Building an Effective Data Science Practice - A Framework to Bootstrap and Manage a Successful Data Science Practice
Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analyst...
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Berkeley, CA
Apress, an imprint of Springer Nature
2022
Apress Apress L. P |
| Edition: | 1 |
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| ISBN: | 9781484274187, 1484274180, 9781484274194, 1484274199 |
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| Abstract | Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science - classes of data science problems, data science techniques and their applications - and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. This book provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. |
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| AbstractList | Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation.You’ll start by delving into the fundamentals of data science – classes of data science problems, data science techniques and their applications – and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects.Building an Effective Data Science Practice provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. Reinforced with real examples, this book allows you to confidently determine the strategic answers to effectively align your business goals with the operations of the data science practice.What You’ll Learn Transform business objectives into concrete problems that can be solved using data scienceEvaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a projectBuild and operate an effective interdisciplinary data science team within an organizationEvaluating the progress of the team towards the business RoIUnderstand the important regulatory aspects that are applicable to a data science practice Who This Book Is ForTechnology leaders, data scientists, and project managers Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science – classes of data science problems, data science techniques and their applications – and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. Reinforced with real examples, this book allows you to confidently determine the strategic answers to effectively align your business goals with the operations of the data science practice. What You'll Learn * Transform business objectives into concrete problems that can be solved using data science * Evaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a project * Build and operate an effective interdisciplinary data science team within an organization * Evaluating the progress of the team towards the business RoI * Understand the important regulatory aspects that are applicable to a data science practice Who This Book Is For Technology leaders, data scientists, and project managers Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science - classes of data science problems, data science techniques and their applications - and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. This book provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. -- Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation.You'll start by delving into the fundamentals of data science - classes of data science problems, data science techniques and their applications - and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects.Building an Effective Data Science Practice provides a common base of reference knowledge and solutions, and addresses the kinds of challenges that arise to ensure your data science team is both productive and aligned with the business goals from the very start. Reinforced with real examples, this book allows you to confidently determine the strategic answers to effectively align your business goals with the operations of the data science practice.What You'll Learn Transform business objectives into concrete problems that can be solved using data scienceEvaluate how problems and the specifics of a business drive the techniques and model evaluation guidelines used in a projectBuild and operate an effective interdisciplinary data science team within an organizationEvaluating the progress of the team towards the business RoIUnderstand the important regulatory aspects that are applicable to a data science practice Who This Book Is ForTechnology leaders, data scientists, and project managers |
| Author | Krishnamurthy, Srinath Raina, Vineet |
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| Copyright | 2022 Vineet Raina and Srinath Krishnamurthy 2022 |
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| Snippet | Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills... This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on... |
