{"id":5108,"date":"2023-05-20T16:02:42","date_gmt":"2023-05-20T08:02:42","guid":{"rendered":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5108"},"modified":"2025-03-23T17:27:22","modified_gmt":"2025-03-23T09:27:22","slug":"r%e7%a8%8b%e5%bc%8f%e3%80%81%e6%a9%9f%e7%8e%87%e8%88%87%e7%b5%b1%e8%a8%88111-2","status":"publish","type":"page","link":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5108","title":{"rendered":"R: \u7a0b\u5f0f\u3001\u6a5f\u7387\u8207\u7d71\u8a08 (113-2)"},"content":{"rendered":"<div id=\"pl-5108\"  class=\"panel-layout\" ><div id=\"pg-5108-0\"  class=\"panel-grid panel-no-style\" ><div id=\"pgc-5108-0-0\"  class=\"panel-grid-cell\" ><div id=\"panel-5108-0-0-0\" class=\"so-panel widget widget_sow-editor panel-first-child panel-last-child\" data-index=\"0\" ><div\n\t\t\t\n\t\t\tclass=\"so-widget-sow-editor so-widget-sow-editor-base\"\n\t\t\t\n\t\t>\n<div class=\"siteorigin-widget-tinymce textwidget\">\n\t<h1 style=\"text-align: center;\"><span style=\"font-family: arial, helvetica, sans-serif;\">R: Statistical Programming Methods<\/span><\/h1>\n<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>Course Syllabus<\/strong><\/span><\/h4>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">\u5728\u8207\u7ba1\u9662\u5927\u6578\u64da\u5e73\u53f0\u76f8\u5c0d\u61c9\uff0c\u6b64\u8ab2\u7a0b\u5c07\u8457\u91cd\u5728\u4ee5R\u7a0b\u5f0f\u8a9e\u8a00\u8b1b\u6388\u7d71\u8a08\u8207\u6a5f\u7387\u6982\u8ad6\u7b49\u8ab2\u984c\u3002\u6709\u5225\u65bc\u4e00\u822c\u7d71\u8a08\u8ab2\u7a0b\uff0c\u672c\u8ab2\u7a0b\u5c07\u4ee5R\u7a0b\u5f0f\u8a9e\u8a00\u65b9\u5f0f\u5f15\u5c0e\u5b78\u751f\u4e86\u89e3\u7d71\u8a08\u8207\u6a5f\u7387\u6a21\u578b\uff0c\u4e26\u80fd\u5920\u5c07\u7d71\u8a08\u77e5\u8b58\u76f4\u63a5\u904b\u7528\u5728R\u4e0a\uff0c\u9032\u800c\u8b93\u5b78\u751f\u76f4\u63a5\u5c07\u7d71\u8a08\u904b\u7528\u5728\u6578\u64da\u5206\u6790\u4e0a\u7684\u5be6\u4f5c\u3002\u85c9\u7531\u6b64\u8ab2\u7a0b\uff0c\u5b78\u751f\u80fd\u5920\u5177\u5099\u904b\u7528R\u505a\u7d71\u8a08\u5206\u6790\u7684\u80fd\u529b\uff0c\u70ba\u4e4b\u5f8c\u5546\u696d\u5be6\u52d9\u5206\u6790\u53ca\u61c9\u7528\u6709\u66f4\u597d\u7684\u7d71\u8a08\u57fa\u790e\u3002<\/span><\/p>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Aligning with the Big Data Business Analytics Platform, NSYSU, this course is aimed to focus on the delivery of statistical methods using R language. Compared to the conventional statistics courses focuses on the concept only, this course is aimed to develop the fundamental and working statistical knowledge with the hands-on experience for students to conduct further advanced business analysis.<\/span><\/p>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">The course will include:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">R programming<\/span><\/li>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Data Analysis<\/span><\/li>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Statistical Experiments and introduction of probability<\/span><\/li>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Regression and Prediction<\/span><\/li>\n<\/ul>\n<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>Objectives<\/strong><\/span><\/h4>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">By the end of the course, students are expected to:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Understand the basic R language and use Rstudio<\/span><\/li>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Manage and filter data to support analysis<\/span><\/li>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Apply statistical tools in R to analyze data and discuss findings<\/span><\/li>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Demonstrate data visualization techniques<\/span><\/li>\n<li><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Develop regression models<\/span><\/li>\n<\/ul>\n<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>Textbook<\/strong><\/span><\/h4>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Peng, R., 2016. <em>R Programming for Data Science<\/em>. The Lean Publishing<\/span><br \/><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Field, A., Miles, J. &amp; Field, Z., 2012. <em>Discovering