Applied Data Science Lessons Learned for the Data Driven Business 1st edition by Martin Braschler, Thilo Stadelmann, Kurt Stockinger 3030118211 9783030118211

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ISBN-10 :  3030118211 

ISBN-13 : 9783030118211

Author:  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 not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other.   With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are.   The book targets professionals as well as students of data science:first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want  to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.  

Applied Data Science: Lessons Learned for the Data-Driven Business 1st table of contents:

Part I: Foundations
Chapter 1: Introduction to Applied Data Science
1 Applied Data Science
2 The History of Data Science
2.1 Data Science, Business, and Hype
2.2 Different Waves of Big Data
3 Data Science and Global Mega Trends
3.1 Big Data
3.2 Artificial Intelligence
3.3 Digitalization
4 Outlook
References
Chapter 2: Data Science
1 Introduction
2 Applied Data Science
3 Interdisciplinarity in Data Science
3.1 Computer Science
3.2 Statistics
3.3 Artificial Intelligence
3.4 Data Mining
3.5 Additional Technical Disciplines
3.6 The “Knowledge Discovery in Databases (KDD)´´ Process
3.7 Data or Information Visualization
3.8 Arts
3.9 Communication
3.10 Entrepreneurship
4 Value Creation in Data Science
5 Conclusions
References
Chapter 3: Data Scientists
1 Introduction
2 The Data Scientist´s Set of Skills and Qualities
3 Disambiguation
3.1 The History of a Job Description
3.2 Insightful Debates
4 Starting a Data Science Career
5 Building Data Science Teams
5.1 Operational Models for Advanced Analytics
5.2 Data-Driven Use Cases
6 Summary
References
Chapter 4: Data Products
1 Introduction
2 Service Design
3 The Gap Toward Data Product Design
4 Bridging the Gap (Then and Now)
5 The Essential Building Block of a Data Product
6 Discussion and Conclusions
References
Chapter 5: Legal Aspects of Applied Data Science
1 Introduction and Background Information
2 “Data´´ from a Legal Standpoint: Goods, Intellectual Property, and Unfair Competition Law
2.1 Introductory Comments
2.2 Ownership of Data as Ownership of Goods?
2.3 Copyrights
2.4 Database Right Sui Generis
2.5 Unfair Competition Law
2.6 Manufacturing and Trade Secrets
3 Data Protection/Privacy
3.1 Background
3.2 Personal Data
3.3 Privacy by Design
3.4 Privacy by Default
3.5 Automated Individual Decisions
3.6 Self-Regulation
4 Regulatory Aspects
5 Conclusion
References
Chapter 6: Risks and Side Effects of Data Science and Data Technology
1 Introduction
2 Main Risks and Side Effects
3 Important Aspects
4 The Battle Field: Individual Freedom Versus Institutional Optimization
5 The Mistake: Choice of an Incorrect Frame
6 The Second-Order Problem of Science and Data Science
7 Conclusion
References
Part II: Use Cases
Chapter 7: Organization
Chapter 8: What Is Data Science?
1 Introduction
2 What Is Data Science?
3 Data Science Is a New Paradigm of Discovery
3.1 Data Science Data, Models, and Methods
3.2 Data Science Fundamentals: Is Data Science a Science?
3.3 The Prime Benefit of Data Science Is Accelerating Discovery
3.4 Causal Reasoning in Data Science Is Complex and Can Be Dangerous
3.5 Data Science Flexibility: Data-Driven or Hypothesis-Driven
3.6 Data Science Is in Its Infancy
3.7 It´s More Complicated Than That
4 Data Science Components
4.1 Data Science Principles, Data, Models, and Methods
4.2 Data Science Workflows or Pipelines
4.3 Data Science and Data Infrastructures
5 What Is the Method for Conducting Data Science?
5.1 A Generic Data Science Method
6 What Is Data Science in Practice?
6.1 Kepler Space Telescope: Discovering Exoplanets
6.2 LIGO: Detecting Gravitational Waves
6.3 Baylor-Watson: Cancer Drug Discovery
7 How Important Is Collaboration in Data Science?
8 What Is World-Class Data Science Research?
9 Conclusions
References
Chapter 9: On Developing Data Science
1 Introduction
2 Twentieth Century Virtuous Cycles
3 Twenty-First Century Virtuous Research, Development, and Delivery Cycles
3.1 The Virtuous DBMS RDandD Cycle
3.2 The Critical Role of Research-Industry Collaboration in Technology Innovation
3.3 The Role of Innovation in RDandD Cycles
3.4 Establishing Causality: A Critical Challenge
