Spécialisation Data Mining

Commence le Jun 04

Spécialisation Data Mining

Analyze Text, Discover Patterns, Visualize Data. Solve real-world data mining challenges.

À propos de cette Spécialisation

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.

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courses
6 courses

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projects
Projets

Conçu pour vous aider à vous exercer et à appliquer les compétences que vous avez acquises.

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Certificats

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Cours
Intermediate Specialization.
Some related experience required.
  1. COURS 1

    Data Visualization

    Session à venir : Jun 4
    Sous-titres
    English, Korean

    À propos du cours

    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 mi
  2. COURS 2

    Text Retrieval and Search Engines

    Session à venir : Jun 4
    Sous-titres
    English

    À propos du cours

    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
  3. COURS 3

    Text Mining and Analytics

    Session à venir : May 28
    Sous-titres
    English

    À propos du cours

    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 a
  4. COURS 4

    Pattern Discovery in Data Mining

    Session à venir : Jun 4
    Sous-titres
    English

    À propos du cours

    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 mi
  5. COURS 5

    Cluster Analysis in Data Mining

    Session à venir : May 28
    Sous-titres
    English

    À propos du cours

    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
  6. COURS 6

    Data Mining Project

    Session à venir : Jun 18
    Sous-titres
    English

    À propos du Projet Final

    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

Créateurs

  • University of Illinois at Urbana-Champaign

    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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