Data is becoming ubiquitous in the everyday life and greatly affecting the way that we interact with the surrounding world. Data Science, the art of working with data, represents an essential skill for empowering your future career in finance, telecommunication, IT, Internet, AI, consulting, education, transportation, healthcare and so on.
In this concise yet comprehensive program, you will gain a complete picture of data analytics and a good understanding of popular data mining algorithms and systems. You will also have the first-hand experience of implementing and applying these techniques. Most importantly, the program will serve as a good starting point for anyone interested in becoming competitive in the job market and pave a solid road for you to continue your future education.
Tsinghua University is one of the very top universities in China and is also highly ranked in the world. Founded in 2001, its Graduate School at Shenzhen is aimed at high quality research student training by combining the research strength of Tsinghua University and the booming IT & Internet industry in Shenzhen, one of the most innovative cities in China.
Courses under this program: Course 1: Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘：理论与算法
Unraveling the mysteries of Data Mining and Big Data, this course is a must-have for any budding Data Scientist. 最有趣的理论+最有用的算法=不得不学的数据科学。
Course 2: Data Science: A New Way of Thinking | 数据科学导论
Despite the large volume of data mining papers and tutorials available on the web, aspiring data scientists find it surprisingly difficult to locate an overview that blends clarity, technical depth and breadth with enough amusement to make big data analytics engaging. This course does just that.
Each module starts with an interesting real-world example that gives rise to the specific research question of interest.
Students are then presented with a general idea of how to tackle this problem along with some intuitive and straightforward approaches.
Finally, a number of representative algorithms are introduced along with concrete examples that show how they function in practice.
While theoretical analysis sometimes overcomplicates things for students, here it’s applied to help them better understand the key features of the techniques.
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.
Recent years have witnessed the rapid increase of the penetration of AI technology into different areas in the industry. Big data systems, the foundation that enables today’s data-driven AI, are thus becoming critically important. This course is dedicated to lead students into the basic concepts of big data systems, covering how data is effectively stored, processed and analyzed. We start from the general principles in the design of distributed systems; then we provide frameworks on how storage, computation, and network capabilities are scaled in big data systems; finally, to make such design principles easy to follow, our case studies use real industrial systems to demonstrate how the basic design principles are applied in real-world systems as well as how their performance and limitation are analyzed.
An ongoing challenge for machine learning is how to deal with big data. At present, the problem of machine learning dealing with large-scale data is widespread. How to propose a machine learning algorithm to meet the needs of big data processing is a hot research topic in the big data era. The course " Big Data Machine Learning" is a basic theory course for senior undergraduates and postgraduates in information science department. Its purpose is to cultivate students' comprehensive ability to understand the theoretical basis of Big Data Machine Learning, master the methods of Big Data Machine Learning firmly, and solve practical problems. This course focuses on the methodsof machine learning and deep learning, and aims to realize the application of big data machine learning. The main contents of the course include:
This course is intended to provide a set of analytical tools for the students to understand and apply intellectual property legal system. Hot topics and crucial issues of intellectual property protection in China will be discussed from local and global perspectives.
Using case method, this course offers a broad introduction of intellectual property, explores its content and features, provides practical training — teaches how to identify, manage, use and protect intellectual property rights. This course focuses on patent, also introduces trademark, copyright and trade secret. In order to deepen and broaden students’ knowledge and understanding, this course also pays particular attention to cutting-edge issues, such as international intellectual property system, gene patent, patent pool and technical standards, etc.
This course is suitable for university students of all majors and practitioners in various disciplines who are interested in visually exploring and understanding the data of interest. Data visualization is an interdisciplinary field about the visual representation of data and information, aiming to communicate messages clearly and effectively using principled graphical means. Instead of solely pursuing theoretical knowledge and abstract concepts, it seamlessly connects theory with practice to enable students to learn useful techniques about data visualization through a series of well-designed case studies. It systematically covers the fundamental knowledge of visualization as well as the history and the state of the art of visualization. By completing this course, students will appreciate both the beauty and power of data visualization and have rich hands-on experiences on implementing popular visualization techniques.