{"id":6349,"date":"2025-07-04T14:43:20","date_gmt":"2025-07-04T06:43:20","guid":{"rendered":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=6349"},"modified":"2025-11-10T16:20:08","modified_gmt":"2025-11-10T08:20:08","slug":"the-practice-of-business-analytics-fall2025","status":"publish","type":"page","link":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=6349&lang=en","title":{"rendered":"The Practice of Business Analytics (Fall\u20192025)"},"content":{"rendered":"<p><span style=\"font-size: 11pt; color: #208000; font-family: 'consolas';\">\ud83c\udf1e Course ID\uff1a<b>IB533<\/b> (114.1)\u00a0 2025\/09\/11 ~ 2025\/12\/25<br \/>\n\ud83d\uddd3 Time\/Place\uff1a Every Thursday Morning 09:10 ~ 12:00 \/ R3051<br \/>\n\u23f1 Collaborative Learning Session\uff1a t.b.d.<br \/>\n\ud83d\udc69\u200d\ud83c\udfeb Instructor\uff1a <a href=\"https:\/\/msrc.cm.nsysu.edu.tw\/p\/404-1030-313552.php\">Yung-Jan Cho<\/a>, tonychuo@mail.nsysu.edu.tw<\/span><\/p>\n<p><span style=\"font-size: 11pt; font-family: 'Arial Black'; color: red;\"><strong> \u23f0 NOTICES : <\/strong><\/span><span style=\"font-size: 10pt; font-family: 'consolas';\"><br \/>\n<b>\u203b Please do the followings Before the First Class\uff1a<\/b><br \/>\n\u25a0 Fill in the <a href=\"https:\/\/forms.gle\/3HeveWSJY9oNiBv89\">Initial Questionnaire<\/a><br \/>\n\u25a0 Subscribe to our <a href=\"https:\/\/www.youtube.com\/channel\/UCAkk44tj-6OlD_Xrs4HsKWA\">YouTube Channel<\/a><br \/>\n\u25a0 Join our <a href=\"https:\/\/www.facebook.com\/groups\/775839258197893\">Facebook Group<\/a><br \/>\n\u25a0 Bring in your notebook computers with `R` \u548c `RStudio` installed\uff01<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'Arial Black';\">\u25c7 \u3010 <a href=\"https:\/\/www.facebook.com\/groups\/775839258197893\">Facebook<\/a> \u3011 \u25c7 \u3010 <a href=\"https:\/\/www.youtube.com\/channel\/UCAkk44tj-6OlD_Xrs4HsKWA\">YouTube<\/a> \u3011 \u25c7 \u3010 <a href=\"https:\/\/drive.google.com\/drive\/folders\/1tgQAKry4mWQqcxhgNPP-rKAJFaJwxz6N?usp=drive_link\">Google Drive<\/a> \u3011 \u25c7 \u3010 <a href=\"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=6387&amp;lang=en\">TA TEAM<\/a> \u3011 \u25c7<br \/>\n<\/span><\/p>\n<hr \/>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Schedule 2025\/09\/11 ~ 2025\/12\/25<\/span><\/strong><\/h3>\n<p><iframe loading=\"lazy\" src=\"https:\/\/docs.google.com\/spreadsheets\/d\/e\/2PACX-1vQy8UPMSKB9SLqIS7UAEnt9HGk9MqxDZSoyZEKOtTIG1Yz3XhRCr_W2XOjWfOoOD3Ym7Cb7yKliiPg2\/pubhtml?gid=0&amp;single=true&amp;range=A1:F21&amp;headers=false&amp;chrome=false\" width=\"750\" height=\"460\" frameborder=\"0\" scrolling=\"no\"><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><\/iframe><\/p>\n<hr \/>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Course Outlines<\/span><\/strong><\/h3>\n<p><strong><span style=\"font-family: 'andale mono', monospace;\">PART-I: DATA &amp; PROGRAMMING<\/span><\/strong><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 01 Intro. R and RStudio<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 02 Cases: Solving Business Problems by Data Manipulation<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 03 Descriptive Analysis with Simple Plots<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 04 Cases: Exploring Data by Comparison<\/span><\/p>\n<p><strong><span style=\"font-family: 'andale mono', monospace;\">PART-II: APPLICATIONAL PROBABILITY<\/span><\/strong><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 05 Applicational Probability in R<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 06 Case: Data, Model, Prediction, Decision<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 07 Case: Analyze Marketing Research Data<\/span><\/p>\n<p><strong><span style=\"font-family: 'andale mono', monospace;\">PART-III: DATA EXPLORATION<\/span><\/strong><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 08 Explorative Analysis