4 Applying Twenty-First Century Virtuous RDandD Cycles to Data Science
4.1 A Data Science RDandD Cycle Example
4.2 Developing Data Science in Practice and as a Discipline
4.3 Developing Data Science Education
5 Lessons Learned
5.1 Data Science and DSRI Stages of Development
5.2 Myths of Applying Data Science in Business
6 Potential Impacts of Data Science
6.1 Benefits
6.2 Threats
6.3 More Profound Questions
7 Conclusions
References
Chapter 10: The Ethics of Big Data Applications in the Consumer Sector
1 Introduction
2 Background Information
2.1 Big Data Ethics
2.2 Methodology of the Study
3 Big Data in the Scientific Literature and in Business
3.1 Bibliometric Study
3.2 Use Cases
4 Ethical Evaluation
4.1 Protection of Privacy
4.2 Equality and Non-discrimination
4.3 Informational Self-Determination
4.4 Controlling the (Digital) Identity
4.5 Transparency
4.6 Solidarity
4.7 Contextual Integrity
4.8 Property and Copyrights
5 Lessons Learned
6 Conclusions
References
Chapter 11: Statistical Modelling
1 Introduction
2 Background Information
2.1 Multiple Linear Regression
2.2 Logistic Regression
2.3 Time-to-Event Models
3 Statistical Regression Models
3.1 Multiple Linear Regression for Continuous Response
3.2 Logistic Regression for Binary Response
3.3 Regression Models for Time-to-Event Data Considering Censoring
4 Conclusions
References
Chapter 12: Beyond ImageNet: Deep Learning in Industrial Practice
1 Introduction to Deep Learning
1.1 Fully Connected Neural Networks for Classification
1.2 Convolutional Neural Networks (CNNs)
1.3 Non-obvious Use Cases
2 Learning to Classify: Single Cell Phenotype Classification Using CNNs
2.1 Baseline Approach
2.2 CNN Analysis
2.3 Results and Discussion
3 Learning to Cluster: Extracting Relevant Features for Speaker Diarization
3.1 Supervised Learning for Improved Unsupervised Speaker Clustering
3.2 Results
4 Learning to Segment: FCNs for Semantic Segmentation of Newspaper Pages
4.1 CNN-Based Pixel Classification vs. One-Pass FCNs
4.2 Results
5 Learning to Detect Outliers: Predictive Maintenance with Unsupervised Deep Learning
5.1 Classical Approaches
5.2 Deep Learning-Based Methods
6 Lessons Learned
6.1 Working with Limited Resources
6.2 Other Advice
References
Chapter 13: The Beauty of Small Data: An Information Retrieval Perspective
1 Introduction
2 The Academic Field of Information Retrieval and Related Work
3 The Changing Matching Task
3.1 The Retrieval Problem and Its Interaction with Collection Size
3.2 Hapax Legomena
3.3 Term Weighting Depending on Collection Size
3.4 Impact of Collection Size on Vocabulary
4 Example Retrieval Applications Operating on Small Data
5 Conclusions: Best Practices for Retrieval on Small Document Collections (Small Data)
References
Chapter 14: Narrative Visualization of Open Data
1 Introduction to Open Data
2 Visualization Techniques
3 Data Visualization Workflow
4 Visualization for Exploring Open Data
5 Narrative Visualization for Presenting Open Data
6 Lessons Learned
7 Conclusions
References
Chapter 15: Security of Data Science and Data Science for Security
1 Introduction
2 Key Concepts of Information Security
3 Security of Data Science
3.1 Infrastructure Security
3.2 Software Security
3.3 Data Protection
3.4 Privacy Preservation/Data Anonymization
3.5 Machine Learning Under Attack
4 Data Science for Security
4.1 Anomaly Detection
4.2 Malware Detection and Classification
4.3 Threat Detection
5 Case Study: Detecting Obfuscated JavaScripts
6 Conclusions and Lessons Learned
References
Chapter 16: Online Anomaly Detection over Big Data Streams
1 Introduction
2 Background Information
2.1 Technologies
2.1.1 Apache Spark
2.1.2 Spark Streaming
2.1.3 Apache Kafka and Real-World Data Streams
2.2 Anomaly Detection Measures
3 Anomaly Detection System
3.1 Stream Processing
3.1.1 Relative Entropy Pipeline
3.1.2 Pearson Correlation Pipeline
4 Empirical Evaluation of the Anomaly Detection System
4.1 Anomaly Detection Accuracy
4.1.1 Relative Entropy Accuracy
4.1.2 Pearson Correlation Accuracy
4.2 Comparison to State-of-the-Art Anomaly Detection Techniques
4.2.1 Volume of Telecommunication Activity
4.2.2 k-Means Clustering
4.3 Scalability of the Algorithms
5 Discussion
5.1 Type of Change: Gradual or Abrupt
5.2 Spatial Granularity
5.3 Efficiency
5.4 Limitations
6 Related Work
