Why AI/Data Science Projects Fail:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
San Rafael
Morgan & Claypool Publishers
2020
|
| Schriftenreihe: | Synthesis Lectures on Computation and Analytics Ser
|
| Online-Zugang: | BFP01 HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 online resource (79 pages) |
| ISBN: | 9781636390390 |
Internformat
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| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Intro -- Preface -- Introduction and Background -- Project Phases and Common Project Pitfalls -- 2.1 Tips for Managers -- Five Methods to Avoid Common Pitfalls -- 3.1 Ask Questions -- 3.2 Get Alignment -- 3.3 Keep It Simple -- 3.4 Leverage Explainability -- 3.5 Have the Conversation -- 3.6 Tips for Managers -- Define Phase -- 4.1 Project Charter -- 4.2 Supplier-Input-Process-Output-Customer (SIPOC) Analysis -- 4.3 Tips for Managers -- Making the Business Case: Assigning Value to Your Project -- 5.1 Data Analysis Projects -- 5.2 Automation Projects -- 5.3 Improving Business Processes -- 5.4 Data Mining Projects -- 5.5 Improved Data Science -- 5.6 Metrics to Dollar Conversion -- Acquisition and Exploration of Data Phase -- 6.1 Acquiring Data -- 6.2 Developing Data Collection Systems -- 6.3 Data Exploration -- 6.4 What Does the Customer Want to Know? -- 6.5 Preparing for a Report or Model -- 6.6 Tips for Managers -- Model-Building Phase -- 7.1 Keep it Simple -- 7.2 Repeatability -- 7.3 Leverage Explainability -- 7.4 Tips for Managers -- Interpret and communicate phase -- 8.1 Know Your Audience -- 8.2 Reports -- 8.3 Presentations -- 8.4 Models -- 8.5 Tips for Mangers -- Deployment Phase -- 9.1 Plan for Deployment from the Start -- 9.2 Documentation -- 9.3 Maintenance -- 9.4 Tips for Managers -- Summary of the Five Methods to Avoid Common Pitfalls -- 10.1 Ask Questions -- 10.2 Get Alignment -- 10.3 Keep It Simple -- 10.4 Leverage Explainability -- 10.5 Have the Conversation -- References -- Author Biography -- Table 1.1: Five project pitfalls -- Table 1.2: Alignment between data science project phases and Lean six sigma DMAIC framework -- Table 3.1: Connection between the methods to avoid pitfalls and the five project pitfalls -- Table 3.2: Questions to ask at retrospectives -- Table 4.1: Key components of a project charter | |
| 505 | 8 | |a Table 5.1: Deliverables and metrics for various types of data science projects -- Table 5.2: Example calculation for time saved -- Table 5.3: Types of waste with manufacturing and office examples -- Table 5.4: Common metrics and dollar conversion -- Table 8.1: Data science project types and typical final deliverables -- Table 8.2: Data visualization reading list -- Blank Page | |
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Datensatz im Suchindex
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| contents | Intro -- Preface -- Introduction and Background -- Project Phases and Common Project Pitfalls -- 2.1 Tips for Managers -- Five Methods to Avoid Common Pitfalls -- 3.1 Ask Questions -- 3.2 Get Alignment -- 3.3 Keep It Simple -- 3.4 Leverage Explainability -- 3.5 Have the Conversation -- 3.6 Tips for Managers -- Define Phase -- 4.1 Project Charter -- 4.2 Supplier-Input-Process-Output-Customer (SIPOC) Analysis -- 4.3 Tips for Managers -- Making the Business Case: Assigning Value to Your Project -- 5.1 Data Analysis Projects -- 5.2 Automation Projects -- 5.3 Improving Business Processes -- 5.4 Data Mining Projects -- 5.5 Improved Data Science -- 5.6 Metrics to Dollar Conversion -- Acquisition and Exploration of Data Phase -- 6.1 Acquiring Data -- 6.2 Developing Data Collection Systems -- 6.3 Data Exploration -- 6.4 What Does the Customer Want to Know? -- 6.5 Preparing for a Report or Model -- 6.6 Tips for Managers -- Model-Building Phase -- 