Applied Data Science Lessons Learned for the Data-Driven Business
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...
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2019, 2019
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| Edition: | 1st ed. 2019 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
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-DrivenFinancial 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.