Methods<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 09 Data Visualization Techniques <\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 10 Cases: Clustering and Dimension Reduction<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 11 Case: Retail POS Data<\/span><\/p>\n<p><strong><span style=\"font-family: 'andale mono', monospace;\">PART-IV: PREDICTIVE MODELS<\/span><\/strong><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 12 Predicting Quantity, Linear Regression<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 13 Predicting Probability, Logistic Regression<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 14 Case: Customer Value Management<\/span><\/p>\n<p><strong><span style=\"font-family: 'andale mono', monospace;\">PART-V: BUSINESS LOGIC<\/span><\/strong><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 15 From Decision to Prediction<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 16 Assumption and Simulation<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 17 Performance Evaluation and Optimization<\/span><br \/>\n<span style=\"font-family: 'andale mono', monospace;\">\u00a0 18 Capstone Project: Data Driven Marketing Plan<\/span><\/p>\n<p>&nbsp;<\/p>\n<hr \/>\n<h3><span style=\"color: #008000;\"><strong>Course Description<\/strong><\/span><\/h3>\n<p>Capitalizing the business school\u2019s Big Data Business Analytics Platform (https:\/\/bap2.cm.nsysu.edu.tw\/), in this course we cover: (1) R Language Basics, (2) Data Exploration and Visualization, (3) Application of Probability &amp; Statistics, (4) Applicational R-Packages, and (5) Practical Strategic Planning. Besides the aforementioned topics of business analysis, this course also prepares the students for advanced technical courses related to big data, machine learning and artificial intelligence in the General Management, Digital Marketing and FinTech Micro Curriculums in the ITSA Program (https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=513).<\/p>\n<h3><strong><span style=\"color: #008000;\">Prerequisites<\/span><\/strong><\/h3>\n<p>Although there is no mandatory prerequisites, basic knowledge of probability (high school level) is presumed. As for statistics, we will align with the class of IB502 (110.1) &#8211; Statistics and Quantitative Methods. The course loading is quite heavy. For those who do not have programming experience, it&#8217;d take 6 ~ 10 hours per week to finish the personal and team assignments.<\/p>\n<h3><strong><span style=\"color: #008000;\">Objectives<\/span><\/strong><\/h3>\n<ol>\n<li>Introduce to programming (R) language. Overcome the entry barrier of programming language with interactive notebooks, web-pages and web-based simulation tools.<\/li>\n<li>Develop major business analytics skills in practical data cases.<\/li>\n<li>Practice and experience the synergy among programming language, statistics and managerial knowledge.<\/li>\n<li>Prepare the advanced analytics courses that involve big-data, ma-chine learning and\/or artificial intelligence.<\/li>\n<\/ol>\n<h3 style=\"text-align: left;\"><span style=\"color: #008000;\"><strong>Text Book\u00a0<\/strong><\/span><\/h3>\n<p>No text book is required. There is an optional (online, free) reference book at\uff1a<br \/>\nHadley Wickham and Garrett Grolemund, R for Data Science, O&#8217;REILLY 2016 (https:\/\/r4ds.had.co.nz\/)<\/p>\n<p>We will use self-developed materials, including web pages, program notebooks, app&#8217;s and presentation files as below<\/p>\n<ul>\n<li><a href=\"https:\/\/bap.cm.nsysu.edu.tw\/?page_id=268\">Course website<\/a>: integer the self-made materials and the online resources to facilitate mutual learning. (HTML)<\/li>\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=bNzcONJsc2Y\">Guided videos<\/a>: assist students to understand and preview the online contents.