大數據與商業分析 Big Data and Business Analytics

Presentation on theme: "大數據與商業分析 Big Data and Business Analytics"— Presentation transcript:

1 大數據與商業分析 Big Data and Business Analytics
楊立偉教授 台灣大學管理學院 2017

2 課程介紹 選課介紹

3 課程資料 開課系所: 工管系、資管系 開課性質: U 字頭，3 學分選修課程 開課日期: 105 學年度第 2 學期 上課時間: 週三 09:10~12:10 上課教室: 管一 103 教室 開授對象: 本校管理學院大學部高年級與碩士班學生 工管系25人、資管系25人、管院三系20人， 總名額70 (未滿則開放外系)；旁聽不拘

4 楊立偉教授 研究領域為資料庫及語意分析技術、知識管理、數位行銷 現任 台大工管系暨商研所兼任助理教授 2006~
台大資管系兼任助理教授 2017~ 資訊及通信國家標準技術委員 意藍資訊　　　董事總經理（創辦人） 1999~ 　　　　　　　國內規模最大的網路情報與社群口碑自動分析平台　　　　　　　 龍捲風科技　　董事總經理 　　　　　　　國內企業搜尋引擎市佔率最高；國際檢索競賽第一名 經歷 智威湯遜數位行銷首席顧問、尚藍互動行銷共同創辦人 2009年獲選100 MVP最有價值經理人，擁有超過20項語意分析專利 2012年獲國家雲端創新獎、數位時代「創業之星」首獎

5 課程介紹 「大數據」（Big Data）自 2011 年起逐漸進入商業大眾的視野，相關書籍與論述如雨後春筍般出現，各行各業也都對此一名詞充滿期待。現實商業情境中，數據分析是圍繞「大數據」各種想像的基礎條件。著眼於培養企業需求日殷的數據分析人才，此一跨系課程針對台大管理學院的同學而設，透過講演與專案，概念與實作並重，有系統介紹數據分析的商管相關應用，為有志於成為商業應用領域數據科學家的不同背景修課同學，奠定未來發展的基礎。

6 Source: 大數據玩行銷 Big Data Marketing

7 Big Data Analytics Introduction
Data that exist in very large volumes and many different varieties (data types) and that need to be processed at a very high velocity (speed). Analytics Systematic analysis and interpretation of data—typically using mathematical, statistical, and computational tools—to improve our understanding of a real-world domain. This chapter is primarily about these two new advances in data technologies and approaches. Traditional data management technologies were created to ensure accurate and efficient transaction processing. As we saw from chapter 9, later database structures were created to support decision-making and overall understanding of the business. We called these data warehouses. Big data and analytics takes us further down this road.

8 Figure 11-11 Generations of Business Intelligence and Analytics
Adapted from Chen et al., 2012 BI&A 1.0 Focus on structured quantitative data largely from relational databases BI&A 2.0 Include data from the Web (web interaction logs, customer reviews, social media) BI&A 2.0 Include data from mobile devices, (location, sensors, etc.) as well as Internet of Things BI&A has evolved along with other elements of information technology. It should be noted that there is far more unstructured and semistructured data (characteristic of Web and mobile technology) than there is structured data (typically found in relational databases). And although all data (structured and unstructured alike) is increasing in volume over time, the rate of growth is largest in the unstructured space.

9 Types of Analytics Descriptive analytics – describes the past status of the domain of interest using a variety of tools through techniques such as reporting, data visualization, dashboards, and scorecards Predictive analytics – applies statistical and computational methods and models to data regarding past and current events to predict what might happen in the future Prescriptive analytics –uses results of predictive analytics along with optimization and simulation tools to recommend actions that will lead to a desired outcome With descriptive analytics we ask what happened already (last week, last year, etc.). With predictive analytics we ask what’s going to happen in the future and how will it affect us. With prescriptive analytics, we ask what is the best decision to make.

10 課程內容 請參考課程網頁 http://homepage.ntu.edu.tw/~wyang/bda2017 文字及社群數據分析
財經數據分析 網站及電商數據分析 其他 (客服、零售、廣告等)

11 選課介紹 登記並分發序號 工管系 25人(超過則抽籤) 資管系 25人(超過則抽籤) 管院三系 20人(超過則抽籤)
工管系 25人(超過則抽籤) 資管系 25人(超過則抽籤) 管院三系 20人(超過則抽籤) 若名額有剩，開放外系抽籤 發加選授權碼

12 其他說明 3 月 1 日上課前，選修同學以 6~7 人為一組，自行分組完畢，並選任組長，將分組名單送交課程助教 (成員不得全為同系所)；分組報告均含互評成績。 選修同學需具高度興趣並投入時間、心力完成各項要求。 不需程式撰寫經驗；惟無相關經驗者若欲選修，應有隨課程進展自習相關工具及技術的決心。 採用R、Python、Java、SAS、Weka 配合資料庫均可

13 問題討論