The Art of Data Science: A Practitioner's Guide
Author Doug Gray shares the most important lessons he has learned over the course of his 30+ year career in analytics and technology. Readers will witness the Analytical Sciences profession through the eye of a practitioner who has delivered solutions that generate hundreds of millions of dollars an...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Milton
CRC Press LLC
2025
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Zusammenfassung: | Author Doug Gray shares the most important lessons he has learned over the course of his 30+ year career in analytics and technology. Readers will witness the Analytical Sciences profession through the eye of a practitioner who has delivered solutions that generate hundreds of millions of dollars annually in business value |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (219 Seiten) |
| ISBN: | 9781040310380 |
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| 505 | 8 | |a Cover -- Endorsements Page -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Acknowledgments -- About the Author -- Introduction -- Motivation -- Creating a Course and a Legacy -- Organization -- 1. Career Summary: On Becoming a Practitioner and Leader -- The "Key" to My Future -- Flying High (and Overseas) at AADT -- Data Down Under -- Movin' On Up -- Delivering Value -- Ride the Waves -- 2. The Dual Challenge of the Analytical Sciences Practitioner -- Introduction -- 3. Digital Transformation -- Introduction -- Observations and Foundational Experiences -- The Internet, Data, and Analytics in Digital Transformation -- The Why and How of Digital Transformation -- Digital Transformation: A Competitive Imperative -- 4. Advanced Analytics is Economically Transformational -- Introduction -- 5. Airworthy: American Airlines Heavy Maintenance Planning and Scheduling -- Introduction -- 6. Consulting Concepts Learned from Airworthy -- Introduction -- 7. A Modern Day Project Applying the Same Principles: Advanced Analytics Commodity Case -- Introduction -- 8. Right Tool, Right Place, Right Time (with Nader Kabbani) -- Introduction -- 9. Under Fire: Lessons from the Front - Revenue Management for Manufacturing -- Introduction -- 10. Analytics Nontechnical Skills -- Introduction -- What They Do Not Teach You in Your MS Data Science/ Business Analytics Program -- Three Common Reasons DS Projects Don't Get Implemented -- MBAs for Data Scientists (Expensive, but Worth It, in My Experience) -- Notes -- 11. Top 10 Analytics Leadership Skills (with Tom Davenport) -- Introduction -- Recruitment, Retention, People Development -- Generating Demand (Securing Projects by Domain Area) -- Relationship Building -- Understand the Business Domain (and Problem in Question) -- Change Management -- Project Management | |
| 505 | 8 | |a Communication Skills -- Planning, Budgeting, Administration, and P& -- L Management -- Practitioner Experience and Expertise -- Information Technology (IT) Experience and Expertise -- 12. Top 10 Reasons Analytical Sciences Projects Fail -- Introduction -- Why Data Science Projects Fail -- References -- Part 1: What's the Problem (That You're Trying to Solve)? -- Reference -- Part 2: Data, Data Everywhere ... But Not in One Location to Analyze -- Part 3: Misapplying the Model -- Part 4: Solving a Problem that is Not a Business Priority -- Part 5: Effective Communication -- Stories as Communication -- Part 6: Change Management -- Principles of Change Management -- Interactive Optimization -- Keys to Success -- Justification for Change -- Notes and References -- Part 7: Unrealistic Expectations -- Setting Business Value Benefits -- Part 8: Project Management -- The "Perfect" Project Management -- The Usual Suspects in PM -- Part 9: Excessive Focus on the Model, Technique, or Technology -- Part 10: Getting from Sandbox Model to Production System -- Failure Begets Success -- Conclusion -- Project Challenges -- Managing Change -- 13. Surrounded by Success -- Introduction -- 14. O.R. in 2048 -- Introduction -- Computing -- Models and Algorithms -- Software Tools -- People -- Conclusion -- References and Bibliography -- Index | |
