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Data Science Strategy For Dummies

Data Science Strategy For Dummies
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Data Science Strategy For Dummies
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    Data Science Strategy For Dummies begins by explaining what exactly data science is and why it's important. While Using non-technical language, it covers mindsets, organizational players, processes and common roadblocks, all the while keeping a razor focus on business value and the nurturing of a top quality data science team. Ulrika Jägareis an M.Sc. Director at Ericsson AB with over 18 years of experience in telecommunications and ten years in analytics and machine intelligence. She's held various leadership positions, mostly in R&D and product management. Ulrika was also the main person behind the launch of Ericsson´s Machine Intelligence strategy and commercial approach. In addition to this book, she is the author of two technical books in the Data Science space by Wiley Foreword _x0..

    Book Details

    Pustak Details
    Sold By Wiley India
    Author Ulrika Jagare
    ISBN-13 9788126533367
    Edition 1st Edition
    Format Paperback
    Language English
    Pages 364 Pages
    Publication Year 2019

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    Book Description

    Data Science Strategy For Dummies begins by explaining what exactly data science is and why it's important. While Using non-technical language, it covers mindsets, organizational players, processes and common roadblocks, all the while keeping a razor focus on business value and the nurturing of a top quality data science team. Ulrika Jägareis an M.Sc. Director at Ericsson AB with over 18 years of experience in telecommunications and ten years in analytics and machine intelligence. She's held various leadership positions, mostly in R&D and product management. Ulrika was also the main person behind the launch of Ericsson´s Machine Intelligence strategy and commercial approach. In addition to this book, she is the author of two technical books in the Data Science space by Wiley Foreword _x000D_ Introduction _x000D_ About This Book _x000D_ Foolish Assumptions _x000D_ How This Book is Organized _x000D_ Icons Used In This Book_x000D_ Beyond The Book_x000D_ Where To Go From Here_x000D_ _x000D_ Part 1: Optimizing Your Data Science Investment _x000D_ Chapter 1: Framing Data Science Strategy_x000D_ ·Establishing the Data Science Narrative _x000D_ ·Capture _x000D_ ·Maintain _x000D_ ·Process _x000D_ ·Analyze _x000D_ ·Communicate _x000D_ ·Actuate _x000D_ ·Sorting Out the Concept of a Data-driven Organization_x000D_ ·Approaching data-driven_x000D_ ·Being data obsessed_x000D_ ·Sorting Out the Concept of Machine Learning _x000D_ ·Defining and Scoping a Data Science Strategy _x000D_ ·Objectives _x000D_ ·Approach _x000D_ ·Choices _x000D_ ·Data _x000D_ ·Legal _x000D_ ·Ethics _x000D_ ·Competence _x000D_ ·Infrastructure _x000D_ ·Governance and security_x000D_ ·Commercial/business models_x000D_ ·Measurements_x000D_ _x000D_ Chapter 2: Considering the Inherent Complexity in Data Science _x000D_ ·Diagnosing Complexity in Data Science _x000D_ ·Recognizing Complexity as a Potential _x000D_ ·Enrolling in Data Science Pitfalls 101 _x000D_ ·Believing that all data is needed _x000D_ ·Thinking that investing in a data lake will solve all your problems_x000D_ ·Focusing on AI when analytics is enough_x000D_ ·Believing in the 1-tool approach _x000D_ ·Investing only in certain areas _x000D_ ·Leveraging the infrastructure for reporting rather than exploration_x000D_ ·Underestimating the need for skilled data scientists _x000D_ ·`Navigating the Complexity _x000D_ _x000D_ Chapter 3: Dealing with Difficult Challenges _x000D_ ·Getting Data from There to Here _x000D_ ·Handling dependencies on data owned by