[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-194411-105":53,"doc-detail-194411-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","chi22-nb2slide","CHI22-NB2Slide","","This document outlines a workflow for data science projects, encompassing requirement gathering, data acquisition and governance, data readiness, preprocessing, and cleaning. It details the process of feature engineering, model building and training, and model presentation and stakeholder verification. The workflow also includes aspects of runtime monitoring, model deployment, model refinement, and decision-making and optimization. Specific sections cover data sourcing, exploratory data analysis, and data cleaning. The model development process includes input, output, optimization goals, alternative model considerations, and detailed model specifications. Model performance evaluation involves metrics, overall performance assessment, and model interpretation. The conclusion provides suggestions, discusses ethical and legal considerations, and outlines limitations and risks. Appendices offer further details on exploratory data analysis, data cleaning, feature engineering, model alternatives, and model details, accompanied by sample data tables.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/chi22-nb2slide/194411/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/chi22-nb2slide/194411.png","ImageObject",442,249,{"name":88,"@type":89},"Mali","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-27","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What are the key stages in the data science workflow presented?","Question",{"text":108,"@type":109},"The workflow includes requirement gathering, data acquisition, data readiness, feature engineering, model building, model performance evaluation, and deployment.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What does the 'Model Details' section cover?",{"text":113,"@type":109},"The 'Model Details' section in the appendix provides further specifications and information about the developed models.",{"name":115,"@type":106,"acceptedAnswer":116},"What are the main topics covered in the 'Model Performance' section?",{"text":117,"@type":109},"The 'Model Performance' section focuses on evaluation metrics, overall performance assessment, and interpretation of the model's results.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},194411,1788438957,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":47,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":125,"read_time":140},2336475104362,"https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868","| Section | Subsection |\n| --- | --- |\n| Introduction | Purpose and Intended use Workfow |\n| Data | Data Source\u003Cbr>Exploratory Data Analysis Data Cleaning\u003Cbr>Feature Engineering |\n| Model | Model Input\u003Cbr>Model Output Optimization Goal Model Alternatives\u003Cbr>Model Details |\n| Model Performance | Metrics\u003Cbr>Performance\u003Cbr>Model Interpretation |\n| Conclusion | Suggestions\u003Cbr>Ethical & Legal considerations Limitation & Risks |\n\n\n| Section | Subsection |\n| --- | --- |\n| Introduction | Purpose and Intended use Workfow\u003Cbr>Data Source |\n| Model | Model Input\u003Cbr>Model Output\u003Cbr>Optimization Goal |\n| Model Performance | Metrics\u003Cbr>Performance\u003Cbr>Model Interpretation |\n| Conclusion | Suggestions\u003Cbr>Ethical & Legal considerations Limitation & Risks |\n| Appendix: Data | Exploratory Data Analysis Data Cleaning\u003Cbr>Feature Engineering |\n| Appendix: Model | Model Alternatives\u003Cbr>Model Details |\n\n\n| PID | Gender | Job Roles in Data Science |\n| --- | --- | --- |\n| P01 | Male | Citizen Data Scientist |\n| P02 | Male | Citizen Data Scientist |\n| P03 | Male | AI-Ops/ML-Ops |\n| P04 | Male | AI-Ops/ML-Ops |\n| P05 | Female | Expert Data Scientist |\n| P06 | Male | Expert Data Scientist |\n| P07 | Female | Expert Data Scientist |\n\n| Section | \\# slides | \\# notebooks | Precision |\n| --- | --- | --- | --- |\n| EDA | 21 | 21 | 0.72 |\n| Data Cleaning | 14 | 13 | 0.67 |\n| Feature Engineering | 10 | 12 | 0.75 |\n| Model Input | 21 | 19 | 0.52 |\n| Model Output | 17 | 19 | 0.65 |\n| Model Performance | 20 | 19 | 0.76 |\n| Model Details | 20 | 19 | 0.84 |","cbCaibaaf8526fth","https://ap.wps.com/l/cbCaibaaf8526fth","pdf",3130610,20,"English","# Introduction\n## Purpose and Intended use Workfow\n# Data\n## Data Source\n## Exploratory Data Analysis\n## Data Cleaning\n## Feature Engineering\n# Model\n## Model Input\n## Model Output\n## Optimization Goal\n## Model Alternatives\n## Model Details\n# Model Performance\n## Metrics\n## Performance\n## Model Interpretation\n# Conclusion\n## Suggestions\n## Ethical & Legal considerations\n## Limitation & Risks\n# Appendix: Data\n## Exploratory Data Analysis\n## Data Cleaning\n## Feature Engineering\n# Appendix: Model\n## Model Alternatives\n## Model Details","[{\"question\":\"What are the key stages in the data science workflow presented?\",\"answer\":\"The workflow includes requirement gathering, data acquisition, data readiness, feature engineering, model building, model performance evaluation, and deployment.\"},{\"question\":\"What does the 'Model Details' section cover?\",\"answer\":\"The 'Model Details' section in the appendix provides further specifications and information about the developed models.\"},{\"question\":\"What are the main topics covered in the 'Model Performance' section?\",\"answer\":\"The 'Model Performance' section focuses on evaluation metrics, overall performance assessment, and interpretation of the model's results.\"}]","CHI22-NB2Slide | PDF",7]