| 520 | |a Author Doug Gray shares the most important lessons he has learned over the course of his 30+ year career in analytics and technology. Readers will witness the Analytical Sciences profession through the eye of a practitioner who has delivered solutions that generate hundreds of millions of dollars annually in business value | ||
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| 650 | 4 | |a Quantitative research | |
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Datensatz im Suchindex
| _version_ | 1824508694086287360 |
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| author | Gray, Douglas A. |
| author_facet | Gray, Douglas A. |
| author_role | aut |
| author_sort | Gray, Douglas A. |
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| building | Verbundindex |
| bvnumber | BV050174155 |
| contents | Cover -- Endorsements Page -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Acknowledgments -- About the Author -- Introduction -- Motivation -- Creating a Course and a Legacy -- Organization -- 1. Career Summary: On Becoming a Practitioner and Leader -- The "Key" to My Future -- Flying High (and Overseas) at AADT -- Data Down Under -- Movin' On Up -- Delivering Value -- Ride the Waves -- 2. The Dual Challenge of the Analytical Sciences Practitioner -- Introduction -- 3. Digital Transformation -- Introduction -- Observations and Foundational Experiences -- The Internet, Data, and Analytics in Digital Transformation -- The Why and How of Digital Transformation -- Digital Transformation: A Competitive Imperative -- 4. Advanced Analytics is Economically Transformational -- Introduction -- 5. Airworthy: American Airlines Heavy Maintenance Planning and Scheduling -- Introduction -- 6. Consulting Concepts Learned from Airworthy -- Introduction -- 7. A Modern Day Project Applying the Same Principles: Advanced Analytics Commodity Case -- Introduction -- 8. Right Tool, Right Place, Right Time (with Nader Kabbani) -- Introduction -- 9. Under Fire: Lessons from the Front - Revenue Management for Manufacturing -- Introduction -- 10. Analytics Nontechnical Skills -- Introduction -- What They Do Not Teach You in Your MS Data Science/ Business Analytics Program -- Three Common Reasons DS Projects Don't Get Implemented -- MBAs for Data Scientists (Expensive, but Worth It, in My Experience) -- Notes -- 11. Top 10 Analytics Leadership Skills (with Tom Davenport) -- Introduction -- Recruitment, Retention, People Development -- Generating Demand (Securing Projects by Domain Area) -- Relationship Building -- Understand the Business Domain (and Problem in Question) -- Change Management -- Project Management Communication Skills -- Planning, Budgeting, Administration, and P& -- L Management -- Practitioner Experience and Expertise -- Information Technology (IT) Experience and Expertise -- 12. Top 10 Reasons Analytical Sciences Projects Fail -- Introduction -- Why Data Science Projects Fail -- References -- Part 1: What's the Problem (That You're Trying to Solve)? -- Reference -- Part 2: Data, Data Everywhere ... But Not in One Location to Analyze -- Part 3: Misapplying the Model -- Part 4: Solving a Problem that is Not a Business Priority -- Part 5: Effective Communication -- Stories as Communication -- Part 6: Change Management -- Principles of Change Management -- Interactive Optimization -- Keys to Success -- Justification for Change -- Notes and References -- Part 7: Unrealistic Expectations -- Setting Business Value Benefits -- Part 8: Project Management -- The "Perfect" Project Management -- The Usual Suspects in PM -- Part 9: Excessive Focus on the Model, Technique, or Technology -- Part 10: Getting from Sandbox Model to Production System -- Failure Begets Success -- Conclusion -- Project Challenges -- Managing Change -- 13. Surrounded by Success -- Introduction -- 14. O.R. in 2048 -- Introduction -- Computing -- Models and Algorithms -- Software Tools -- People -- Conclusion -- References and Bibliography -- Index |
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| dewey-full | 658.4012 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4012 |
| dewey-search | 658.4012 |
| dewey-sort | 3658.4012 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV050174155 |