<\/li>\n<li><a href=\"https:\/\/drive.google.com\/open?id=17NYL53esNEs56fvlfhKmXxvanduOjwRy\">Handouts<\/a>: the slides of every unit. (PPT)<\/li>\n<li><a href=\"http:\/\/rpubs.com\/tonychuo\/AS8-0-CVM\">Course notes<\/a>: code used in every course will be provided. (R-Notebook)<\/li>\n<li><a href=\"http:\/\/rpubs.com\/tonychuo\/AS7-1\">Assignment notes<\/a>: the assignment of every unit will be provided. (R-Notebook)<\/li>\n<li><a href=\"http:\/\/140.117.69.135:4949\/A0011\/CustPolicy\/\">Online simulation<\/a>: assist students to understand complicated concepts through the interactive simulation. (R:Shiny)<\/li>\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=ZNC3AQj1Pv8&amp;index=1&amp;list=PLZgYls1tojKn4cRSjR2q0vVys4AWXDsKP\">Course videos<\/a>: the videos of every course will be recorded and provided for students to review. (YouTube)<\/li>\n<\/ul>\n<p>Please review the examples via the web links above.<\/p>\n<h3 style=\"text-align: left;\"><span style=\"color: #008000;\"><strong>Optional Readings<\/strong><\/span><\/h3>\n<p>The optional reference readings are :<\/p>\n<ol>\n<li>Field, A., Miles, J. &amp; Field, Z. (2012). Discovering Statistics Using R. SAGE Publications Ltd.<\/li>\n<li>Kabacoff, R. (2015). R in Action: Data Analysis and Graphics with R. Manning Publications<\/li>\n<li>Peng, R., R Programming for Data Science. The Lean Publishing\u00a0(<a href=\"https:\/\/www.cs.upc.edu\/~robert\/teaching\/estadistica\/rprogramming.pdf\">pdf<\/a>)<\/li>\n<li>Venkatesan, R., Farris, P., &amp; Wilcox, R. T. (2015). Cutting-edge marketing analytics: real world cases and data sets for hands on learning. Pearson Education.<\/li>\n<li>Wickham, H. &amp; Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O&#8217;Reilly Media.<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h3 style=\"text-align: left;\"><span style=\"color: #008000;\"><strong>Online Reference Courses<\/strong><\/span><\/h3>\n<ul>\n<li><span style=\"font-family: 'andale mono', monospace;\">A series of <a href=\"https:\/\/www.edx.org\/professional-certificate\/harvardx-data-science\">Data Science<\/a> courses offered by Harvard University (edX)<\/span>\n<ul>\n<li><span style=\"font-family: 'andale mono', monospace;\"><a href=\"https:\/\/www.edx.org\/course\/data-science-probability\">Data Science: Probability<\/a><\/span><\/li>\n<li><span style=\"font-family: 'andale mono', monospace;\"><a href=\"https:\/\/www.edx.org\/course\/data-science-inference\">Data Science: Inference and Modeling<\/a><\/span><\/li>\n<li><span style=\"font-family: 'andale mono', monospace;\"><a href=\"https:\/\/www.edx.org\/course\/data-science-wrangling-harvardx-ph125-6x\">Data Science: Wrangling<\/a><\/span><\/li>\n<li><span style=\"font-family: 'andale mono', monospace;\"><a href=\"https:\/\/www.edx.org\/course\/data-science-linear-regression\">Data Science: Linear Regression<\/a><\/span><\/li>\n<\/ul>\n<\/li>\n<li><span style=\"font-family: 'andale mono', monospace;\">A series of <a href=\"https:\/\/www.coursera.org\/specializations\/strategic-analytics\">Strategic Business Analytics Specialization<\/a> offered by ESSEC Business School and a services company, Accenture. (Coursera)<\/span>\n<ul>\n<li><span style=\"font-family: 'andale mono', monospace;\"><a href=\"https:\/\/www.coursera.org\/learn\/strategic-business-analytics\">Foundations of strategic business analytics<\/a><\/span><\/li>\n<li><span style=\"font-family: 'andale mono', monospace;\"><a href=\"https:\/\/www.coursera.org\/learn\/foundations-marketing-analytics\">Foundations of marketing analytics<\/a><\/span><\/li>\n<\/ul>\n<\/li>\n<li><span style=\"font-family: 'andale mono', monospace;\">A series of <a href=\"https:\/\/www.edx.org\/micromasters\/business-analytics\">Business Analytics<\/a> courses offered by Columbia University (edX)<\/span>\n<ul>\n<li><span style=\"font-family: 'andale mono', monospace;\"><a href=\"https:\/\/www.edx.org\/course\/data-models-decisions-business-analytics-columbiax-bamm-102x-1\">Data, Models and Decisions in Business Analytics<\/a><\/span><\/li>\n<li><span