因應並針對統計軟體在當今實際產業或是研學單位在時代中的許多推演與程式應用發展，並在大數據時代和數據科學的連結，統計軟體的課程概述在此課程上進一步調整如下:
1. 本課程主要目標在於介紹一些在業界或學術界常用之統計軟體，因應大數據時代統計分析的需求及應用。軟體包跨R、SAS、以及一部分的SQL
2. 我們課程將著重於這些軟體包括Ｒ、SAS。課程主要涵蓋如何利用這些軟體所提供之相關程式功能
(a) 進行資料處理
(b) 軟體提供的重要之統計方法來瞭解資料分析。
(c) 相關數據科學遇到的軟體應用
3. 課程主要進行R和SAS，其中內容上會以軟體基本指令、資料處理、數據方法分析為主軸。
with R: basic commands, programming for data, graphics and visualization, TensorFlow for deep learning
with SAS: basic procedures for data handling and preparation, basic data analysis and modeling
In response to and in response to the development of many deductions and program applications of statistical software in today's actual industries or research institutions, and in connection with data science in the era of big data, the course overview of statistical software has been further adjusted for this course as follows:
1. The main goal of this course is to introduce some statistical software commonly used in industry or academia to meet the needs and applications of statistical analysis in the big data era. Packages span R, SAS, and some SQL
2. Our courses will focus on these software including R and SAS. The course mainly covers how to use the relevant program functions provided by these software
(a) To carry out data processing
(b) The software provides important statistical methods for understanding data analysis.
(c) Software applications related to data science
3. The course mainly covers R and SAS, and the content will focus on basic software instructions, data processing, and data method analysis.
with R: basic commands, programming for data, graphics and visualization, TensorFlow for deep learning
with SAS: basic procedures for data handling and preparation, basic data analysis and modeling
本課程主要目標在於介紹一些在業界或學術界常用之統計軟體，這些軟體包括Splus、SAS、SPSS以及Excel。課程主要涵蓋如何利用這些軟體所提供之相關統計方法來做實際資料分析。
The main goal of this course is to introduce some statistical software commonly used in industry or academia, including Splus, SAS, SPSS and Excel. The course mainly covers how to use the relevant statistical methods provided by these software to conduct actual data analysis.
1. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data 1st Edition
2. R graphics cookbook: Practical Recipes for Visualizing data
3. Deep learning with R
4. Materials on SAS help and support center
5. SAS online documents
1. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data 1st Edition
2. R graphics cookbook: Practical Recipes for Visualizing data
3. Deep learning with R
4. Materials on SAS help and support center
5. SAS online documents
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| Attendance Attendance attendance |
10 | |
| 課堂練習及課後作業課堂練習及課後作業 Class exercises and homework |
30 | |
| Midterm ExamMidterm Exam midterm exam |
30 | |
| Final Exam (含自主學習之加分)Final Exam (含自主學習之加分) Final Exam (including bonus points for independent study) |
30 |
