Type of Credit: Elective
Credit(s)
Number of Students
In this course, students will gain practical experience in the field of data science with a focus on its applications in the business world. The class begins with an introduction to Python programming and covers essential Python libraries for data science, such as Pandas, NumPy, and Matplotlib. Additionally, students will learn about Scikit-learn, the most commonly used Python library for machine learning. This course utilizes a two-stage training approach to develop students' expertise in utilizing Python for data pre-processing, data management, and data analytics, as well as implementing machine learning algorithms to solve real-world business problems. The aim is to enhance students' comprehension of data science's practical applications in a business environment, ultimately improving their understanding of how data science can be applied to real-world situations.
能力項目說明
By enrolling in this course, students will thoroughly comprehend diverse data science techniques and popular machine learning algorithms. Equipped with these abilities, students will be capable of adeptly addressing genuine business obstacles and honing their expertise in data science and business analytics.
| 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 |
|---|---|---|
|
週次 Week |
課程主題 Topic |
課程內容與指定閱讀 Content and Reading Assignment |
教學活動與作業 Teaching Activities and Homework |
學習投入時間 Student workload expectation |
|
|
課堂講授 In-class Hours |
課程前後 Outside-of-class Hours |
||||
|
1 |
Introduction |
Introduction to Data Science and Anaconda Setup |
|
3 |
3 |
|
2 |
Data Analytics |
Pandas: Data Pre-processing |
|
3 |
3 |
|
3 |
Data Analytics |
Pandas: Data Pre-processing |
Quiz1 |
3 |
3 |
|
4 |
Data Analytics |
Pandas: Data Analytics |
Quiz2 |
3 |
3 |
|
5 |
|
National Holiday |
|
|
|
|
6 |
Data Analytics |
Pandas: Data Analytics |
Quiz3 |
3 |
3 |
|
7 |
Data Analytics |
Numpy: ndarray |
Quiz4 |
3 |
3 |
|
8 |
Data Analytics |
Numpy: ndarray |
Quiz5 |
3 |
3 |
|
9 |
Exam |
Midterm Exam |
|
|
|
|
10 |
Data Visualization |
Pandas, Matplotlib, & Other API: Data Visualization |
|
3 |
3 |
|
11 |
Machine Learning |
Machine Learning: Regression |
Quiz6 |
3 |
3 |
|
12 |
Machine Learning |
Machine Learning: Classification |
Quiz7 |
3 |
3 |
|
13 |
Machine Learning |
Machine Learning: Evaluation Metrics |
Quiz8 |
3 |
3 |
|
14 |
Machine Learning |
Machine Learning: Model Selection |
Quiz9 |
3 |
3 |
|
15 |
Exam Review |
Final Exam Review |
Quiz10 |
3 |
3 |
|
16 |
Exam |
Final Exam |
|
|
|
|
15 |
Group Project |
Final Project |
|
|
|
|
16 |
Group Project |
Final Project |
|
|
|
10 Quizzes 40%
Midterm Exam 20%
Final Exam 20%
Final Project 20%
Required
The documentation for the latest version of Scikit-learn
https://scikit-learn.org/stable/user_guide.html
Recommended
1. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.
2. Müller, A., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, Inc. ISBN: 9781449369415
3. Géron, A., (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O'Reilly Media, Inc. ISBN: 9781492032649