7.1 Keep it Simple -- 7.2 Repeatability -- 7.3 Leverage Explainability -- 7.4 Tips for Managers -- Interpret and communicate phase -- 8.1 Know Your Audience -- 8.2 Reports -- 8.3 Presentations -- 8.4 Models -- 8.5 Tips for Mangers -- Deployment Phase -- 9.1 Plan for Deployment from the Start -- 9.2 Documentation -- 9.3 Maintenance -- 9.4 Tips for Managers -- Summary of the Five Methods to Avoid Common Pitfalls -- 10.1 Ask Questions -- 10.2 Get Alignment -- 10.3 Keep It Simple -- 10.4 Leverage Explainability -- 10.5 Have the Conversation -- References -- Author Biography -- Table 1.1: Five project pitfalls -- Table 1.2: Alignment between data science project phases and Lean six sigma DMAIC framework -- Table 3.1: Connection between the methods to avoid pitfalls and the five project pitfalls -- Table 3.2: Questions to ask at retrospectives -- Table 4.1: Key components of a project charter Table 5.1: Deliverables and metrics for various types of data science projects -- Table 5.2: Example calculation for time saved -- Table 5.3: Types of waste with manufacturing and office examples -- Table 5.4: Common metrics and dollar conversion -- Table 8.1: Data science project types and typical final deliverables -- Table 8.2: Data visualization reading list -- Blank Page |
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| format | Electronic eBook |
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| id | DE-604.BV047688962 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:57:01Z |
| indexdate | 2024-07-10T09:19:15Z |
| institution | BVB |
| isbn | 9781636390390 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033072977 |
| oclc_num | 1298742092 |
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| owner_facet | DE-2070s DE-525 |
| physical | 1 online resource (79 pages) |
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| publishDate | 2020 |
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| publisher | Morgan & Claypool Publishers |
| record_format | marc |
| series2 | Synthesis Lectures on Computation and Analytics Ser |
| spelling | Weiner, Joyce Verfasser aut Why AI/Data Science Projects Fail San Rafael Morgan & Claypool Publishers 2020 ©2020 1 online resource (79 pages) txt rdacontent c rdamedia cr rdacarrier Synthesis Lectures on Computation and Analytics Ser Description based on publisher supplied metadata and other sources Intro -- Preface -- Introduction and Background -- Project Phases and Common Project Pitfalls -- 2.1 Tips for Managers -- Five Methods to Avoid Common Pitfalls -- 3.1 Ask Questions -- 3.2 Get Alignment -- 3.3 Keep It Simple -- 3.4 Leverage Explainability -- 3.5 Have the Conversation -- 3.6 Tips for Managers -- Define Phase -- 4.1 Project Charter -- 4.2 Supplier-Input-Process-Output-Customer (SIPOC) Analysis -- 4.3 Tips for Managers -- Making the Business Case: Assigning Value to Your Project -- 5.1 Data Analysis Projects -- 5.2 Automation Projects -- 5.3 Improving Business Processes -- 5.4 Data Mining Projects -- 5.5 Improved Data Science -- 5.6 Metrics to Dollar Conversion -- Acquisition and Exploration of Data Phase -- 6.1 Acquiring Data -- 6.2 Developing Data Collection Systems -- 6.3 Data Exploration -- 6.4 What Does the Customer Want to Know? -- 6.5 Preparing for a Report or Model -- 6.6 Tips for Managers -- Model-Building Phase -- 7.1 Keep it Simple -- 7.2 Repeatability -- 7.3 Leverage Explainability -- 7.4 Tips for Managers -- Interpret and communicate phase -- 8.1 Know Your Audience -- 8.2 Reports -- 8.3 Presentations -- 8.4 Models -- 8.5 Tips for Mangers -- Deployment Phase -- 9.1 Plan for Deployment from the Start -- 9.2 Documentation -- 9.3 Maintenance -- 9.4 Tips for Managers -- Summary of the Five Methods to Avoid Common Pitfalls -- 10.1 Ask Questions -- 10.2 Get Alignment -- 10.3 Keep