Data science strategy for dummies

Data science strategy for dummies

All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with...

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Detalles Bibliográficos
Otros Autores: Jagare, Ulrika, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley & Sons, Inc [2019]
Edición:1st edition
Colección:--For dummies.
Materias:
Big data.
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630585506719
Tabla de Contenidos:
  • Intro
  • Title Page
  • Copyright Page
  • Table of Contents
  • Foreword
  • Introduction
  • About This Book
  • Foolish Assumptions
  • How This Book Is Organized
  • Icons Used In This Book
  • Beyond The Book
  • Where To Go From Here
  • Part 1: Optimizing Your Data Science Investment
  • Chapter 1: Framing Data Science Strategy
  • Establishing the Data Science Narrative
  • Capture
  • Maintain
  • Process
  • Analyze
  • Communicate
  • Actuate
  • Sorting Out the Concept of a Data-driven Organization
  • Approaching data-driven
  • Being data obsessed
  • Sorting Out the Concept of Machine Learning
  • Defining and Scoping a Data Science Strategy
  • Objectives
  • Approach
  • Choices
  • Data
  • Legal
  • Ethics
  • Competence
  • Infrastructure
  • Governance and security
  • Commercial/business models
  • Measurements
  • Chapter 2: Considering the Inherent Complexity in Data Science
  • Diagnosing Complexity in Data Science
  • Recognizing Complexity as a Potential
  • Enrolling in Data Science Pitfalls 101
  • Believing that all data is needed
  • Thinking that investing in a data lake will solve all your problems
  • Focusing on AI when analytics is enough
  • Believing in the 1-tool approach
  • Investing only in certain areas
  • Leveraging the infrastructure for reporting rather than exploration
  • Underestimating the need for skilled data scientists
  • `Navigating the Complexity
  • Chapter 3: Dealing with Difficult Challenges
  • Getting Data from There to Here
  • Handling dependencies on data owned by others
  • Managing data transfer and computation across-country borders
  • Managing Data Consistency Across the Data Science Environment
  • Securing Explainability in AI
  • Dealing with the Difference between Machine Learning and Traditional Software Programming
  • Managing the Rapid AI Technology Evolution and Lack of Standardization.
  • Chapter 4: Managing Change in Data Science
  • Understanding Change Management in Data Science
  • Approaching Change in Data Science
  • Recognizing what to avoid when driving change in data science
  • Using Data Science Techniques to Drive Successful Change
  • Using digital engagement tools
  • Applying social media analytics to identify stakeholder sentiment
  • Capturing reference data in change projects
  • Using data to select people for change roles
  • Automating change metrics
  • Getting Started
  • Part 2: Making Strategic Choices for Your Data
  • Chapter 5: Understanding the Past, Present, and Future of Data
  • Sorting Out the Basics of Data
  • Explaining traditional data versus big data
  • Knowing the value of data
  • Exploring Current Trends in Data
  • Data monetization
  • Responsible AI
  • Cloud-based data architectures
  • Computation and intelligence in the edge
  • Digital twins
  • Blockchain
  • Conversational platforms
  • Elaborating on Some Future Scenarios
  • Standardization for data science productivity
  • From data monetization scenarios to a data economy
  • An explosion of human/machine hybrid systems
  • Quantum computing will solve the unsolvable problems
  • Chapter 6: Knowing Your Data
  • Selecting Your Data
  • Describing Data
  • Exploring Data
  • Assessing Data Quality
  • Improving Data Quality
  • Chapter 7: Considering the Ethical Aspects of Data Science
  • Explaining AI Ethics
  • Addressing trustworthy artificial intelligence
  • Introducing Ethics by Design
  • Chapter 8: Becoming Data-driven
  • Understanding Why Data-Driven Is a Must
  • Transitioning to a Data-Driven Model
  • Securing management buy-in and assigning a chief data officer (CDO)
  • Identifying the key business value aligned with the business maturity
  • Developing a Data Strategy
  • Caring for your data
  • Democratizing the data
  • Driving data standardization.
  • Structuring the data strategy
  • Establishing a Data-Driven Culture and Mindset
  • Chapter 9: Evolving from Data-driven to Machine-driven
  • Digitizing the Data
  • Applying a Data-driven Approach
  • Automating Workflows
  • Introducing AI/ML capabilities
  • Part 3: Building a Successful Data Science Organization
  • Chapter 10: Building Successful Data Science Teams
  • Starting with the Data Science Team Leader
  • Adopting different leadership approaches
  • Approaching data science leadership
  • Finding the right data science leader or manager
  • Defining the Prerequisites for a Successful Team
  • Developing a team structure
  • Establishing an infrastructure
  • Ensuring data availability
  • Insisting on interesting projects
