Data Mining 专项课程

于 Mar 27 开始

Data Mining 专项课程

Analyze Text, Discover Patterns, Visualize Data

Solve real-world data mining challenges.

本专项课程介绍

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.

制作方:

courses
6 courses

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项目

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课程
Intermediate Specialization.
Some related experience required.
  1. 第 1 门课程

    数据可视化

    即将开课的班次:Mar 27 — May 1。
    字幕
    English, Chinese (Simplified)

    课程概述

    这这一课程中,我们将学习数据挖掘的基本概念及其基础的方法和应用,然后深入到数据挖掘的子领域——模式发现中,深入学习模式发现的概念、方法,及应用。我们也将介绍基于模式进行分类的方法以及一些模式发现有趣的应用。这一课程将给你提供学习技能和实践的机会,将可扩展的模式发现方法应用在在大体量交易数据上,讨论模式评估指标,以及学习用于挖掘各类不同的模式、序列模式,以及子图模式的方法。
  2. 第 2 门课程

    Text Retrieval and Search Engines

    即将开课的班次:Mar 27 — May 15。
    字幕
    English

    课程概述

    Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text. This course will cover search engine technologies, which play an important role in any data mining applications involving text data for two reasons. First, while the raw data may be large for any particular problem, it is often a relatively small subset of the data that are relevant, and a search engine is an essential tool for quickly discovering a small subset of relevant text data in a large text collection. Second, search engines are needed to help analysts interpret any patterns discovered in the data by allowing them to examine the relevant original text data to make sense of any discovered pattern. You will learn the basic concepts, principles, and the major techniques in text retrieval, which is the underlying science of search engines.
  3. 第 3 门课程

    Text Mining and Analytics

    即将开课的班次:Apr 3 — May 22。
    字幕
    English

    课程概述

    This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.
  4. 第 4 门课程

    Pattern Discovery in Data Mining

    即将开课的班次:Mar 27 — May 1。
    字幕
    English

    课程概述

    这这一课程中,我们将学习数据挖掘的基本概念及其基础的方法和应用,然后深入到数据挖掘的子领域——模式发现中,学习模式发现深入的概念、方法,及应用。我们也将介绍基于模式进行分类的方法以及一些模式发现有趣的应用。这一课程将给你提供学习技能和实践的机会,将可扩展的模式发现方法应用在在大体量交易数据上,讨论模式评估指标,以及学习用于挖掘各类不同的模式、序列模式,以及子图模式的方法。
  5. 第 5 门课程

    Cluster Analysis in Data Mining

    即将开课的班次:Apr 3 — May 8。
    字幕
    English

    课程概述

    Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
  6. 第 6 门课程

    Data Mining Project

    即将开课的班次:Apr 24 — Jun 12。
    字幕
    English

    毕业项目介绍

    Note: You should complete all the other courses in this Specialization before beginning this course. This six-week long Project course of the Data Mining Specialization will allow you to apply the learned algorithms and techniques for data mining from the previous courses in the Specialization, including Pattern Discovery, Clustering, Text Retrieval, Text Mining, and Visualization, to solve interesting real-world data mining challenges. Specifically, you will work on a restaurant review data set from Yelp and use all the knowledge and skills you’ve learned from the previous courses to mine this data set to discover interesting and useful knowledge. The design of the Project emphasizes: 1) simulating the workflow of a data miner in a real job setting; 2) integrating different mining techniques covered in multiple individual courses; 3) experimenting with different ways to solve a problem to deepen your understanding of techniques; and 4) allowing you to propose and explore your own ideas creatively. The goal of the Project is to analyze and mine a large Yelp review data set to discover useful knowledge to help people make decisions in dining. The project will include the following outputs: 1. Opinion visualization: explore and visualize the review content to understand what people have said in those reviews. 2. Cuisine map construction: mine the data set to understand the landscape of different types of cuisines and their similarities. 3. Discovery of popular dishes for a cuisine: mine the data set to discover the common/popular dishes of a particular cuisine. 4. Recommendation of restaurants to help people decide where to dine: mine the data set to rank restaurants for a specific dish and predict the hygiene condition of a restaurant. From the perspective of users, a cuisine map can help them understand what cuisines are there and see the big picture of all kinds of cuisines and their relations. Once they decide what cuisine to try, they would be interested in knowing what the popular dishes of that cuisine are and decide what dishes to have. Finally, they will need to choose a restaurant. Thus, recommending restaurants based on a particular dish would be useful. Moreover, predicting the hygiene condition of a restaurant would also be helpful. By working on these tasks, you will gain experience with a typical workflow in data mining that includes data preprocessing, data exploration, data analysis, improvement of analysis methods, and presentation of results. You will have an opportunity to combine multiple algorithms from different courses to complete a relatively complicated mining task and experiment with different ways to solve a problem to understand the best way to solve it. We will suggest specific approaches, but you are highly encouraged to explore your own ideas since open exploration is, by design, a goal of the Project. You are required to submit a brief report for each of the tasks for peer grading. A final consolidated report is also required, which will be peer-graded.

制作方

  • 伊利诺伊大学香槟分校

    Founded in 1867, the University of Illinois at Urbana-Champaign pioneers innovative research that tackles global problems and expands the human experience.

    The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.

  • John C. Hart

    John C. Hart

    Professor of Computer Science
  • Jiawei Han

    Jiawei Han

    Abel Bliss Professor
  • ChengXiang Zhai

    ChengXiang Zhai

    Professor

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