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.
Este curso faz parte do Programa de cursos integrados Mineração de dadosData Mining
oferecido por


Informações sobre o curso
Habilidades que você terá
- Data Visualization Software
- Tableau Software
- Data Virtualization
- Data Visualization (DataViz)
oferecido por

Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em Urbana-Champaign
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.
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Programa - O que você aprenderá com este curso
Course Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
Week 1: The Computer and the Human
In this week's module, you will learn what data visualization is, how it's used, and how computers display information. You'll also explore different types of visualization and how humans perceive information.
Week 2: Visualization of Numerical Data
In this week's module, you will start to think about how to visualize data effectively. This will include assigning data to appropriate chart elements, using glyphs, parallel coordinates, and streamgraphs, as well as implementing principles of design and color to make your visualizations more engaging and effective.
Week 3: Visualization of Non-Numerical Data
In this week's module, you will learn how to visualize graphs that depict relationships between data items. You'll also plot data using coordinates that are not specifically provided by the data set.
Week 4: The Visualization Dashboard
In this week's module, you will start to put together everything you've learned by designing your own visualization system for large datasets and dashboards. You'll create and interpret the visualization you created from your data set, and you'll also apply techniques from user-interface design to create an effective visualization system.
Avaliações
- 5 stars64,56%
- 4 stars24,82%
- 3 stars6,30%
- 2 stars2,45%
- 1 star1,84%
Principais avaliações do VISUALIZAÇÃO DE DADOS
It provides me with the base to transmit to the user the data analysis made in a visual way easy for them to understand and take decision.
A fairly interesting course with a good instructor. The course gave me a chance to play with my visualization tools in order to expand my usage rather than being in a rush to complete my tasks.
Very useful course. It enlightens my ways to data visualization. I knew some concepts, but in a disorganized way and not knowing how. This course fills these gaps. It is tremendously helpful.
I found the class to be very informative. The assignments on creating charts and graphs for large data sets were practical and helped me understand the concepts taught in the course.
Sobre Programa de cursos integrados Mineração de dadosData Mining
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.

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