  • Promoting continuous learning
  • Encouraging research studies
  • Building the Team
  • Developing smart hiring processes
  • Letting your teams evolve organically
  • Connecting the Team to the Business Purpose
  • Chapter 11: Approaching a Data Science Organizational Setup
  • Finding the Right Organizational Design
  • Designing the data science function
  • Evaluating the benefits of a center of excellence for data science
  • Identifying success factors for a data science center of excellence
  • Applying a Common Data Science Function
  • Selecting a location
  • Approaching ways of working
  • Managing expectations
  • Selecting an execution approach
  • Chapter 12: Positioning the Role of the Chief Data Officer (CDO)
  • Scoping the Role of the Chief Data Officer (CDO)
  • Explaining Why a Chief Data Officer Is Needed
  • Establishing the CDO Role
  • The Future of the CDO Role
  • Chapter 13: Acquiring Resources and Competencies
  • Identifying the Roles in a Data Science Team
  • Data scientist
  • Data engineer
  • Machine learning engineer
  • Data architect
  • Business analyst
  • Software engineer
  • Domain expert.
  • Seeing What Makes a Great Data Scientist
  • Structuring a Data Science Team
  • Hiring and evaluating the data science talent you need
  • Retaining Competence in Data Science
  • Understanding what makes a data scientist leave
  • Part 4: Investing in the Right Infrastructure
  • Chapter 14: Developing a Data Architecture
  • Defining What Makes Up a Data Architecture
  • Describing traditional architectural approaches
  • Elements of a data architecture
  • Exploring the Characteristics of a Modern Data Architecture
  • Explaining Data Architecture Layers
  • Listing the Essential Technologies for a Modern Data Architecture
  • NoSQL databases
  • Real-time streaming platforms
  • Docker and containers
  • Container repositories
  • Container orchestration
  • Microservices
  • Function as a service
  • Creating a Modern Data Architecture
  • Chapter 15: Focusing Data Governance on the Right Aspects
  • Sorting Out Data Governance
  • Data governance for defense or offense
  • Objectives for data governance
  • Explaining Why Data Governance is Needed
  • Data governance saves money
  • Bad data governance is dangerous
  • Good data governance provides clarity
  • Establishing Data Stewardship to Enforce Data Governance Rules
  • Implementing a Structured Approach to Data Governance
  • Chapter 16: Managing Models During Development and Production
  • Unfolding the Fundamentals of Model Management
  • Working with many models
  • Making the case for efficient model management
  • Implementing Model Management
  • Pinpointing implementation challenges
  • Managing model risk
  • Measuring the risk level
  • Identifying suitable control mechanisms
  • Chapter 17: Exploring the Importance of Open Source
  • Exploring the Role of Open Source
  • Understanding the importance of open source in smaller companies
  • Understanding the trend.
  • Describing the Context of Data Science Programming Languages
  • Unfolding Open Source Frameworks for AI/ML Models
  • TensorFlow
  • Theano
  • Torch
  • Caffe and Caffe2
  • The Microsoft Cognitive Toolkit (previously known as Microsoft CNTK)
  • Keras
  • Scikit-learn
  • Spark MLlib
  • Azure ML Studio
  • Amazon Machine Learning
  • Choosing Open Source or Not?
  • Chapter 18: Realizing the Infrastructure
  • Approaching Infrastructure Realization
  • Listing Key Infrastructure Considerations for AI and ML Support
  • Location
  • Capacity
  • Data center setup
  • End-to-end management
  • Network infrastructure
  • Security and ethics
  • Advisory and supporting services
  • Ecosystem fit
  • Automating Workflows in Your Data Infrastructure
  • Enabling an Efficient Workspace for Data Engineers and Data Scientists
  • Part 5: Data as a Business
  • Chapter 19: Investing in Data as a Business
  • Exploring How to Monetize Data
  • Approaching data monetization is about treating data as an asset
  • Data monetization in a data economy
  • Looking to the Future of the Data Economy
  • Chapter 20: Using Data for Insights or Commercial Opportunities
  • Focusing Your Data Science Investment
  • Determining the Drivers for Internal Business Insights
  • Recognizing data science categories for practical implementation
  • Applying data-science-driven internal business insights
  • Using Data for Commercial Opportunities
  • Defining a data product
  • Distinguishing between categories of data products
  • Balancing Strategic Objectives
  • Chapter 21: Engaging Differently with Your Customers
  • Understanding Your Customers
  • Step 1: Engage your customers
  • Step 2: Identify what drives your customers
  • Step 3: Apply analytics and machine learning to customer actions
  • Step 4: Predict and prepare for the next step
  • Step 5: Imagine your customer's future
  • Keeping Your Customers Happy.
  • Serving Customers More Efficiently.

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