教學大綱 Syllabus

週次

授課內容

學生指定閱讀資料

授課方式

1

Course overview

Learning objectives: Introduce students to the interactive JupyterLab environment. Overview of Python applications in business analytics. Installation of the Anaconda distribution.

Course design: Presentation slides, news articles, and computer demonstrations.

Classroom session to introduce the topics, assessments, and class delivery options.

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2

Basic data types

Learning objectives: Introduce built-in data types including str, int, float, bool, NoneType and etc. Illustrate basic math operations and text manipulations.

Course design: Computer demonstrations.

Assessment: Homework 1

ABSP Ch1, JupyterLab

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3

Flow control I

Learning objectives: Explain the concepts of comparison operators and True-False tables.

Course design: Flow charts and computer demonstrations.

Assessment: Homework 2

ABSP Ch2, JupyterLab

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4

Flow control II

Learning objectives: Automate processes with while- and for-loop.

Course design: Flow charts and computer demonstrations.

ABSP Ch2, JupyterLab

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5

Functions and methods

Learning objectives: Write functions to reuse code. Discuss concepts in recursive functions.

Course design: Computer demonstrations and applications of recursion.

Assessment: Homework 3

ABSP Ch3, JupyterLab

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6

Basic data structures I

Learning objectives: Introduce the list, indexing, and list methods.

Course design: Computer demonstrations and apply flow controls to list.

ABSP Ch4, JupyterLab

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7

Basic data structures II

Learning objectives: Introduce dict and tuple; keys and values; mutable and immutable data structures.

Course design: Computer demonstrations and applications of data structures.

Assessment: Homework 4

ABSP Ch5, JupyterLab

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8

NumPy and Pandas I

Learning objectives: Numerical computations in Python, arrays, and matrix operations.

Course design: Computer demonstrations and applications of NumPy.

PDA Ch4,5, JupyterLab

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9

NumPy and Pandas II

Learning objectives: Pandas dataframe; attributes and methods of dataframe. Data import and cleaning.

Course design: Computer demonstrations and data analysis with NumPy and Pandas.

Assessment: Homework 5

PDA Ch6,7,8, JupyterLab

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10

Web-scraping

Learning objectives: Automate data gathering from websites;  Application Programming Interface (API).

Course design: Computer demonstrations and applications of API with economics and finance data.

ABSP Ch12, JupyterLab

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11

Brainstorming session for project

Learning objectives: Encourage innovation through a capstone project. Develop a roadmap for doing data analytics with business applications. Discuss the feasibility of students’ ideas. Q&A on technical issues.

Course design: Group discussions and in-class presentations.

Classroom session to discuss with students on potential topics for the semester project.

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12

Data visualization

Learning objectives: Visualize data with matplotlib and seaborn. Compare and constrast data visualization tools.

Course design: Computer demonstrations and interactive plotting with real-world data.

Assessment: Homework 6

PDA Ch9, JupyterLab

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13

Manipulating strings

Learning objectives: Methods and functions for manipulating strings. Import and export string data.

Course design: Computer demonstrations.

ABSP Ch6, JupyterLab

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14

Textual analysis

Learning objectives: Dictionary approach for counting words; word clouds; document-term matrix; text-mining.

Course design: Computer demonstrations with applications of English and Chinese corpora.

Lecture notes and JupyterLab

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15

Project presentations

Learning objectives: Students present their projects on business analytics with Python

Course design: Group discussions and in-class presentations.

Classroom session

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16

Project presentations

Learning objectives: Students present their projects on business analytics with Python

Course design: Group discussions and in-class presentations.

Classroom session

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17 (17+1)

NumPy, SciPy, and Statsmodels

Learning objectives: Scientifical computing and statistical modeling with Python.

Course design: Computer demonstrations with applications of econometrics and optimizations.

Assessment: Homework 7

Lecture notes and JupyterLab

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18

Final online examination

Exam design: Examinations with different question types including multiple choices, short computations, data gathering, data analysis, debugging, and code writing problems.

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