Statistics Using R<\/em>. SAGE Publications Ltd<\/span><br \/><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Bruce, P., Bruce, A., &amp; Gedeck, P., 2017. <em>Practical Statistics for Data Scientists: 50 Essential Concepts Using R and Python<\/em>. O\u2019Reilly<\/span><\/p>\n<h4><span style=\"color: #008000; font-family: 'times new roman', times, serif; font-size: 14pt;\"><strong>Components<\/strong><\/span><\/h4>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Class participation: 15%<\/span><\/p>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Individual Assignment: 25%<\/span><\/p>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Midterm Open-book exam: 25%<\/span><\/p>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Final Project: 30%<\/span><\/p>\n<p><span style=\"font-family: 'times new roman', times, serif; font-size: 14pt;\">Peer Review: 10%<\/span><\/p>\n<table style=\"width: 55%; font-size: 13pt; height: 442px;\">\n<tbody>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px; background-color: #acd4ff;\"><span style=\"font-family: 'times new roman', times, serif;\">Week<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px; text-align: center; background-color: #acd4ff;\"><span style=\"font-family: 'times new roman', times, serif;\">Syllabus<\/span><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">1<\/span><\/td>\n<td width=\"449\" style=\"width: 91%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5255\"><span style=\"font-family: 'times new roman', times, serif;\">Introduction to course<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">2<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5271\"><span style=\"font-family: 'times new roman', times, serif;\">Basic R Language and Introduction to R studio<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">3<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5282\"><span style=\"font-family: 'times new roman', times, serif;\">Exploratory Data Analysis (1)<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">4<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5288\"><span style=\"font-family: 'times new roman', times, serif;\">Exploratory Data Analysis (2)<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td tyle=\"text-align: right; width: 9%;\" style=\"text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">5<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5291\"><span style=\"font-family: 'times new roman', times, serif;\">Data Visualization (1)<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">6<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5297\"><span style=\"font-family: 'times new roman', times, serif;\">Data Visualization (2)<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">7<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5299\"><span style=\"font-family: 'times new roman', times, serif;\">Sampling Distribution<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">8<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5305\"><span style=\"font-family: 'times new roman', times, serif;\">Midterm and Practice<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">9<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5308\"><span style=\"font-family: 'times new roman', times, serif;\">Significance Testing (1)<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 9%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">10<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5310\"><span style=\"font-family: 'times new roman', times, serif;\">Significance Testing (2)<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">11<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5318\"><span style=\"font-family: 'times new roman', times, serif;\">Simple Linear Regression<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">12<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5320\"><span style=\"font-family: 'times new roman', times, serif;\">Interpretation of Linear Regression<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">13<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5323\"><span style=\"font-family: 'times new roman', times, serif;\">Logistic