others_x000D_ ·Managing data transfer and computation across-country borders_x000D_ ·Managing Data Consistency Across the Data Science Environment _x000D_ ·Securing Explain ability in AI _x000D_ ·Dealing with the Difference between Machine Learning and Traditional Software Programming_x000D_ ·Managing the Rapid AI Technology Evolution and Lack of Standardization_x000D_ _x000D_ Chapter 4: Managing Change in Data Science _x000D_ ·Understanding Change Management in Data Science_x000D_ ·Approaching Change in Data Science _x000D_ ·Recognizing what to avoid when driving change in data science _x000D_ ·Using Data Science Techniques to Drive Successful Change_x000D_ ·Using digital engagement tools_x000D_ ·Applying social media analytics to identify stakeholder sentiment_x000D_ ·Capturing reference data in change projects_x000D_ ·Using data to select people for change roles _x000D_ ·Automating change metrics_x000D_ ·Getting Started _x000D_ _x000D_ Part 2: Making Strategic Choices for Your Data _x000D_ Chapter 5: Understanding the Past, Present, and Future of Data _x000D_ ·Sorting Out the Basics of Data _x000D_ ·Explaining traditional data versus big data _x000D_ ·Knowing the value of data _x000D_ ·Exploring Current Trends in Data _x000D_ ·Data monetization _x000D_ ·Responsible AI _x000D_ ·Cloud-based data architectures _x000D_ ·Computation and intelligence in the edge _x000D_ ·Digital twins _x000D_ ·Blockchain _x000D_ ·Conversational platforms _x000D_ ·Elaborating on Some Future Scenarios _x000D_ ·Standardization for data science productivity _x000D_ ·From data monetization scenarios to a data economy _x000D_ ·An explosion of human/machine hybrid systems _x000D_ ·Quantum computing will solve the unsolvable problems _x000D_ _x000D_ Chapter 6: Knowing Your Data _x000D_ ·Selecting Your Data _x000D_ ·Describing Data _x000D_ ·Exploring Data _x000D_ ·Assessing Data Quality _x000D_ ·Improving Data Quality _x000D_ _x000D_ Chapter 7: Considering the Ethical Aspects of Data Science _x000D_ ·Explaining AI Ethics _x000D_ ·Addressing trustworthy artificial intelligence _x000D_ ·Introducing Ethics by Design _x000D_ _x000D_ Chapter 8: Becoming Data-driven _x000D_ ·Understanding Why Data-Driven is a Must _x000D_ ·Transitioning to a Data-Driven Model _x000D_ ·Securing management buy-in and assigning a chief data officer (CDO) _x000D_ ·Identifying the key business value aligned with the business maturity _x000D_ ·Developing a Data Strategy _x000D_ ·Caring for your data _x000D_ ·Democratizing the data _x000D_ ·Driving data standardization _x000D_ ·Structuring the data strategy _x000D_ ·Establishing a Data-Driven Culture and Mindset _x000D_ _x000D_ Chapter 9: Evolving from Data-driven to Machine-driven _x000D_ ·Digitizing the Data _x000D_ ·Applying a Data-driven Approach _x000D_ ·Automating Workflows _x000D_ ·Introducing AI/ML capabilities _x000D_ _x000D_ Part 3: Building a Successful Data Science Organization _x000D_ Chapter 10: Building Successful Data Science Teams _x000D_ ·Starting with the Data Science Team Leader _x000D_ ·Adopting different leadership approaches _x000D_ ·Approaching data science leadership _x000D_ ·Finding the right data science leader or manager _x000D_ ·Defining the Prerequisites for a Successful Team _x000D_ ·Developing a team structure _x000D_ ·Establishing an infrastructure _x000D_ ·Ensuring data availability _x000D_ ·Insisting on interesting projects _x000D_ ·Promoting continuous learning _x000D_ ·Encouraging research studies _x000D_ ·Building the Team _x000D_ ·Developing smart hiring processes _x000D_ ·Letting your teams evolve organically _x000D_ ·Connecting the Team to the Business Purpose_x000D_ _x000D_ Chapter 11: Approaching a Data Science Organizational Setup _x000D_ ·Finding the Right Organizational Design _x000D_ ·Designing the data science function _x000D_ ·Evaluating the benefits of a center of excellence