style=\"font-family: 'andale mono', monospace;\"><a href=\"https:\/\/www.edx.org\/course\/marketing-analytics-columbiax-bamm-104x-0\">Marketing Analytics<\/a><\/span><\/li>\n<li><span style=\"font-family: 'andale mono', monospace;\"><a href=\"https:\/\/www.edx.org\/course\/demand-supply-analytics-columbiax-bamm-103x-0\">Demand and Supply Analytics<\/a><\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<hr \/>\n<h3 style=\"text-align: left;\"><strong><span style=\"color: #008000;\">Previous Course Recording (2022)<\/span><\/strong><\/h3>\n<p><iframe loading=\"lazy\" src=\"https:\/\/docs.google.com\/spreadsheets\/d\/e\/2PACX-1vTPuiOKjNAiLIaNo39-qy5sXKgV8xFegBnNmCoEHOzi-uxPcHIvJcS5C4MM528_tIRaVhCWg_s6-ZmL\/pubhtml?gid=0&amp;single=true&amp;range=A1:F20&amp;headers=false&amp;chrome=false\" width=\"750\" height=\"460\" frameborder=\"0\" scrolling=\"no\"><\/iframe><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ud83c\udf1e Course ID\uff1aIB533 (114.1)\u00a0 2025\/09\/11 ~ 2025\/12\/25 \ud83d\uddd3 Time\/Place\uff1a Every Thursday Morning 09:10 ~ 12:00 \/ R3051 \u23f1 Collaborative Learning Session\uff1a t.b.d. \ud83d\udc69\u200d\ud83c\udfeb Instructor\uff1a Yung-Jan Cho, tonychuo@mail.nsysu.edu.tw \u23f0 NOTICES : \u203b Please do the followings Before the First Class\uff1a \u25a0 Fill in the Initial Questionnaire \u25a0 Subscribe to our YouTube Channel \u25a0 Join our Facebook Group \u25a0 Bring in your notebook computers with `R` \u548c `RStudio` installed\uff01 \u25c7 \u3010 Facebook \u3011 \u25c7 \u3010 YouTube \u3011 \u25c7 \u3010 Google Drive \u3011 \u25c7 \u3010 TA TEAM \u3011 \u25c7 Schedule 2025\/09\/11 ~ 2025\/12\/25 \ufeff Course Outlines PART-I: DATA &amp; PROGRAMMING \u00a0 01 Intro. R and RStudio \u00a0 02 Cases: Solving Business Problems by Data Manipulation \u00a0 03 Descriptive Analysis with Simple Plots \u00a0 04 Cases: Exploring Data by Comparison PART-II: APPLICATIONAL PROBABILITY \u00a0 05 Applicational Probability in R \u00a0 06 Case: Data, Model, Prediction, Decision \u00a0 07 Case: Analyze Marketing Research Data PART-III: DATA EXPLORATION \u00a0 08 Explorative Analysis Methods \u00a0 09 Data Visualization Techniques \u00a0 10 Cases: Clustering and Dimension Reduction \u00a0 11 Case: Retail POS Data PART-IV: PREDICTIVE MODELS \u00a0 12 Predicting Quantity, Linear Regression \u00a0 13 Predicting Probability, Logistic Regression \u00a0 14 Case: Customer Value Management PART-V: BUSINESS LOGIC \u00a0 15 From Decision to Prediction \u00a0 16 Assumption and Simulation \u00a0 17 Performance Evaluation and Optimization \u00a0 18 Capstone Project: Data Driven Marketing Plan &nbsp; Course Description Capitalizing the business school\u2019s Big Data Business Analytics Platform (https:\/\/bap2.cm.nsysu.edu.tw\/), in this course we cover: (1) R Language Basics, (2) Data Exploration and Visualization, (3) Application of Probability &amp; Statistics, (4) Applicational R-Packages, and (5) Practical Strategic Planning. Besides the aforementioned topics of business analysis, this course also prepares the students for advanced technical courses related to big data, machine learning and artificial intelligence in the General Management, Digital Marketing and FinT&#8230;<\/p>\n","protected":false},"author":2,"featured_media":4786,"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-6349","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6349","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\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6349"}],"version-history":[{"count":7,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6349\/revisions"}],"predecessor-version":[{"id":6546,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6349\/revisions\/6546"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/media\/4786"}],"wp:attachment":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6349"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}