教學大綱 Syllabus

科目名稱：程式設計與統計軟體實務

Course Name: Programming and Statistical Software Applications
  • 學年學期：112-1 Fall Semester, 2023
  • 科目代碼：304838001 Course No.304838001

修別：選

Type of Credit: Elective

1.0

學分數

Credit(s)

70

預收人數

Number of Students

課程資料Course Details
  • 開課單位：統計二 Course Department:Statistics/B/2
  • 授課老師：張志浩 Instructor: CHANG CHIH-HAO
  • 先修科目：無Prerequisite(N/A)
  • 上課時間：四4 Session: thu11-12

課程簡介Course Description

This course applies the most popular languages in data science: R and Python to run simulations and analyze real data.

核心能力分析圖 Core Competence Analysis Chart

能力項目說明

    課程目標與學習成效Course Objectives & Learning Outcomes

    The students are expected to run simulations, manipulate data, perform statistical analysis, and create visualizations in R and Python.

    每周課程進度與作業要求 Course Schedule & Requirements

    教學週次Course Week 彈性補充教學週次Flexible Supplemental Instruction Week 彈性補充教學類別Flexible Supplemental Instruction Type

     

    Week

    Topic

    Content and Reading Assignment

    Teaching Activities and Homework

    1

    Advanced Topics in introducing R

    Help students to practice functions and packages in R

    PowerPoint with case studies and assign homework to using basic mathematical R functions

    2

    Advanced Topics in Control Flow and Loops of R

    Help students to practice functions and packages in R

    PowerPoint with case studies and assign homework to writing self-defined R functions

    3

    Advanced Topics in R functions

    Help students to practice functions and packages in R

    PowerPoint with case studies and assign homework to using basic statistical R functions

    4

    Advanced Topics in R packages

    Help students to practice functions and packages in R

    PowerPoint with case studies and assign homework to using advanced statistical R functions

    5

    Advanced Topics in Data Manipulation  of R

    Help students to practice functions and packages in R

    PowerPoint with case studies and assign homework to load/save data files for basic analysis in R

    6

    Advanced Topics in Data Visualization of R

    Help students to practice functions and packages in R

    PowerPoint with case studies and assign homework with more plot functions for basic data analysis in R

    7

    Advanced Topics in basic Statistical Analysis of R

    Help students to practice functions and packages in R

    PowerPoint with case studies and assign homework of advanced statistical analysis in R

    8

    Advanced Topics in advanced Statistical Analysis of R

    Help students to practice functions and packages in R

    PowerPoint with case studies and assign homework of more advanced statistical analysis in R

    9

    Midterm Exam （期中考）

    Midterm Exam

    Midterm Exam

    10

    Advanced Topics in introducing Python

    Help students to practice functions and modules in Python

    PowerPoint with case studies and assign homework to using basic mathematical Python functions

    11

    Advanced Topics in Control Flow and Loops of Python

    Help students to practice functions and modules in Python

    PowerPoint with case studies and assign homework to writing self-defined Python functions

    12

    Advanced Topics in Python Functions

    Help students to practice functions and modules in Python

    PowerPoint with case studies and assign homework to using basic machine learning Python functions

    13

    Advanced Topics in Python modules

    Help students to practice functions and modules in Python

    PowerPoint with case studies and assign homework to using advanced machine learning Python functions

    14

    Advanced Topics in Files and Exceptions of Python

    Help students to practice functions and modules in Python

    PowerPoint with case studies and assign homework to load/save data files for basic analysis in Python

    15

    Advanced Topics in  Object-Oriented Programming of Python

    Help students to practice functions and modules in Python

    PowerPoint with case studies and assign homework of advanced statistical analysis in Python

    16

    Final Exam（期末考）

    Final Exam

    Final Exam

    17

    Review of R（Python回顧）

    A comprehensive review of R by students

    Review

    18

    Review of Python-III（Python回顧）

    A comprehensive review of Python by students

    Review

    授課方式Teaching Approach

    70%

    講述 Lecture

    30%

    討論 Discussion

    0%

    小組活動 Group activity

    0%

    數位學習 E-learning

    0%

    其他： Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    1. Midterm Exam(期中考)：30%
    2. Final Exam(期末考)：30%
    3. Regular homework assignments(平常作業)：40%

    指定/參考書目Textbook & References

    1."R for Data Science" by Hadley Wickham and Garrett Grolemund.

    2."Python for Data Analysis" by Wes McKinney.

    已申請之圖書館指定參考書目 圖書館指定參考書查詢 |相關處理要點

    維護智慧財產權，務必使用正版書籍。 Respect Copyright.

    課程相關連結Course Related Links

    URL of R reference book: https://r4ds.hadley.nz/
URL of Python reference book: https://wesmckinney.com/book/

    課程附件Course Attachments

    課程進行中，使用智慧型手機、平板等隨身設備 To Use Smart Devices During the Class

    是 Yes

    列印