for data science _x000D_ ·Identifying success factors for a data science center of excellence _x000D_ ·Applying a Common Data Science Function_x000D_ ·Selecting a location _x000D_ ·Approaching ways of working _x000D_ ·Managing expectations _x000D_ ·Selecting an execution approach _x000D_ _x000D_ Chapter 12: Positioning the Role of the Chief Data Officer (CDO) _x000D_ ·Scoping the Role of the Chief Data Officer (CDO) _x000D_ ·Explaining Why a Chief Data Officer is Needed _x000D_ ·Establishing the CDO Role _x000D_ ·The Future of the CDO Role _x000D_ _x000D_ Chapter 13: Acquiring Resources and Competencies _x000D_ ·Identifying the Roles in a Data Science Team _x000D_ ·Data scientist _x000D_ ·Data engineer _x000D_ ·Machine learning engineer _x000D_ ·Data architect _x000D_ ·Business analyst _x000D_ ·Software engineer _x000D_ ·Domain expert _x000D_ ·Seeing What Makes a Great Data Scientist _x000D_ ·Structuring a Data Science Team _x000D_ ·Hiring and evaluating the data science talent you need _x000D_ ·Retaining Competence in Data Science _x000D_ ·Understanding what makes a data scientist leave _x000D_ _x000D_ Part 4: Investing in the Right Infrastructure _x000D_ Chapter 14: Developing a Data Architecture _x000D_ ·Defining What Makes Up a Data Architecture _x000D_ ·Describing traditional architectural approaches _x000D_ ·Elements of a data architecture _x000D_ ·Exploring the Characteristics of a Modern Data Architecture _x000D_ ·Explaining Data Architecture Layers _x000D_ ·Listing the Essential Technologies for a Modern Data Architecture _x000D_ ·NoSQL databases _x000D_ ·Real-time streaming platforms _x000D_ ·Docker and containers _x000D_ ·Container repositories _x000D_ ·Container orchestration _x000D_ ·Microservices _x000D_ ·Function as a service_x000D_ ·Creating a Modern Data Architecture_x000D_ _x000D_ Chapter 15: Focusing Data Governance on the Right Aspects _x000D_ ·Sorting Out Data Governance _x000D_ ·Data governance for defense or offense _x000D_ ·Objectives for data governance _x000D_ ·Explaining Why Data Governance is Needed _x000D_ ·Data governance saves money _x000D_ ·Bad data governance is dangerous _x000D_ ·Good data governance provides clarity _x000D_ ·Establishing Data Stewardship to Enforce Data Governance Rules _x000D_ ·Implementing a Structured Approach to Data Governance _x000D_ _x000D_ Chapter 16: Managing Models During Development and Production _x000D_ ·Unfolding the Fundamentals of Model Management _x000D_ ·Working with many models _x000D_ ·Making the case for efficient model management _x000D_ ·Implementing Model Management _x000D_ ·Pinpointing implementation challenges _x000D_ ·Managing model risk _x000D_ ·Measuring the risk level _x000D_ ·Identifying suitable control mechanisms _x000D_ _x000D_ Chapter 17: Exploring the Importance of Open Source _x000D_ ·Exploring the Role of Open Source _x000D_ ·Understanding the importance of open source in smaller companies _x000D_ ·Understanding the trend _x000D_ ·Describing the Context of Data Science Programming Languages _x000D_ ·Unfolding Open Source Frameworks for AI/ML Models _x000D_ ·TensorFlow _x000D_ ·Theano _x000D_ ·Torch _x000D_ ·Caffe and Caffe2 _x000D_ ·The Microsoft Cognitive Toolkit (previously known as Microsoft CNTK) _x000D_ ·Keras _x000D_ ·Scikit-learn _x000D_ ·Spark MLlib _x000D_ ·Azure ML Studio _x000D_ ·Amazon Machine Learning _x000D_ ·Choosing Open Source or Not? _x000D_ _x000D_ Chapter 18: Realizing the Infrastructure _x000D_ ·Approaching Infrastructure Realization _x000D_ ·Listing Key Infrastructure Considerations for AI and ML Support _x000D_ ·Location _x000D_ ·Capacity _x000D_ ·Data center setup _x000D_ ·End-to-end management _x000D_ ·Network infrastructure _x000D_ ·Security and ethics _x000D_ ·Advisory and supporting services _x000D_ ·Ecosystem fit _x000D_ ·Automating Workflows in Your Data Infrastructure _x000D_ ·Enabling an Efficient