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)

DA Capstone Projects/ Specialized Research

Introduction to AI

In recent years, artificial intelligence (AI) has been classified as one of the most important transformational technologies to improve social life and address organizations’ problems. AI’s practical application is profoundly versatile and has the unique ability to offer convenience and efficiency. However, the proliferated application of AI has also raised some skepticism since the impacts of AI involve not only institutions that maintain societal operations, but also the way in which we confront social problems.

Data Science

Artificial Intelligence (AI) refers to the intelligence demonstrated by machines, in contrast to the natural intelligence displayed by animals, including humans. Initially, computers were primarily used for numerical calculations, leading to the development of applications that supported routine tasks, such as retrieving news articles from the internet. However, achieving AI requires a substantial amount of data and precise handling of various details and issues. A notable project in the field of AI is ChatGPT, where the objective is to develop an advanced language model capable of generating human-like text. Through extensive training on diverse datasets, ChatGPT leverages deep learning techniques to comprehend context and produce coherent responses, making it a powerful tool for natural language processing tasks. In GPT series, GPT-3 is trained on a massive dataset of text and code, including text from the internet, books, code repositories, and other sources. The exact composition of the dataset is not publicly known, but it is estimated to be over 500 gigabytes in size.

The course covers various topics in data science. It includes an introduction to data, computer vision (CV) concepts such as semantic segmentation, image classification, and object detection. Additionally, it covers natural language processing (NLP) areas like language modeling, question answering, machine translation, sentiment analysis, and text generation. The course also delves into time series analysis, covering anomaly detection and time series forecasting, as well as speech-related topics like speech recognition and speech synthesis.

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 for academic studies, business analysis, and data journalism.

Design Thinking

Innovation and human centered design are the driving force behind the mindsets that communities and employers are looking for today and into the future.  Adopting a human centered design approach presents opportunities to solve novel problems, start a business, and/or uncover new questions to explore. The process encourages students to take risks, confront failure, and build up resilience in the face of persistent challenges. In this course, students will learn idea creation techniques to examine, change, and address a social issue.  Students will apply creative skills more effectively through journaling, in-depth research, peer feedback, storytelling, and a semester long group project that develops a solution for a pressing social problem in or around the National Chengchi University community. The course requires meaningful cooperation with peers, willingness to provide candid feedback to others, and humility to receive critique from others.

Misinformation Governance and Democracy

Since the 2016 Brexit referendum and the 2016 US presidential election, the spread of misinformation has generated considerable concern. The rise in popularity of misinformation and conspiracy theories affects citizen’s cognition and consensus building, thereby threatening and often impairing the functioning of a democratic society. Thus, it has become crucial to address the negative impacts of misinformation. However, since misinformation is related to interdisciplinary fields, it is rather challenging to coordinate the expertise of relevant professionals to abate the prevalence of misinformation.

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