本課程首先介紹大數據(主要為社群媒體)的內涵與性質。其次，課程討論社群媒體分析中最重要的部分—情緒分析。課程接著以實作方式介紹大數據分析的各個步驟，從資料篩檢、處理、整併到預測。最後，本課程以討論大數據分析目前應用的限制及未來方向總結。
This course begins with the introduction of concepts and texture of big data, and specifically, social media. Then, it discusses the critical part of doing social listening, sentiment analysis. It continues to illustrate the big data analysis step by step, from data verification to predication. Finally, we conclude the course with the discussion of the current limits of big data analytics in social sciences and the path should be paved in the future.

三、每週進度及教學內容簡述
01. 09/12 – introduction（概論）
[No. 1]
02. 09/19 –World Congress of Girl Scouts and Guiding (NO CLASS)
03. 09/26 – big data types and generation（大數據種類與原理）
[No. 2] [No. 3]
{case study: location based data}
{case study: consumer purchase data}
04. 10/03 – social media as the opinion platform（社群媒體作為觀測工具）
[No. 4] [No. 5] [No. 6] [No. 7]
{case study: Facebook}
{case study: twitter}
05. 10/10 – Double Tenth Day (NO CLASS)
06. 10/17 – sentiment analysis（情緒分析 I）
[No. 8] [No. 9] [No. 10]
07. 10/24 – two-dimensional sentiment analysis II（二維情緒分析暨實作）
[No. 11] [No. 12]
{case study: CVAW}
08. 10/31 – aggregate anonymous data and privacy protection（去識別化資料與隱私保護）
{case study: facebook topic data}
{case study: google trend}
09. 11/07 -- data filtering and accuracy verification（資料篩檢與確認）
{case study: OTT}
{case study: 桃機淹水}
10. 11/14 -- data processing and integration（資料處理與來源整併）
{case study: VoIP}
{case study: game industry}
11. 11/21 – Midterm Assignment Due (NO CLASS)
12. 11/28 – modeling and predication（模型與預測）
[No. 13] [No. 14] [No. 15]
13. 12/05 -- campaign predication and practice（選舉預測暨實作）
[No. 16] [No. 17]
{case study: the 2016 general election}
{case study: KMT chairman election 2017}
14. 12/12 – cacophony: algorithms and keyword setting（影響大數據分析效度的原因）
[No. 18] [No. 19]
{case study: machine interactions}
{case study: fake sources}
15. 12/19 – the up-to-date domestic research （國內政治學界研究現況）
[No. 20]