| illustrated | Not Illustrated |
| indexdate | 2025-02-19T17:44:41Z |
| institution | BVB |
| isbn | 9781040310380 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035510035 |
| oclc_num | 1492396309 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (219 Seiten) |
| psigel | ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2025 |
| publishDateSearch | 2025 |
| publishDateSort | 2025 |
| publisher | CRC Press LLC |
| record_format | marc |
| spelling | Gray, Douglas A. Verfasser aut The Art of Data Science A Practitioner's Guide 1st ed Milton CRC Press LLC 2025 ©2025 1 Online-Ressource (219 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Endorsements Page -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Acknowledgments -- About the Author -- Introduction -- Motivation -- Creating a Course and a Legacy -- Organization -- 1. Career Summary: On Becoming a Practitioner and Leader -- The "Key" to My Future -- Flying High (and Overseas) at AADT -- Data Down Under -- Movin' On Up -- Delivering Value -- Ride the Waves -- 2. The Dual Challenge of the Analytical Sciences Practitioner -- Introduction -- 3. Digital Transformation -- Introduction -- Observations and Foundational Experiences -- The Internet, Data, and Analytics in Digital Transformation -- The Why and How of Digital Transformation -- Digital Transformation: A Competitive Imperative -- 4. Advanced Analytics is Economically Transformational -- Introduction -- 5. Airworthy: American Airlines Heavy Maintenance Planning and Scheduling -- Introduction -- 6. Consulting Concepts Learned from Airworthy -- Introduction -- 7. A Modern Day Project Applying the Same Principles: Advanced Analytics Commodity Case -- Introduction -- 8. Right Tool, Right Place, Right Time (with Nader Kabbani) -- Introduction -- 9. Under Fire: Lessons from the Front - Revenue Management for Manufacturing -- Introduction -- 10. Analytics Nontechnical Skills -- Introduction -- What They Do Not Teach You in Your MS Data Science/ Business Analytics Program -- Three Common Reasons DS Projects Don't Get Implemented -- MBAs for Data Scientists (Expensive, but Worth It, in My Experience) -- Notes -- 11. Top 10 Analytics Leadership Skills (with Tom Davenport) -- Introduction -- Recruitment, Retention, People Development -- Generating Demand (Securing Projects by Domain Area) -- Relationship Building -- Understand the Business Domain (and Problem in Question) -- Change Management -- Project Management Communication Skills -- Planning, Budgeting, Administration, and P& -- L Management -- Practitioner Experience and Expertise -- Information Technology (IT) Experience and Expertise -- 12. Top 10 Reasons Analytical Sciences Projects Fail -- Introduction -- Why Data Science Projects Fail -- References -- Part 1: What's the Problem (That You're Trying to Solve)? -- Reference -- Part 2: Data, Data Everywhere ... But Not in One Location to Analyze -- Part 3: Misapplying the Model -- Part 4: Solving a Problem that is Not a Business Priority -- Part 5: Effective Communication -- Stories as Communication -- Part 6: Change Management -- Principles of Change Management -- Interactive Optimization -- Keys to Success -- Justification for Change -- Notes and References -- Part 7: Unrealistic Expectations -- Setting Business Value Benefits -- Part 8: Project Management -- The "Perfect" Project Management -- The Usual Suspects in PM -- Part 9: Excessive Focus on the Model, Technique, or Technology -- Part 10: Getting from Sandbox Model to Production System -- Failure Begets Success -- Conclusion -- Project Challenges -- Managing Change -- 13. Surrounded by Success -- Introduction -- 14. O.R. in 2048 -- Introduction -- Computing -- Models and Algorithms -- Software Tools -- People -- Conclusion -- References and Bibliography -- Index Author Doug Gray shares the most important lessons he has learned over the course of his 30+ year career in analytics and technology. Readers will witness the Analytical Sciences profession through the eye of a practitioner who has delivered solutions that generate hundreds of millions of dollars annually in business value Business analysts Management-Statistical methods Quantitative research Erscheint auch als Druck-Ausgabe Gray, Douglas A. The Art of Data Science Milton : CRC Press LLC,c2025 9781032816968 |
