大數據分析：商業應用與策略管理 (Big Data Analytics: Business Applications and Strategic Decisions)

Go to Course: https://www.coursera.org/learn/bigdataanalysis

Introduction

## Course Review: Big Data Analytics: Business Applications and Strategic Decisions on Coursera ### Overview In the rapidly advancing world of technology, understanding big data analytics is no longer confined to data science professionals. Coursera’s course, **“大數據分析：商業應用與策略管理”** (Big Data Analytics: Business Applications and Strategic Decisions), is specifically tailored for individuals from management backgrounds who wish to delve into the practical applications of big data in business settings. This course is conducted in Mandarin and is designed to broaden your perspective on how big data can reshape decision-making processes within various industries. ### Course Structure The course spans six weeks, each uniquely tackling specific subjects within the realm of big data and its business applications. Here’s a breakdown of the weekly topics: 1. **Week 1: Introduction to Big Data** The course kicks off with an engaging dialogue between academia and industry professionals. This session sets the stage for understanding the relevance of big data in today’s business environment, supplemented by a live event where participants are able to interact and discuss real-world challenges. 2. **Week 2: Financial and Financial Services Applications** This week explores data analytics within the financial sector, detailing how institutions can leverage databases to identify trends and refine services. Insights into decision trees and correlation analysis will be presented, illustrating how data can predict customer behaviors. 3. **Week 3: Marketing and Retail Applications** Similar in structure to Week 2, this session emphasizes the significant advantages data analytics provides in marketing and retail. Students will learn about correlation analysis and its applications in market segmentation, while also understanding classic marketing theories. 4. **Week 4: Social Media Sentiment Analysis** In this module, students will learn how to harness data from social media platforms to make informed business decisions. The focus will be on real-time analysis of consumer sentiment, paving the way for more targeted marketing strategies. 5. **Week 5: Social Media Analysis and Marketing Intelligence** Building on the previous module, this week delves deeper into analyzing user-generated content and its implications for brand management. Techniques such as sentiment analysis and competitive market structure analysis will be explored in depth. 6. **Week 6: Strategic Business Applications of Big Data** The course culminates with a case study led by industry expert Lee Cheng-Kuo from E.SUN Commercial Bank. This session offers practical insights into how big data has transformed banking strategies and addresses the challenges and opportunities banks face today. ### Learning Experience Participants in this course can expect a well-structured learning environment with a mixture of theoretical foundations and practical industry applications. The course is designed to foster interaction, allowing for questions and rich discussions through online discussion forums. The mix of live introductory sessions and expertly crafted lessons from professionals ensures a comprehensive learning experience. ### Recommendations **Who Should Enroll?** This course is ideal for: - Business professionals aiming to enhance their understanding of big data. - Students interested in the intersection of data science and business strategy. - Anyone curious about the practical applications of big data across various sectors like finance, marketing, and social media. **Why Take This Course?** 1. **Non-Technical Focus**: This course effectively demystifies big data analytics, making it accessible to those without a technical background. 2. **Expert Insights**: Access to industry professionals provides invaluable perspectives that bridge the gap between academic theories and real-world applications. 3. **Career Advancement**: For professionals, the knowledge gained could be pivotal for enhancing your role within your organization, ensuring you are prepared to leverage data-driven insights in decision-making. ### Conclusion By completing **“大數據分析：商業應用與策略管理”**, learners will emerge with a robust understanding of big data's role in various sectors, preparing them for the data-driven world of business. Whether you’re looking to pivot your career towards analytics or deepen your strategic prowess, this course is a worthwhile investment in your professional development. Enroll today on Coursera and start your journey into the fascinating world of big data!

Syllabus

課程簡介

本單元首先由課程引言人引導此門課程學習的脈絡，接著由魏志平老師簡介此課程。課程簡介還有一個特別的部分，是以直播方式與大家互動的。與大數據的午餐約會直播是已在 2018/4/13 ，透過直播的方式，在NTUMOOC Facebook 粉絲團呈現（Facebook 搜尋：NTUTI - 臺大創新教學組（MOOCs/翻轉教育）） ，引入大數據生活應用的議題、談論產學之間的落差，作為此門課程的開頭。2018/4/13 沒有收看到直播的同學也別擔心，直播的影片已剪輯完畢，在此週呈現，學習者依然可以透過 Discussion Forums 向老師提問並與同學互動。

本週老師首先會介紹金融服務的功能，並一一點出這些不同金融服務創新的趨勢，也帶領學生認識金融機構業者有哪些不同的資料庫種類，讓學生瞭解各項金融服務的功能是如何透過數據求新求變。 第二個段落將以保險業為例，帶出決策樹與關聯分析的方法，說明資料分析如何讓我們更加了解客戶，例如發掘客戶類型與購買保單種類之間的關聯性等，進一步預測客戶的行為。 最後一個段落，老師將帶領同學認識一些常見的股市分析方法，例如大家常常聽到的基本面、技術面分析等等。在股市裡，我們可能會想了解發行股票的公司，本單元會介紹我們可以為此取得哪些資料種類，以及這些資料庫有什麼特性。最後，老師將會使用許多案例，較為細膩地為大家講解資料分析在股市的三大應用，包括企業評等、股價預測與趨勢分析。

本週的課程架構與上週類似，老師一樣會先介紹業者掌握的資源，以及善用資料在行銷與零售上能產生的效益，接著講授分析方法。本週的重點是關聯分析，老師會從這種分析方法的概念、指標計算方式等基礎知識開始教起，並以實例讓同學理解關聯分析在業界如何被應用。在認識資料種類、瞭解應用目的與學習分析方法之前，老師也會在課程的開頭介紹STP市場區隔理論，讓同學對大數據的時代來臨之前就已發展出的經典行銷理論有些認識，再進一步去學習資料科學可以如何幫助我們更有效的實踐經典理論。

本週的課程教導大家如何將平常使用社群媒體所產生的各式資料，例如Facebook上的貼文、Instagram裡的追蹤與被追蹤關係等等，做適當的分析，幫助我們在行銷、客服等方面做出更貼近顧客需求的商業決策。

本週所關注的資料來源一樣是社群媒體，與上週不同的是，我們將眼光從社群媒體上複雜的關係（按讚、追蹤等），轉向人們留下的評論（例如食記、開箱文等）以及企業在這些評論中所呈現的品牌印象。首先，老師將介紹情感分析與社群聆聽的概念，並講述情感分析的流程，讓我們學習從評論資料中萃取出重要的產品特徵，並辨別資料中所隱含之消費者對不同特徵的情感態度(正向或負向)。本週的第二個重點是學習以品牌經營者的角度去思考問題，老師會介紹傳統的市場結構分析方法與其限制。接下來，將分享如何利用產品評論來進行市場結構分析，從評論中的產品間比較關係來建構市場競爭的樣貌，並以清楚的步驟幫助同學了解分析流程。最後一個重點是品牌聯想。老師將分別介紹兩種品牌聯想萃取方法，包括傳統利用問卷、以及利用社群媒體資料的萃取方法。在了解如何利用社群媒體資料來進行品牌聯想分析之後，最後老師提出透過社群媒體資料進行行銷智慧分析可能面臨的相關挑戰，等待我們繼續突破。

最後一週的課程，由玉山金控的李正國數位金融長主講，帶領我們用業界的視角，看資料科學的發展是如何刺激金融創新、驅動銀行轉型，當中也舉了許多玉山銀行的實際案例，帶領我們瞭解玉山銀行在現今面臨了哪些挑戰，而業界又如何因應大數據的時代做不同的人才佈局。