Type of Credit: Elective
Credit(s)
Number of Students
電腦教室位置有限，第一週會抽加簽，但名額不會太多。
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本課程將介紹如何使用R進行數據分析和視覺化計算。在最後的期末報告中，將運用學到的技能來分析實際商業數據，並下正確的策略與建議。
*此堂課只教R。上課前，請先裝好最新版的R及RStudio。
*必需具備基礎統計知識，如迴歸、機率、分配等，並對程式撰寫充滿興趣。
能力項目說明
Topics include data management, exploring and visualizing data, hypothesis testing, confidence intervals, counts and tables, analysis of variance, regression, principal components, and machine learning.
Upon completion of this course, students should be able to think critically about data and apply standard statistical inference procedures to draw conclusions from such analyses.
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週次 |
課程主題 |
課程內容與 指定閱讀 |
教學活動與作業 |
學生學習投入時間 |
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課堂講授 |
課程前後 |
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1. Feb 17 |
Introduction |
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2. Feb 24 |
Introduction of Business Analytics and R |
RMarkdown. Basic Math operations. Advanced Data Structures. tidyverse套件 “Hello World” |
HW1 |
3 |
2 |
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3. Mar 3 |
Data Analysis Data Visualization |
Statistical Measures. Probability Distribution and its types. EDA, ggplot2. |
HW1 |
3 |
2 |
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4. Mar 10 |
R Programming |
Loop and Control Statements. |
HW2 |
3 |
2 |
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5. Mar 17 |
R Programming | Built-in Function. | HW2 |
3 |
2 |
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6. Mar 24 |
Regression |
Regression and its application. |
HW3 |
3 |
2 |
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7. Mar 31 |
Regression |
GLM and its application. |
HW3 |
3 |
2 |
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8. Apr 7 |
Sampling |
Bootstrapping, CV. |
HW3 |
3 |
2 |
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9. Apr 14 |
Machine Learning |
Unsupervised Learning and its application. |
HW4 |
3 |
2 |
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10. Apr 21 |
Machine Learning |
Supervised Learning and its application. |
HW4 |
3 |
2 |
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11. Apr 28 |
Dimension Reduction |
PCA. |
HW5 |
3 |
2 |
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12. May 5 |
Text Mining |
Web Crawler, word cloud. |
HW5 |
3 |
2 |
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13. May 12 |
Network Analysis | SNA | HW5 | ||
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14. May 19 |
校慶放假 | ||||
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15. May 26 |
A/B test |
A/B test |
HW5 |
3 |
2 |
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16. June 2 |
期末考試 |
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17. June 9 |
期末考試 |
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＊每週進度，依學生學習狀況會再調整。
作業 60% (每個作業繳交期限為2~3個禮拜後)
期末考試 40%
Lecture notes.
參考書目：
R for Data Science http://r4ds.had.co.nz/
R Programming for Data Science https://bookdown.org/rdpeng/rprogdatascience/
R for Business Analytics
https://www.springer.com/us/book/9781461443421
An Introduction to Statistical Learning with Applications in R
http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf
http://moodle.nccu.edu.tw/