Titlebook: Data Science in Practice; Alan Said,Vicen? Torra Book 2019 Springer Nature Switzerland AG 2019 Data Science applications.Big Data.Intellig

malcontented

書目名稱Data Science in Practice影響因子(影響力)




書目名稱Data Science in Practice影響因子(影響力)學(xué)科排名




書目名稱Data Science in Practice網(wǎng)絡(luò)公開度




書目名稱Data Science in Practice網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Science in Practice被引頻次




書目名稱Data Science in Practice被引頻次學(xué)科排名




書目名稱Data Science in Practice年度引用




書目名稱Data Science in Practice年度引用學(xué)科排名




書目名稱Data Science in Practice讀者反饋




書目名稱Data Science in Practice讀者反饋學(xué)科排名

obsolete

凌辱

Aids209

Complex Data Analysishat can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.

coddle

Data Science: An Introduction,elevant tools that can be used in data science ranging from optimization to software. We also discuss the relevance of building models from data. The chapter ends with a detailed review of the structure of the book.

RUPT

RUPT

Cengiz Kahraman,Cafer Erhan Bozda?hat can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.

構(gòu)想

Book 2019d how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.

彎腰

無效

Business Intelligenceloratory, inferential, and predictive. This is followed by a discussion on metric design, data dictionaries, and key performance indicators. We end by reviewing the role of business intelligence teams and some of the current trends in business intelligence.