「巨量資料/大數據(Big Data)」在我們的生活裡已經掀起滔天巨浪，繼雲端運算(Cloud Computing) 之後，儼然成為學術界跟科技業中最熱門的潮字，似乎每家公司都在進行有關的研究，三句不離大數 據。巨量資料時代，統計與資料分析是根本中的根本。數據專家(Data Scientist)或量化分析師(Quantitative Analyst)的專業包含了統計學、電腦科學和數學，過去這些人才都搶著要進華爾街工 作，但多虧了 Big Data 帶來的風潮，現在各行各業都在尋找擁有量化分析、統計學背景的工程師、數據專家。本課程將以實際體驗Hadoop多台主機的分散式叢集架構，做到HDFS分散式儲存和MapReduce的叢集運算，達到BigData的處理與分析。學習Hadoop儲存系統與資源管理框架及Spark In-Memory巨量資料相關關鍵技術。資料分析軟體及程式語言-R語言做為進入巨量資料分 析的初階基本課程，相信要進大數據一行不成問題。 "Big Data" has caused huge waves in our lives. After Cloud Computing, it has become the hottest trend in the academic and technological industry. It seems that every company is conducting relevant research, and three sentences are not inferior to the large number. In the era of huge data, statistics and data analysis are the fundamentals. Data Scientist or Quantitative Analyst's profession includes statistics, computer science and mathematics. In the past, these talents have been dying to work in the Huaer Street, but more of the trend brought by Big Data are being found. Now all walks of life are looking for engineers and data experts with backgrounds in quantitative analysis and statistical analysis. This course will actually experience the distributed assembly architecture of Hadoop multiple hosts, achieve HDFS distributed storage and MapReduce collection computing, and achieve BigData processing and analysis. Learn Hadoop storage system, resource management framework and Spark In-Memory's huge data-related key technologies. Data Analysis Software and Program Language - R Language is the basic course of entering a huge amount of data analysis. I believe that it will be impossible to enter a large number of data analysis.
書名 R 軟體資料分析基礎與應用 出版社 旗 標 科 技 股 份 有 限 公 司
作者 鍾振蔚譯 出版年 2015
Book Name R Software Data Analysis Basics and Applications Publishing House Flags Technology Co., Ltd.
Author: Long Zhenwei, Publishing Year 2015
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 作業與出席作業與出席 Work and attendance |
40 | |
| 期中考期中考 Midterm exam |
30 | |
| 期末考與期末分組專題期末考與期末分組專題 Final exam and final division topics |
30 |