Workspace for Data Engineers and Data Scientists _x000D_ _x000D_ Part 5: Data as a Business _x000D_ Chapter 19: Investing in Data as a Business _x000D_ ·Exploring How to Monetize Data _x000D_ ·Approaching data monetization is about treating data as an asset _x000D_ ·Data monetization in a data economy _x000D_ ·Looking to the Future of the Data Economy _x000D_ _x000D_ Chapter 20: Using Data for Insights or Commercial Opportunities _x000D_ ·Focusing Your Data Science Investment _x000D_ ·Determining the Drivers for Internal Business Insights _x000D_ ·Recognizing data science categories for practical implementation _x000D_ ·Applying data-science-driven internal business insights _x000D_ ·Using Data for Commercial Opportunities _x000D_ ·Defining a data product _x000D_ ·Distinguishing between categories of data products _x000D_ ·Balancing Strategic Objectives _x000D_ _x000D_ Chapter 21: Engaging Differently with Your Customers _x000D_ ·Understanding Your Customers _x000D_ ·Step 1: Engage your customers _x000D_ ·Step 2: Identify what drives your customers _x000D_ ·Step 3: Apply analytics and machine learning to customer actions _x000D_ ·Step 4: Predict and prepare for the next step _x000D_ ·Step 5: Imagine your customer's future _x000D_ ·Keeping Your Customers Happy _x000D_ ·Serving Customers More Efficiently _x000D_ ·Predicting demand _x000D_ ·Automating tasks _x000D_ ·Making company applications predictive _x000D_ _x000D_ Chapter 22: Introducing Data-driven Business Models _x000D_ ·Defining Business Models _x000D_ ·Exploring Data-driven Business Models _x000D_ ·Creating data-centric businesses _x000D_ ·Investigating different types of data-driven business models _x000D_ ·Using a Framework for Data-driven Business Models _x000D_ ·Creating a data-driven business model using a framework _x000D_ ·Key resources _x000D_ ·Key activities _x000D_ ·Offering/value proposition _x000D_ ·Customer segment _x000D_ ·Revenue model _x000D_ ·Cost structure _x000D_ ·Putting it all together _x000D_ _x000D_ Chapter 23: Handling New Delivery Models _x000D_ ·Defining Delivery Models for Data Products and Services _x000D_ ·Understanding and Adapting to New Delivery Models _x000D_ ·Introducing New Ways to Deliver Data Products _x000D_ ·Self-service analytics environments as a delivery model _x000D_ ·Applications, websites, and product/service interfaces as delivery models _x000D_ ·Existing products and services _x000D_ ·Downloadable files _x000D_ ·APIs _x000D_ ·Cloud services _x000D_ ·Online market places _x000D_ ·Downloadable licenses _x000D_ ·Online services _x000D_ ·Onsite services _x000D_ _x000D_ Part 6: The Part of Tens _x000D_ Chapter 24: Ten Reasons to Develop a Data Science Strategy _x000D_ ·Expanding Your View on Data Science _x000D_ ·Aligning the Company View _x000D_ ·Creating a Solid Base for Execution _x000D_ ·Realizing Priorities Early _x000D_ ·Putting the Objective into Perspective _x000D_ ·Creating an Excellent Base for Communication _x000D_ ·Understanding Why Choices Matter _x000D_ ·Identifying the Risks Early _x000D_ ·Thoroughly Considering Your Data Need _x000D_ ·Understanding the Change Impact _x000D_ _x000D_ Chapter 25: Ten Mistakes to Avoid When Investing in Data Science _x000D_ ·Don't Tolerate Top Management's Ignorance of Data Science _x000D_ ·Don't Believe That AI is Magic _x000D_ ·Don't Approach Data Science as a Race to the Death between Man and Machine _x000D_ ·Don't Underestimate the Potential of AI _x000D_ ·Don't Underestimate the Needed Data Science Skill Set _x000D_ ·Don't Think That a Dashboard is the End Objective _x000D_ ·Don't Forget about the Ethical Aspects of AI _x000D_ ·Don't Forget to Consider the Legal Rights to the Data _x000D_ ·Don't Ignore the Scale of Change Needed _x000D_ ·Don't Forget the Measurements Needed to Prove Value _x000D_ _x000D_ Index

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