[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-241537-105":53,"doc-detail-241537-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","project-execution-plan-part-2-course-project","Project Execution Plan - Part 2 - Course Project","","Course Project Part 2 defines the required Project Execution Plan for DATA 301: Introduction to Data Science. The milestone extends a prior project proposal and guides teams from initial feedback into active work, covering data acquisition and preprocessing, feature engineering, exploratory analysis, and initial machine learning modeling. The plan functions as a design document describing, for each analytical problem, the datasets and methodology, including required sections for title, dataset description, and project design with evaluation.",{"@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":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/project-execution-plan-part-2-course-project/241537/",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/project-execution-plan-part-2-course-project/241537.png","ImageObject",442,249,{"name":88,"@type":89},"Liam","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-11","2026-09-12",true,{"@type":98,"interactionType":99,"userInteractionCount":73},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the purpose of the Project Execution Plan in this course project?","Question",{"text":108,"@type":109},"It is a design document that specifies, for each analytical problem, what data will be used and what methodology will be applied, reflecting what the team is actually doing as the milestone advances.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What tasks are teams expected to begin before the November 18 deadline?",{"text":113,"@type":109},"Teams should start active work, including data acquisition, cleaning and preparation, feature engineering, exploratory data analysis, and building initial machine learning models to the extent they are comfortable.",{"name":115,"@type":106,"acceptedAnswer":116},"What sections must be included in the Project Execution Plan submission?",{"text":117,"@type":109},"The plan must include a title page, a dataset description section (with provenance, structure, and data quality/transformation notes), and a project design section that addresses each analytical question with data choices, model types, and evaluation approach.","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},241537,1791730284,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":135,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":79,"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":140,"read_time":73},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","DATA 301 : Introduction to Data Science Cal Poly, San Luis Obispo  \nFall 2025  \nCourse Project  \nPart 2: Project Execution Plan  \nDue Date: November 18 (Tuesday)  \nThe second project milestone is the Project Execution Plan. This document is a natural extension and expansion of the project proposal your team submitted last week. Below we describe what information we want to see in this document.  \nWork on Project. The Project Execution Plan is a milestone for your project, but it should not be construed to be the only thing you work on before the November 18 deadline.  \nBy the end of Thursday, November 11 , each team will have received informal project proposal feedback. Additional feedback will be provided via slack/gradescope over the weekend. Based on this feedback, we are encouraging each team to revise and revisit the questions you plan to study (and on occasion - the data you plan to use) to account for the comments. As of the moment, each team is expected to start active work on the project including:  \n● Data acquisition, cleaning, preparation, as necessitated by the needs of your project.  \n● Feature engineering to construct the dataset (datasets) you intend to use for your analysis  \n● Exploratory data analysis to gain understanding of what is in the data  \n● Building initial machine learning models (to the degree you feel comfortable) - to understand how well you may be able to establish relationships in the data.  \nThe Project Execution Plan you will be writing and submitting is essentially a design document specifying, for each problem you plan to study, what data you will be using, and what methodology you will be applying. This should be a natural byproduct of your work with project data, and your efforts to clarify-first and foremost to yourselves (and only then-as a byproduct of this milestone-to the instructor team) -what you are actually doing.  \nThe Project Execution Plan shall consist of the following:  \n1. Title page. The title page shall contain the name of the project, the name of the team, the names and Stanford email addresses of all team members, and the discussion section the team is in.  \n2. Dataset description. This should be a relatively short section giving us the precise description of all the data you are planning on using in the project. This section shall expand on your project proposal section specifying the data in the following ways:  \na. You shall identify all individual data files/data sources you will be using in your analysis. For each data file/data source:  \ni. provide information about its provenance. If you are using existing data files - provide the link to the specific data file (note: by \"provide the link\"we mean include the actual URL!) , and - if appropriate - cite any accompanying academic publications. If you are creating your own data, acknowledge that and (see below) provide the description of what is in your data.  \nii. Discuss its structure. Data files may have a lot of features, and it might not be appropriate to describe all of them in your Project Execution Plan. If there is a data dictionary available - you can cite it/link to it for a thorough description of the dataset. If there is none (for example, if this is a dataset you created) , you may put the complete data dictionary in the appendix. However you choose to reference the full structure of the dataset, your Project Execution Plan shall include some information on  \n(a) what are the observational units in the data file (i.e. , what information is contained in one row of data) , (b) what are the features found in the data file that are important to you, and (c ) what the overall size/shape of the data file (in terms of number of records, number of attributes) is. Finally, if the data file has missing values or other data quality issues that you will be fixing as part of your project, discuss them as well.  \nb. You shall include a brief discussion of any data transformations (aggregations, joins, attribute conve","cbCaipRNzRJHKU4T","https://ap.wps.com/l/cbCaipRNzRJHKU4T","pdf",107635,6,"English","# Project Execution Plan Requirements\n## What Teams Should Do Before Submission\n## Core Purpose of the Execution Plan\n## Required Sections\n## Submission Process","[{\"question\":\"What is the purpose of the Project Execution Plan in this course project?\",\"answer\":\"It is a design document that specifies, for each analytical problem, what data will be used and what methodology will be applied, reflecting what the team is actually doing as the milestone advances.\"},{\"question\":\"What tasks are teams expected to begin before the November 18 deadline?\",\"answer\":\"Teams should start active work, including data acquisition, cleaning and preparation, feature engineering, exploratory data analysis, and building initial machine learning models to the extent they are comfortable.\"},{\"question\":\"What sections must be included in the Project Execution Plan submission?\",\"answer\":\"The plan must include a title page, a dataset description section (with provenance, structure, and data quality/transformation notes), and a project design section that addresses each analytical question with data choices, model types, and evaluation approach.\"}]","Project Execution Plan - Part 2 - Course Project | PDF",1789176990]