Regression<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">14<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5328\"><span style=\"font-family: 'times new roman', times, serif;\">Hands-on Practice and Group Discussion<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">15<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5332\"><span style=\"font-family: 'times new roman', times, serif;\">Group Project<\/span><\/a><\/td>\n<\/tr>\n<tr style=\"height: 26px;\">\n<td style=\"width: 13.2617%; text-align: center; height: 26px;\"><span style=\"font-family: 'times new roman', times, serif;\">16<\/span><\/td>\n<td width=\"449\" style=\"width: 69.3372%; height: 26px;\"><a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=5332\"><span style=\"font-family: 'times new roman', times, serif;\">Group Project<\/span><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>R: Statistical Programming Methods Course Syllabus \u5728\u8207\u7ba1\u9662\u5927\u6578\u64da\u5e73\u53f0\u76f8\u5c0d\u61c9\uff0c\u6b64\u8ab2\u7a0b\u5c07\u8457\u91cd\u5728\u4ee5R\u7a0b\u5f0f\u8a9e\u8a00\u8b1b\u6388\u7d71\u8a08\u8207\u6a5f\u7387\u6982\u8ad6\u7b49\u8ab2\u984c\u3002\u6709\u5225\u65bc\u4e00\u822c\u7d71\u8a08\u8ab2\u7a0b\uff0c\u672c\u8ab2\u7a0b\u5c07\u4ee5R\u7a0b\u5f0f\u8a9e\u8a00\u65b9\u5f0f\u5f15\u5c0e\u5b78\u751f\u4e86\u89e3\u7d71\u8a08\u8207\u6a5f\u7387\u6a21\u578b\uff0c\u4e26\u80fd\u5920\u5c07\u7d71\u8a08\u77e5\u8b58\u76f4\u63a5\u904b\u7528\u5728R\u4e0a\uff0c\u9032\u800c\u8b93\u5b78\u751f\u76f4\u63a5\u5c07\u7d71\u8a08\u904b\u7528\u5728\u6578\u64da\u5206\u6790\u4e0a\u7684\u5be6\u4f5c\u3002\u85c9\u7531\u6b64\u8ab2\u7a0b\uff0c\u5b78\u751f\u80fd\u5920\u5177\u5099\u904b\u7528R\u505a\u7d71\u8a08\u5206\u6790\u7684\u80fd\u529b\uff0c\u70ba\u4e4b\u5f8c\u5546\u696d\u5be6\u52d9\u5206\u6790\u53ca\u61c9\u7528\u6709\u66f4\u597d\u7684\u7d71\u8a08\u57fa\u790e\u3002 Aligning with the Big Data Business Analytics Platform, NSYSU, this course is aimed to focus on the delivery of statistical methods using R language. Compared to the conventional statistics courses focuses on the concept only, this course is aimed to develop the fundamental and working statistical knowledge with the hands-on experience for students to conduct further advanced business analysis. The course will include: R programming Data Analysis Statistical Experiments and introduction of probability Regression and Prediction Objectives By the end of the course, students are expected to: Understand the basic R language and use Rstudio Manage and filter data to support analysis Apply statistical tools in R to analyze data and discuss findings Demonstrate data visualization techniques Develop regression models Textbook Peng, R., 2016. R Programming for Data Science. The Lean PublishingField, A., Miles, J. &amp; Field, Z., 2012. Discovering Statistics Using R. SAGE Publications LtdBruce, P., Bruce, A., &amp; Gedeck, P., 2017. Practical Statistics for Data Scientists: 50 Essential Concepts Using R and Python. O\u2019Reilly Components Class participation: 15% Individual Assignment: 25% Midterm Open-book exam: 25% Final Project: 30% Peer Review: 10% Week Syllabus 1 Introduction to course 2 Basic R Language and Introduction to R studio 3 Exploratory Data Analysis (1) 4 Exploratory Data Analysis (2) 5 Data Visualization (1) 6 Data Visualization (2) 7 Sampling Distribution 8 Midterm and Practice 9 Significance Testing (1) 10 Significance Testing (2) 11 Simple Linear Regression 12 Interpretation of Linear Regression 13 Logistic Regression 14 Hands-on Practice and Group Discussion 15 Group Project 16 Group Project<\/p>\n","protected":false},"author":69,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-5108","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/5108","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/users\/69"}],"replies":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5108"}],"version-history":[{"count":14,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/5108\/revisions"}],"predecessor-version":[{"id":6189,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/5108\/revisions\/6189"}],"wp:attachment":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5108"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}