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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Data-Mining
von


Über diesen Kurs
Kompetenzen, die Sie erwerben
- Data Visualization Software
- Tableau Software
- Data Virtualization
- Data Visualization (DataViz)
von

University of Illinois at 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.
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Lehrplan - Was Sie in diesem Kurs lernen werden
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.
Bewertungen
- 5 stars64,76 %
- 4 stars24,72 %
- 3 stars6,25 %
- 2 stars2,47 %
- 1 star1,77 %
Top-Bewertungen von DATENVISUALISIERUNG
This very interesting course have sharpened my ability to read and interpret graphs in general and more importantly to pay more attention to every little details.
One of the excellent courses I have ever studied. Professor style of teaching is very soft and simple, point to point and very clear. I have given 100 out 100 marks.
Thank you for this amazing course, for.me the most enjoyable and amazing tool for this course is how encouraging me to find real life data repository and learn how to visualize it.
It was a very enriching experience. Coursera is such a nice platform for learners. Very good lectures. I am thankful for the team and the Instructor.
Über den Spezialisierung Data-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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