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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。

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.

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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.

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

Traffic lights

Read about what's good
what should give you pause
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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Reviews summary

数据挖掘:扎实理论与实践结合

学生们说,这门数据挖掘课程提供了一个全面且深入的概览,特别是在理论与实践的平衡上做得出色。讲师的教学风格被广泛赞誉,尤其擅长用生动幽默的例子来解释复杂的概念,使得学习过程引人入胜易于理解。许多学习者认为这门课不仅传授了数据挖掘的核心技术,还培养了数据分析的思维方式,对职业发展和研究入门大有裨益。一些评论提到,虽然课程理论性较强,但其实用案例清晰的讲解使其成为入门和提升数据挖掘技能的理想选择。
课程注重方法论与思维方式的培养,而非死记硬背。
"最让我印象深刻的是,课程不仅教我具体算法,更重要的是教我如何思考问题,培养了数据分析的研究方法和思维方式。"
"这不仅仅是一门技术课,更像是一门思维提升课,它告诉我如何从数据中发现价值,这是我最看重的地方。"
"我觉得课程让我从一个更高的视角去理解数据挖掘,不仅仅是工具的使用,更是对问题解决思路的启发。"
课程覆盖广泛的数据挖掘技术,适合不同背景。
"课程涵盖了数据挖掘的各个方面,从预处理到推荐系统,广度足够,而且每个主题都有一定的深度。"
"对于像我这样想全面了解数据挖掘的人来说,这门课简直是宝藏,知识体系搭建得很完整。"
"我之前对数据挖掘只有零散的了解,这门课帮我把知识点都串联起来,形成了一个清晰的框架。"
课程兼顾理论深度与实际应用,提供实用知识。
"这门课很好地平衡了理论深度和实际应用,不像有些课程只讲理论不落地,这里有很多真实的案例分析。"
"我觉得课程最棒的地方在于它不回避复杂的理论,但又总是能用具体的例子把它阐释清楚,学完感觉既懂原理又会应用。"
"课程强调的理论与实践结合,确实让我学到了“活的知识”,很多思路和方法可以直接用于工作中。"
讲师以风趣的方式讲解复杂概念,让学习充满乐趣。
"老师的讲解非常风趣幽默,用各种生活中的例子来解释枯燥的算法,让我在学习过程中保持了很高的兴趣。"
"我发现老师的口才和故事真的很吸引人,把数据挖掘讲得一点都不干涩,是我上过的最有趣的课程之一。"
"通过这门课,我不仅学到了专业的知识,更重要的是享受了学习的过程,老师的教学魅力是关键。"
部分学习者希望课程能增加更多动手编程练习。
"虽然有很多应用案例,但如果能有更多配套的编程作业或实验,上手操作的机会更多就更好了。"
"我觉得理论部分很精彩,但对于完全的新手来说,动手实践的机会还是太少,需要自己额外找项目来练手。"
"希望课程能增加一些小型的编程挑战,帮助我们更好地巩固算法的实现细节。"

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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