6.1 Data Streams
6.2 Anomaly Detection on Time Series and Data Streams
7 Conclusion
7.1 Lessons Learned
References
Chapter 17: Unsupervised Learning and Simulation for Complexity Management in Business Operations
1 Introduction
2 Case Study: Complexity Management in Business Operations
3 Linking Simulation and Learning
3.1 Simulation Models Can Provide Data
3.2 A Concrete Example: The Job Shop Model
3.3 A Novel Neural Net-Based Complexity Measure of Industrial Processes
4 Experiments and Discussion
4.1 Scenarios
4.2 Data Preprocessing and Autoencoder Network Topology
4.3 Results
5 Conclusions
References
Chapter 18: Data Warehousing and Exploratory Analysis for Market Monitoring
1 Data Warehouse Architecture
1.1 Inmon-Approach
1.1.1 Discussion of Inmon-Approach
1.2 Kimball-Approach
1.2.1 Discussion of Kimball-Approach
1.3 Alternative Approaches
2 Data Warehouse Use Case: Market Monitoring
3 Enrichment with Google Analytics Data
4 Unsupervised Machine Learning Approach to Cluster Users and Products
4.1 User Clustering Using K-Means
4.2 Product Clustering Based on Click Paths
5 Conclusions and Lessons Learned
References
Chapter 19: Mining Person-Centric Datasets for Insight, Prediction, and Public Health Planning
1 Introduction
2 Modeling and Simulation in Health
3 Data Characteristics
4 Analyses and Mining
4.1 Data Processing
4.2 Advanced Analysis
4.3 Abstract Representation: Modeling and Visualization
5 Lessons Learned
References
Chapter 20: Economic Measures of Forecast Accuracy for Demand Planning: A Case-Based Discussion
1 Introduction
1.1 Sales Forecasting and Food Demand Planning
1.2 Successful Demand Forecasting: The Past and the Future Inside
1.3 Traditional Measures of Forecast Accuracy
2 Cost Error Metrics
2.1 Which Metric Is Best: A Toy Example
2.2 Constructing Cost-Based Error Metrics
2.3 Sensitivity Analysis for linMCE
3 Evaluation
3.1 Calculating the Linear MPE: Toy Example Revisited
3.2 Real World Example
3.3 Stock-Keeping Models: Beyond Simple Cost Measures
4 Conclusions and Lessons Learned
References
Chapter 21: Large-Scale Data-Driven Financial Risk Assessment
1 Introduction
2 The ACTUS Approach
3 Stress Testing of a Fixed Income Test Portfolio
3.1 Mapping CSDB Data into the ACTUS Format
3.2 Running the ACTUS Algorithm with Suitable Interest Rate Scenarios
3.3 Financial Analysis by Aggregation of the Raw Results
3.3.1 Liquidity Analysis Without Credit Risk
3.3.2 Valuation
3.3.3 Liquidity Analysis with Credit Risk
4 ACTUS in a Big Data Context
4.1 System Architecture and Design Considerations
4.2 Experiments with SparkR
4.2.1 Experimental Setup
4.2.2 Results
4.3 Experiments with Spark Java
4.3.1 Experimental Setup
4.3.2 Results
5 Discussion and Conclusion
5.1 Lessons Learned for Data Scientists
Appendix: The European Central Bank´s Centralized Securities Database
References
Chapter 22: Governance and IT Architecture
1 Introduction
2 Utility and Benefits in Using Personal Health Data
2.1 Benefits for Citizens and Patients
2.2 Benefits for Health Professionals
2.3 Benefits for the Researchers
2.4 Benefits for the Pharmaceutical, Medtech, and IT Industries
2.5 Benefits for Public Health
3 Which Data?
4 Trust-Promoting Frameworks for Data Science Projects
5 IT Platform
6 Data Protection and Security
7 Example of a Data Science Project Running on MIDATA
8 Conclusion and Lessons Learned
References
Chapter 23: Image Analysis at Scale for Finding the Links Between Structure and Biology
1 Introduction
2 Background: Where Do Images Come From?
3 How Is an Image Represented?
4 Use Case: How to Look at Femur Fracture?
5 Study Design
5.1 Image Acquisition
5.2 Image Analysis
5.3 Genetic Cross-Studies
5.4 Messy Data
6 Statistical Methods
6.1 Quantitative Trait Loci Analysis (QTL)
6.2 Imputing
6.3 Bootstrapping
7 Results/Evaluation
8 Conclusions
9 Lessons Learned
References
Part III: Lessons Learned and Outlook
Chapter 24: Lessons Learned from Challenging Data Science Case Studies
1 Introduction
2 Taxonomy
3 Concise Reference of Individual Lessons Learned
4 Aggregated Insights
5 Conclusions
5.1 Deconstructing Myths by the Example of Recommender Services
5.2 Outlook to a Data-Driven Society

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Tags: Data Science, Lessons Learned, the Data Driven Business, Martin Braschler, Thilo Stadelmann, Kurt Stockinger