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| SubjectTerms | Artificial Intelligence Big data Business Computer Science Computer Science, general COMPUTERS Data mining Data Structures and Information Theory Engineering Management & Leadership General References Information technology Leadership, Coaching & Mentoring Machine learning Professional and Applied Computing Software Engineering |
| SubjectTermsDisplay | Databases. Electronic books. Information technology. |
| TableOfContents | Title Page
Introduction
Table of Contents
Part I. Fundamentals
1. Introduction: The Data Science Process
2. Data Science and Your Business
3. Monks vs. Cowboys: Data Science Cultures
Part II. Classes of Problems
4. Classification
5. Regression
6. Natural Language Processing
7. Clustering
8. Anomaly Detection
9. Recommendations
10. Computer Vision
11. Sequential Decision-Making
Part III. Techniques and Technologies
12. Techniques and Technologies: An Overview
13. Data Capture
14. Data Preparation
15. Data Visualization
16. Machine Learning
17. Inference
18. Other Tools and Services
19. Reference Architecture
20. Monks vs. Cowboys: Praxis
Part IV. Building Teams and Executing Projects
21. The Skills Framework
22. Building and Structuring the Team
23. Data Science Projects
Index References -- Part III: Techniques and Technologies -- Chapter 12: Techniques and Technologies: An Overview -- Chapter 13: Data Capture -- Data Sources (1) -- Ingestion (2) -- Data Storage -- Data Lake (3) -- Data Warehouse (4) -- Shared File Systems (5) -- Read Data (6) -- Programmatic Access -- SQL Query Engine -- Open Source vs. Paid -- Data Engineering -- Conclusion -- Chapter 14: Data Preparation -- Handling Missing Values -- Feature Scaling -- Text Preprocessing -- Stemming -- TF-IDF -- Converting Categorical Variables into Numeric Variables -- Transforming Images -- Libraries and Tools -- Libraries -- Tools -- Data Engineering -- Conclusion -- Chapter 15: Data Visualization -- Graphs/Charts/Plots -- Legends -- Layouts -- Options -- Interactive Visualizations -- Deriving Insights from Visualizations -- Histogram -- Kernel Density Estimate Plot -- Libraries and Tools -- Libraries -- Tools -- Data Engineering -- Conclusion -- Chapter 16: Machine Learning -- Categories of Machine Learning Algorithms -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Popular Machine Learning Algorithms -- Linear Regression -- Logistic Regression -- Support Vector Machine -- Decision Tree -- Random Forest -- Gradient Boosted Trees -- Artificial Neural Network -- Convolutional Neural Network -- Evaluating and Tuning Models -- Evaluating Models -- Tuning models -- Cross-Validation -- Libraries and Tools -- Data Engineering -- Conclusion -- Further Reading -- References -- Chapter 17: Inference -- Model Release Process (1) -- Model Registry -- Model Converter -- Interexchange Format -- Target System -- Model Packaging -- Production -- Inference Server (2) -- Inference/Prediction Service -- Model Monitoring -- Mobile and Web Applications (3) -- ML Ops -- Open Source vs. Paid -- Data Engineering -- Conclusion Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Part I: Fundamentals -- Chapter 1: Introduction: The Data Science Process -- What We Mean by Data Science -- The Data Science Process -- Machine Learning -- Data Capture (from the World) -- Data Preparation -- Data Visualization -- Inference -- Data Engineering -- Terminology Chaos: AI, ML, Data Science, Deep Learning, Etc. -- Conclusion -- Further Reading -- References -- Chapter 2: Data Science and Your Business -- How Data Science Fits into a Business -- Operational Optimizations -- Product Enhancements -- Strategic Insights -- Is Your Business Ready for Data Science? -- A Cautionary Tale -- In the Beginning Was the Data -- And the Data Was with… Whom Exactly? -- The Model Said "Here Am I, Send Me" -- Conclusion -- Further Reading -- References -- Chapter 3: Monks vs. Cowboys: Data Science Cultures -- The Two Cultures of Data Science -- Hybrid Cultures -- Cultural Differences -- Data Science Culture and Your Business -- The Cultural Spectrum of Data Scientists -- Theory and Experimentation in Data Science -- Data Engineering -- Conclusion -- Summary of Part 1 -- Part II: Classes of Problems -- Chapter 4: Classification -- Data Capture -- Data Preparation -- Data Visualization -- Machine Learning -- Inference -- Data Engineering -- Conclusion -- Chapter 5: Regression -- Data Capture -- Data Preparation -- Data Visualization -- Machine Learning -- Inference -- Conclusion -- Chapter 6: Natural Language Processing -- Data Capture -- Data Preparation -- Machine Learning -- Inference -- Conclusion -- Chapter 7: Clustering -- Data Capture -- Data Preparation -- Handling Missing Values -- Normalization -- Data Visualization -- Machine Learning -- Similarity of Observations -- Data