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.
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.
Gregory J Hamel ( Life Is Study) completed this course.
Data Visualization is the fifth and final course in the data mining specialization offered by John Hopkins University on Coursera. The 4-week course provides a high-level overview of data visualization, covering topics like human visual perception, basic...
Data Visualization is the fifth and final course in the data mining specialization offered by John Hopkins University on Coursera. The 4-week course provides a high-level overview of data visualization, covering topics like human visual perception, basic plotting constructs and design principles, visualizing networks and visualizing databases. The course doesn’t have any particular prerequisites, but knowing how to make plots with some software package or programming language will be helpful for the assignments. Grading is based on two quizzes and two peer-graded visualization projects.
The lecture content in Data Visualization is better than the lectures of the previous courses in the data mining specialization. The instructor is easy to understand and there isn’t as much dense technical content to absorb. On the downside, since the course focuses on high-level concepts, you won’t learn how to actually construct your own visualizations. It’s up to you to pick out software and figure out how to make visualizations with it. It would have been preferable for the entire data science specialization to pick a programming language and stick with it throughout to pair concepts with specific implementations and exercises.
Data Visualization is a nice introduction to visualization at a high level, but the lack of low-level technical instruction and exercises limits its practical usefulness, especially for students who don’t already know how to create their own visualizations. The course is relatively smooth end to what is otherwise a rocky specialization, but since the content has no real connection to the other courses in the data mining track, you could take it as a standalone course.
Kristina Šekrst completed this course and found the course difficulty to be medium.
I thought this class would be boring, but it was a bit of a rest we all needed after the previous programming-buffed courses in the Data Mining specialization. However, I did manage to learn some new stuff and concepts. I would like to recommend to make this class perhaps a bit longer, and to introduce people into d3 coding, since the vast majority of the capstone examples and tasks evolved around creating nice visualizations of the mining tasks.
Dario Bertero completed this course, spending 2 hours a week on it and found the course difficulty to be medium.
The course was simply a theoretical overwiew of some basic techniques used in data visualization. The only practical experience was given in the two homeworks, which mostly required to rely on previous or self-taught knowledge and experience. Lectures were mostly long and boring dictations of the transparencies, and were of little use for the two practical homeworks. Overall I would say there is very little to learn in the course, and these few things are better learnt elsewhere.
If you're interested in data visualization for science purposes, then don't look here: take the Cousera "Caltech JPL summer school on data analytic" instead, which is aimed at doing science and not at business products. If you're interested in business products, then, oh well... I guess this may be ok for you
Jordan Lui completed this course, spending 1 hours a week on it and found the course difficulty to be very easy.
This is a very useful course to anyone who wants to learn how to convey information more effectively. A very useful course to Engineers and technical people to learn how to present their valuable data in a way that is well understood by everyday people.
Daniel Munroe completed this course, spending 8 hours a week on it and found the course difficulty to be hard.
It was introductory but to do the assignments there was a lot of discovery/reading/research/etc to be done to be able to do it properly