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Bo Yuan

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。

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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.

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What's inside

Learning objectives

  • Basic data science concepts
  • Typical data mining techniques
  • Applications for data mining
  • A taste of research in data mining
  • Funny stories about data science

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Emphasizes core concepts of data mining, including data preprocessing, classification, clustering, regression, association, and recommendation
Covers both theoretical foundations and practical applications of data mining techniques
Suitable for students and professionals with an interest in data science and big data analytics
Provides insights into data analysis research methods and thinking
Introduces real-world examples to illustrate the practical applications of data mining
Injects humor and engaging content to make learning data science enjoyable

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法 with these activities:
阅读数据挖掘经典著作
通过阅读经典著作,深入理解数据挖掘的基础原理和应用。
Show steps
  • 获取书籍并阅读相关章节
  • 标记重点内容并记下笔记
  • 完成书中练习题或案例分析
Show all one activities

Career center

Learners who complete Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法 will develop knowledge and skills that may be useful to these careers:
Data Scientist
Take a comprehensive look into data mining with this course, Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法. In this course, you can explore the field of data mining and learn about data pre-processing, classification, clustering, regression, association, recommendation, ensemble learning, evolutionary computation and more.
Data Analyst
Begin building a foundation in data analysis with this course, Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法. This course will allow you to learn about key concepts in data science and typical data mining techniques.
Machine Learning Engineer
Learn more about the theories and algorithms used in machine learning with Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法. This course will help you develop a foundation in data mining and machine learning.
Data Engineer
Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法 will help build a foundation for data mining for those interested in becoming a Data Engineer. Learn about data pre-processing, classification, clustering, regression, association, recommendation, ensemble learning, evolutionary computation and more.
Software Engineer
Expand your knowledge of data mining with this course, Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法. Software Engineers can utilize this course to learn about data pre-processing, classification, clustering, regression, association, recommendation, ensemble learning, evolutionary computation and more.
Quantitative Analyst
Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法 is a great place to start learning about data mining and machine learning for those who aspire to become Quantitative Analysts.
Business Analyst
This course, Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法, will give Business Analysts a solid foundation to build upon and expand their knowledge of data mining.
Operations Research Analyst
For those who are interested in a career as an Operations Research Analyst, Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法 covers a range of topics including data pre-processing, classification, clustering, regression, association, recommendation, ensemble learning, evolutionary computation.
Statistician
Statisticians may find the course, Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法, useful as it covers data pre-processing, classification, clustering, regression, association, recommendation, ensemble learning, evolutionary computation and more.
Actuary
Actuaries who take the course Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法 can learn about data pre-processing, classification, clustering, regression, association, recommendation, ensemble learning and evolutionary computation.
Financial Analyst
Lay a foundation for data mining with this course, Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法. Financial Analysts who take the course will learn fundamentals of data mining and machine learning.
Marketing Manager
Marketing Managers can use this course, Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法, to learn about the role of data mining in marketing and build a foundation in the field.
Product Manager
Learn about data mining and its applications in product development with this course, Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法. Product Managers can gain insight into how data mining can help them make better decisions.
Consultant
Gain a comprehensive understanding of data mining techniques with Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法. Consultants can use this foundational knowledge to provide valuable data mining insights to clients.
Teacher
Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法 may help Teachers to gain exposure to the field of data mining. They can utilize this information when teaching students about data science and related subjects.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Mining: Theories and Algorithms for Tackling Big Data | 数据挖掘:理论与算法.
经典数据挖掘教材,涵盖数据预处理、分类、聚类、关联分析、时序模式挖掘等核心技术。内容全面、深入浅出,适合作为数据挖掘入门或进阶读物。
统计学习领域的经典教材,内容全面、深入透彻。涵盖机器学习基本原理、监督学习、非监督学习、模型评估和选择等内容。适合作为统计学习或机器学习进阶读物。
深度学习领域权威著作,中文版由李沐等翻译。内容涵盖深度学习基本原理、模型架构、训练算法等内容。适合作为深度学习入门或进阶读物。
模式识别和机器学习领域的经典教材,内容涵盖概率论、贝叶斯方法、神经网络、支持向量机等机器学习核心技术。适合作为机器学习进阶读物或参考书。
深度学习领域权威著作,涵盖深度学习基本原理、模型架构、训练算法等内容。适合作为深度学习入门或进阶读物。
数据科学领域入門教材,內容涵蓋數據科學基礎、數據分析、機器學習、商業應用等內容。適合作為數據科學入門讀物或進階參考。
数据科学入门教材,内容涵盖数据科学基础、数据分析、机器学习等内容。适合作为数据科学入門讀物或進階參考。
机器学习入门书籍,内容涵盖机器学习基础、算法、应用等内容。适合作为机器学习入門讀物或進階參考。

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