数据科学 | Data Science

本课程完整覆盖数据挖掘领域的各项核心技术，包括数据预处理、分类、聚类、回归、关联、推荐、集成学习、进化计算等。强调在知识的广度、深度和趣味性之间寻找最佳平衡点，在生动幽默中讲述数据挖掘的核心思想、关键技术以及一些在其它相关课程和教科书中少有涉及的重要知识点。本课程适合对大数据和数据科学感兴趣的各专业学生以及工程技术人员学习，不追求纯粹的理论推导，而是把理论与实践有机结合，让学生学到活的知识、有用的知识和真正属于自己的知识，特别是数据分析领域的研究方法和思维方式。

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.

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.

本课程将重点讲解高级大数据系统的实现、优化和应用，包括分布式文件系统、MapReduce/Spark、Storm/Spark streaming、Mahout等系统的原理、实现、策略优化。

近年来，人工智能技术正在快速地渗透进各个不同领域。因大数据系统是当今数据驱动人工智能的基础，而变得至关重要。本课程旨在引导学生了解大数据系统的基本概念，包括如何有效地存储、处理和分析数据。课程从分布式系统设计的一般原理出发。之后我们提供了如何在大数据系统中评定存储、计算和网络功能的框架。最后，为了使这些设计原则便于理解，我们的案例研究将使用真实的工业系统来演示基本设计原则如何应用于实际系统，以及该如何分析它们的性能以及局限性。

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.

How to process big data is an ongoing challenge facing machine learning. Currently, the problem of machine learning processing large-scale data is very common. How to propose a machine learning algorithm that meets the needs of big data processing is a hot research topic in the era of big data. The "Big Data Machine Learning" course is a basic theoretical course for senior undergraduates and graduate students in the Department of Information Science. Its purpose is to train students to comprehensively understand the theoretical basis of big data machine learning and firmly master the methods and solutions of big data machine learning. Ability to solve practical problems. This course mainly studies machine learning and deep learning methods, aiming to realize the application of big data machine learning. The main contents of this course include:

1. Basic theory of statistical learning
2. .Basic methods of machine learning
3. Deep learning theories and methods

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:

1. The basic theories of statistical learning
2. The basic methods of machine learning
3. The theories and methods of deep learning

This course aims to provide a set of analytical tools to help students understand and apply the intellectual property legal system. Hotspots and key issues in intellectual property protection in China will be discussed from local and global perspectives.
This course uses case teaching method to provide a broad introduction to intellectual property, explore the content and characteristics of intellectual property, and provide practical training on how to identify, manage, apply and protect intellectual property. This course focuses on patent rights and provides an introduction to trademarks, copyrights, and trade secrets. In order to deepen and broaden students' knowledge and understanding, this course also pays special attention to cutting-edge issues such as the international intellectual property system, genetic exclusivity, patent pools, and technical standards.

This course aims to provide students with a set of analytical tools for understanding and applying the intellectual property legal system. Hot topics and key issues in intellectual property protection in China will be discussed from local and global perspectives.

This course uses case teaching methods to provide a broad introduction to intellectual property, explore its content and characteristics, and provide practical training - teaching how to identify, manage, use and protect intellectual property. This course focuses on patents and also provides an introduction to trademarks, copyrights, and trade secrets. In order to deepen and broaden students' knowledge and understanding, this course also pays special attention to cutting-edge issues such as the international intellectual property system, gene patents, patent pools and technical standards.