Data Analytics (DA)

1. Introduction

The development of science and technology has led to phenomenal changes in global politics, economy, and society. Moreover, the variety of talents needed for contemporary science and technology has also reflected a state of highly dramatic change. Thus, the “Data Analytics (DA)” track features a rolling design methodology to train talents who can meet the needs of contemporary technological development and social applications, which further promote technological and social innovation.

In recent years, with the rapid accumulation of data, the appropriate analysis of such data has become a crucial driver in promoting technological, corporate, and social innovation. Moreover, data sciences connect the web of knowledge across a substantial number of disciplines that create a real-time collective portrait of the world containing a vast array of diversities and individual characteristics and ideologies. Thus, the track curriculum of DA serves as a gateway to advanced data analysis courses, complementing the foundational knowledge of other tracking modules for application and training. Our primary goal is to cultivate professionals in using Data Analytics & Artificial Intelligence with a focus on social analysis and organizational management for innovative developments in the industry, which also involve critical thinking in data sciences.

 

2. The Design of Courses

DA combines basic, advanced, interdisciplinary and practical courses. Freshmen and sophomores will be provided with basic data science programming courses such as Introduction for AI and Data Science. These courses are project-oriented designs. Juniors and seniors will be offered advanced courses which include the following: International Innovation Management; Machine Learning & Deep Learning; Business Data Analytics; Sustainable Development and Data Analytics; AI and Ethics; AI and Governance; Innovative Information and Data Project Design; and Database Design and Management.DA also provides internship opportunities and a capstone course to train students to further develop their collaboration and professional skills.

Year 1-1 (CC) Economics I

(CC) Statistics I: R
Year 1-2 (CC) Computational Programming I: Python

(CC) Statistics II: R

(CC) Economics II
Year 2-1 (RE) Introduction to AI
Year 2-2 (RE) Data Science: R and Python

(IO) Data Visualization: Power BI and R
Year 3 (IO) International Innovation Management

(RE) Machine Learning and AI: Python

(IO) Innovative Information and Data Project Design

(IO) AI and Governance

(IO) Database Design and Management: MySQL
Year 4 (IO) Business Data Analytics: R and Python

(IO) AI and Ethic

(IO) Sustainable Development and Data Analytics: R

(IO) Deep Learning

Core Curriculum (CC), Required Electives (RE), and Issue Oriented (IO)

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DA Capstone Projects/ Specialized Research

Business Data Analytics

This course is an introduction and practice for data analysis for social analysis. In recent years, the application of Big Data has become an important trend in almost every field. This course employs a project-driven strategy that students are able to follow the instructors’ steps about how a data project is developed and how to use R programming to finish a project.

There are three sections in this course. The first is the statistics section which focuses on the basic statistical knowledge for data analysis. The second section is to use Kaohsiung city pollution data project to teach R programming’s basic coding skills in 5 weeks. The third section will introduce an advanced data project every week. You are required to develop a research project with your teammates by end of the course. Lastly, this course is designed to provide you with the basic ability to become a member of a professional data analysis team.

Innovative Technology and Applications

Technology Transfer and Commercialization

Innovation and Management of Intellectual Property Rights

Computer Programming (I)

This course is an introductory level of Python programming language. We start this course by introducing Google Colaboratory, a platform which runs on the cloud and offers free computing resources, will be introduced as your code playground in this course. Then, basic Python syntaxes will be introduced. To provide a better understanding, some examples or assignments will be given. Students need to find an issue to address and solve it using Python as your term-project. A final report about this issue and how you solve it should be submitted at the end of the semester.

Big Data for Social Analysis

This course is an introduction and practice for data analysis for social analysis. In recent years, the application of Big Data has become an important trend in almost every field. This course employs a project-driven strategy that students are able to follow the instructors’ steps about how a data project is developed and how to use R programming to finish a project.

There are three sections in this course. The first is the statistics section which focuses on the basic statistical knowledge for data analysis. The second section is to use Kaohsiung city pollution data project to teach R programming’s basic coding skills in 5 weeks. The third section will introduce an advanced data project every week. You are required to develop a research project with your teammates by end of the course. Lastly, this course is designed to provide you with the basic ability to become a member of a professional data analysis team.

AI & Ethics

Artificial intelligence (AI) is developing at an rapid pace. We should expect to see significant changes in our daily lives. This course will cover legal and philosophical issues raised by AI systems. Questions we consider include:

  1. Who is responsible when AI causes suffering?
  2. Do we have to give up privacy to get technological innovation?
  3. How can bias in algorithms be prevented?
  4. What can we do to make AI more ethical?
  5. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers?
  6. Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few?
  7. How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend?
  8. What, if any, legal rights and responsibilities should we grant them?

Should we regard them merely as sophisticated tools or as a newly emerging form of life?

AI & Governance

Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems.

The prospect of “turning over the keys” to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life?

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