Visualization Iteration -- Inference Interpreting the Dendrogram -- Actionable Insights for Marketing -- Conclusion -- Further Reading -- Reference -- Chapter 8: Anomaly Detection -- Anomaly Detection Using Unlabeled Data -- Novelty Detection Using Pure Data -- Data Science Process for Anomaly Detection -- The World and Data Capture -- Data Preparation -- Data Visualization -- Box Plots -- Conditional Box Plots -- Scatter Plots -- Machine Learning -- Inference -- Anatomy of an Anomaly -- Complex Anomalies -- Collective Anomalies -- Contextual Anomalies -- Time Series -- Conclusion -- Further Reading -- References -- Chapter 9: Recommendations -- Data Capture -- Items and Interactions -- Quantifying an Interaction -- Example Data -- Data Preparation -- Normalization -- Handling Missing Values -- Data Visualization -- Machine Learning -- Clustering-Based Approach -- Inference -- End-to-End Automation -- Conclusion -- Further Reading -- References -- Chapter 10: Computer Vision -- Processing Images -- Image Classification/Regression -- Object Detection -- Datasets, Competitions, and Architectures -- Processing Videos -- Video Classification -- Object Tracking -- Data Science Process for Computer Vision -- The World and Data Capture -- Data Preparation -- Data Visualization -- Machine Learning -- Model Performance Evaluation -- Inference -- Data Engineering -- Conclusion -- Further Reading -- References -- Chapter 11: Sequential Decision-Making -- The RL Setting -- Basic Knowledge and Rules -- Training Nestor -- Episode -- Training Phases -- Past Cases -- Ongoing New Cases, with Imitation -- Supervised Exploration -- Supervised Exploitation -- Data in the RL Setting -- Data of Experts' Decisions -- Simulated Data -- Challenges in RL -- Availability of Data -- Information in Observations -- Exploration vs. Exploitation -- Data Science Process for RL -- Conclusion -- Further Reading Chapter 18: Other Tools and Services -- Development Environment -- Experiment Registry -- Compute Infrastructure -- AutoML -- Purpose of AutoML -- AutoML Cautions -- Tools and Services -- Multimodal Predictive Analytics and Machine Learning -- Data Science Apps/Workflows -- Off-the-Shelf AI Services and Libraries -- When to Use -- Open Source vs. Paid -- Conclusion -- Chapter 19: Reference Architecture -- Experimentation -- Dev Environment (1) -- Data Sources (2) -- Ingestion (3) -- Core Infra (4) -- Analytics (5) -- Data Science Apps/Workflows (6) -- AutoML (7) -- From Experimentation to Production -- AI Services -- Conclusion -- Chapter 20: Monks vs. Cowboys: Praxis -- Goals of Modeling -- Estimating Truth: Simplicity of Representation -- Estimating Truth: Attribution -- Prediction: Interpretability -- Prediction: Accuracy -- Grading ML Techniques -- Cultural Differences -- Conclusion -- Summary of Part 3 -- References -- Part IV: Building Teams and Executing Projects -- Chapter 21: The Skills Framework -- The Three Dimensions of Skills -- Data Analysis Skills -- Software Engineering Skills -- Domain Expertise -- The Roles in a Data Science Team -- Citizen Data Scientist -- Data Analyst -- Data Science Technician -- ML Ops -- Data Engineer -- Data Architect -- ML Engineer -- Data Scientist -- Chief Data Scientist -- Deviations in Skills -- Conclusion -- Chapter 22: Building and Structuring the Team -- Typical Team Structures -- Small Incubation Team -- Mature Operational team -- Team Evolution -- The Key Hire: Chief Data Scientist -- Evaluating the Culture -- Hiring vs. Getting a Consultant -- Data Engineering: Requirements and Staffing -- Notes on Upskilling -- Conclusion -- Chapter 23: Data Science Projects -- Types of Data Science Projects -- Knowledge Discovery from Data/Data Mining -- Data Science Infusion in Processes Data Science Infusion in Products -- Data Science-Based Product -- Typical Traits of Data Science Projects -- KPIs -- Model Performance -- Experimentation Cycle Time -- Effort-Cost Trade-Offs -- Data Quality -- Importance of Data Quality -- Issues Arising from Poor-Quality Data -- Severity of Impact -- Dimensions of Data Quality -- Measuring Data Quality -- Ensuring Data Quality -- Resistance to Data Quality Efforts -- Data Protection and Privacy -- Encryption -- Access Controls -- Identifiable/Protected/Sensitive Information -- Federated Learning -- Legal and Regulatory Aspects -- When Are These Relevant? -- Nondiscrimination -- Explainability and Accountability -- Explainable AI: What Is an "Explanation"? -- Cognitive Bias -- Cognitive Bias and Data Science Projects -- Conclusion and Further Reading -- References -- Index |
| Title | Building an Effective Data Science Practice - A Framework to Bootstrap and Manage a Successful Data Science Practice |
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