It Simple -- 10.4 Leverage Explainability -- 10.5 Have the Conversation -- References -- Author Biography -- Table 1.1: Five project pitfalls -- Table 1.2: Alignment between data science project phases and Lean six sigma DMAIC framework -- Table 3.1: Connection between the methods to avoid pitfalls and the five project pitfalls -- Table 3.2: Questions to ask at retrospectives -- Table 4.1: Key components of a project charter Table 5.1: Deliverables and metrics for various types of data science projects -- Table 5.2: Example calculation for time saved -- Table 5.3: Types of waste with manufacturing and office examples -- Table 5.4: Common metrics and dollar conversion -- Table 8.1: Data science project types and typical final deliverables -- Table 8.2: Data visualization reading list -- Blank Page Erscheint auch als Druck-Ausgabe Weiner, Joyce Why AI/Data Science Projects Fail San Rafael : Morgan & Claypool Publishers,c2020 9781636390406 |
| spellingShingle | Weiner, Joyce Why AI/Data Science Projects Fail Intro -- Preface -- Introduction and Background -- Project Phases and Common Project Pitfalls -- 2.1 Tips for Managers -- Five Methods to Avoid Common Pitfalls -- 3.1 Ask Questions -- 3.2 Get Alignment -- 3.3 Keep It Simple -- 3.4 Leverage Explainability -- 3.5 Have the Conversation -- 3.6 Tips for Managers -- Define Phase -- 4.1 Project Charter -- 4.2 Supplier-Input-Process-Output-Customer (SIPOC) Analysis -- 4.3 Tips for Managers -- Making the Business Case: Assigning Value to Your Project -- 5.1 Data Analysis Projects -- 5.2 Automation Projects -- 5.3 Improving Business Processes -- 5.4 Data Mining Projects -- 5.5 Improved Data Science -- 5.6 Metrics to Dollar Conversion -- Acquisition and Exploration of Data Phase -- 6.1 Acquiring Data -- 6.2 Developing Data Collection Systems -- 6.3 Data Exploration -- 6.4 What Does the Customer Want to Know? -- 6.5 Preparing for a Report or Model -- 6.6 Tips for Managers -- Model-Building Phase -- 7.1 Keep it Simple -- 7.2 Repeatability -- 7.3 Leverage Explainability -- 7.4 Tips for Managers -- Interpret and communicate phase -- 8.1 Know Your Audience -- 8.2 Reports -- 8.3 Presentations -- 8.4 Models -- 8.5 Tips for Mangers -- Deployment Phase -- 9.1 Plan for Deployment from the Start -- 9.2 Documentation -- 9.3 Maintenance -- 9.4 Tips for Managers -- Summary of the Five Methods to Avoid Common Pitfalls -- 10.1 Ask Questions -- 10.2 Get Alignment -- 10.3 Keep It Simple -- 10.4 Leverage Explainability -- 10.5 Have the Conversation -- References -- Author Biography -- Table 1.1: Five project pitfalls -- Table 1.2: Alignment between data science project phases and Lean six sigma DMAIC framework -- Table 3.1: Connection between the methods to avoid pitfalls and the five project pitfalls -- Table 3.2: Questions to ask at retrospectives -- Table 4.1: Key components of a project charter Table 5.1: Deliverables and metrics for various types of data science projects -- Table 5.2: Example calculation for time saved -- Table 5.3: Types of waste with manufacturing and office examples -- Table 5.4: Common metrics and dollar conversion -- Table 8.1: Data science project types and typical final deliverables -- Table 8.2: Data visualization reading list -- Blank Page |
| title | Why AI/Data Science Projects Fail |
| title_auth | Why AI/Data Science Projects Fail |
| title_exact_search | Why AI/Data Science Projects Fail |
| title_exact_search_txtP | Why AI/Data Science Projects Fail |
| title_full | Why AI/Data Science Projects Fail |
| title_fullStr | Why AI/Data Science Projects Fail |
| title_full_unstemmed | Why AI/Data Science Projects Fail |
| title_short | Why AI/Data Science Projects Fail |
| title_sort | why ai data science projects fail |
| work_keys_str_mv | AT weinerjoyce whyaidatascienceprojectsfail |