Data Science: A New Way of Thinking | 数据科学导论

As a pilot course and cognitive course for data science, this course is dedicated to popularizing the basic knowledge, core concepts and thinking models related to data mining and big data for students through a vivid teaching model, from engineering technology, legal norms, and application practice. Describe the beautiful blueprint of data science from different angles. This course is suitable for college students from various backgrounds who are interested in the fascinating field of data science. Existing online data science courses mainly focus on purely technical content such as learning specific algorithms. In contrast, data science is an application-oriented, highly interdisciplinary field that requires systematic knowledge from multiple domains. In addition to algorithmic learning, students also need to recognize the challenges people may face in the real world and the relationship between data and human society. The purpose of this course is to comprehensively understand the key issues in the big data era, improve data awareness, and help students lay a solid foundation for subsequent data science courses. This is an introductory course suitable for university students with diverse backgrounds interested in getting into the fascinating world of data science. Existing online data science courses mainly focus on learning specific algorithms and other purely technical contents. By contrast, data science is an application-oriented, highly interdisciplinary domain, which requires systematic knowledge from a variety of sources. In addition to algorithm learning, students also need to appreciate the challenges that people may face in the real world as well as the relationship between data and human society. The purpose of this course is to provide a comprehensive understanding of the key issues in the era of big data and promote data awareness to help students lay a solid foundation for subsequent data science courses.

• Basic concepts of data science
• Techniques for data acquisition, storage and transmission
• Data visualization: principles and techniques
• High performance computing
• Data ethics and intellectual property law
• Real-world case studies