数据挖掘

Data Mining : Analyze Text, Discover Patterns, Visualize Data

Solve real-world data mining challenges.

伊利诺伊大学香槟分校

专项课程

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数据挖掘
  • 分类: 计算机
  • 平台: Coursera
  • 语言: 英语

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.

第 1 门课程

数据可视化

Upcoming session: 5月 9 — 6月 20.

课程概述
Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

第 2 门课程

Text Retrieval and Search Engines

于 May 16th 开始

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.

第 3 门课程

Text Mining and Analytics

于 June 20th 开始

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.

第 4 门课程

Pattern Discovery in Data Mining

于 July 25th 开始

课程概述
Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

第 5 门课程

Cluster Analysis in Data Mining

于 August 29th 开始

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, density-based methods such as DBSCAN/OPTICS, probabilistic models, and the EM algorithm. Learn clustering and methods for clustering high dimensional data, streaming data, graph data, and networked data. Explore concepts and methods for constraint-based clustering and semi-supervised clustering. Finally, see examples of cluster analysis in applications.

毕业项目

Data Mining Capstone

于 October 3rd 开始

毕业项目介绍
This six-week long Capstone 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 Capstone 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 Capstone 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 Capstone.

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, and the top 20 reports, as evaluated by peer review, will also be judged by an Industry Expert Committee who will select 10 of the reports as the Best Capstone Projects. The Industry Expert Committee is composed of multiple relevant industry experts representing different companies.

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