Applied Data Science Lessons Learned for the Data-Driven Business

Applied Data Science Lessons Learned for the Data-Driven Business / edited by Martin Braschler, Thilo Stadelmann, Kurt Stockinger.

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is no...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Braschler, Martin (Editor), Stadelmann, Thilo (Editor), Stockinger, Kurt (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:Springer eBook Collection.
Subjects:
Data mining.
Machine learning.
Big data.
Information storage and retrieval.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Preface
  • 1 Introduction
  • 2 Data Science
  • 3 Data Scientists
  • 4 Data products
  • 5 Legal Aspects of Applied Data Science
  • 6 Risks and Side Effects of Data Science and Data Technology
  • 7 Organization
  • 8 What is Data Science?
  • 9 On Developing Data Science
  • 10 The ethics of Big Data applications in the consumer sector
  • 11 Statistical Modelling
  • 12 Beyond ImageNet - Deep Learning in Industrial Practice
  • 13 THE BEAUTY OF SMALL DATA - AN INFORMATION RETRIEVAL PERSPECTIVE
  • 14 Narrative Visualization of Open Data
  • 15 Security of Data Science and Data Science for Security
  • 16 Online Anomaly Detection over Big Data Streams
  • 17 Unsupervised Learning and Simulation for Complexity Management in Business Operations
  • 18 Data Warehousing and Exploratory Analysis for Market Monitoring
  • 19 Mining Person-Centric Datasets for Insight, Prediction, and Public Health Planning
  • 20 Economic Measures of Forecast Accuracy for Demand Planning - A Case-Based Discussion
  • 21 Large-Scale Data-Driven Financial Risk Assessment
  • 22 Governance and IT Architecture
  • 23 Image Analysis at Scale for Finding the Links between Structure and Biology
  • 24 Lessons Learned from Challenging Data Science Case Studies. .

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