedings
This book constitutes the refereed proceedings of the 34th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2020, held in Regensburg, Germany, in June 2020.* The 14 full papers and 8 short papers presented were carefully reviewed and selected from 39 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections named network and cyber-physical systems security; information flow and access control; privacy-preserving computation; visualization and analytics for security; spatial systems and crowdsourcing security; and secure outsourcing and privacy.
Data analytics, computational statistics, and operations research for engineers : Methodologies and applications
Presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information.
Data Analytics for Accounting
Data Analytics is changing the business world-data simply surrounds us! So much data is available to businesses about each of us-how we shop, what we read, what we buy, what music we listen to, where we travel, whom we trust, where we invest our time and money, and so on. Accountants create value by addressing fundamental business and accounting questions using Data Analytics
Data Algorithms with Spark
Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Build and apply a model using PySpark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data Learn how to use and apply feature engineering in ML algorithms Understand and use practical and pragmatic data design patterns
Creative applications of artificial intelligence in education
Explores the synergy between AI and education, highlighting its potential impact on pedagogical practices. It navigates the evolving landscape of AI-powered educational technologies and suggests practical ways to personalise instruction, nurture human-AI co-creativity, and transform the learning experience. Spanning from primary to higher education, this short and engaging volume proposes concrete examples of how educational stakeholders can be empowered in their AI literacy to foster creativity, inspire critical thinking, and promote problem-solving by embracing AI as a tool for expansive learning. Structured in three parts, the book starts developing the creative engagement perspective for learning and teaching to then present practical applications of AI in K-12 and higher education, covering different fields (teacher education, professional education, business education) as well as different types of AI supported tools (games, chatbots, and AI assisted assessment).
Correlated Data Analysis : Modeling, Analytics, and Applications
Presents some recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to handle a broader range of data types than those analyzed by traditional generalized linear models.
Corporate financial accounting
This book clearly presents core financial accounting concepts within a meaningful business context, using engaging learning features that ensure a relevant, innovative learning experience. Updated content addresses the latest development and tools in corporate accounting today -- from using data analytics effectively to understanding the impact of discounts and sales in retail accounting. CNOWv2 online resources work with practice opportunities to ensure you understand and can apply key accounting principles.