| spellingShingle | Gray, Douglas A. The Art of Data Science A Practitioner's Guide Cover -- Endorsements Page -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Acknowledgments -- About the Author -- Introduction -- Motivation -- Creating a Course and a Legacy -- Organization -- 1. Career Summary: On Becoming a Practitioner and Leader -- The "Key" to My Future -- Flying High (and Overseas) at AADT -- Data Down Under -- Movin' On Up -- Delivering Value -- Ride the Waves -- 2. The Dual Challenge of the Analytical Sciences Practitioner -- Introduction -- 3. Digital Transformation -- Introduction -- Observations and Foundational Experiences -- The Internet, Data, and Analytics in Digital Transformation -- The Why and How of Digital Transformation -- Digital Transformation: A Competitive Imperative -- 4. Advanced Analytics is Economically Transformational -- Introduction -- 5. Airworthy: American Airlines Heavy Maintenance Planning and Scheduling -- Introduction -- 6. Consulting Concepts Learned from Airworthy -- Introduction -- 7. A Modern Day Project Applying the Same Principles: Advanced Analytics Commodity Case -- Introduction -- 8. Right Tool, Right Place, Right Time (with Nader Kabbani) -- Introduction -- 9. Under Fire: Lessons from the Front - Revenue Management for Manufacturing -- Introduction -- 10. Analytics Nontechnical Skills -- Introduction -- What They Do Not Teach You in Your MS Data Science/ Business Analytics Program -- Three Common Reasons DS Projects Don't Get Implemented -- MBAs for Data Scientists (Expensive, but Worth It, in My Experience) -- Notes -- 11. Top 10 Analytics Leadership Skills (with Tom Davenport) -- Introduction -- Recruitment, Retention, People Development -- Generating Demand (Securing Projects by Domain Area) -- Relationship Building -- Understand the Business Domain (and Problem in Question) -- Change Management -- Project Management Communication Skills -- Planning, Budgeting, Administration, and P& -- L Management -- Practitioner Experience and Expertise -- Information Technology (IT) Experience and Expertise -- 12. Top 10 Reasons Analytical Sciences Projects Fail -- Introduction -- Why Data Science Projects Fail -- References -- Part 1: What's the Problem (That You're Trying to Solve)? -- Reference -- Part 2: Data, Data Everywhere ... But Not in One Location to Analyze -- Part 3: Misapplying the Model -- Part 4: Solving a Problem that is Not a Business Priority -- Part 5: Effective Communication -- Stories as Communication -- Part 6: Change Management -- Principles of Change Management -- Interactive Optimization -- Keys to Success -- Justification for Change -- Notes and References -- Part 7: Unrealistic Expectations -- Setting Business Value Benefits -- Part 8: Project Management -- The "Perfect" Project Management -- The Usual Suspects in PM -- Part 9: Excessive Focus on the Model, Technique, or Technology -- Part 10: Getting from Sandbox Model to Production System -- Failure Begets Success -- Conclusion -- Project Challenges -- Managing Change -- 13. Surrounded by Success -- Introduction -- 14. O.R. in 2048 -- Introduction -- Computing -- Models and Algorithms -- Software Tools -- People -- Conclusion -- References and Bibliography -- Index Business analysts Management-Statistical methods Quantitative research |
| title | The Art of Data Science A Practitioner's Guide |
| title_auth | The Art of Data Science A Practitioner's Guide |
| title_exact_search | The Art of Data Science A Practitioner's Guide |
| title_full | The Art of Data Science A Practitioner's Guide |
| title_fullStr | The Art of Data Science A Practitioner's Guide |
| title_full_unstemmed | The Art of Data Science A Practitioner's Guide |
| title_short | The Art of Data Science |
| title_sort | the art of data science a practitioner s guide |
| title_sub | A Practitioner's Guide |
| topic | Business analysts Management-Statistical methods Quantitative research |
| topic_facet | Business analysts Management-Statistical methods Quantitative research |
| work_keys_str_mv | AT graydouglasa theartofdatascienceapractitionersguide |