MK
Good course, very well structured and with interesting assignments. Some (especially first) lessons are more of a general culture but most are very helpful and allow to learn a lot of things.

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.

MK
Good course, very well structured and with interesting assignments. Some (especially first) lessons are more of a general culture but most are very helpful and allow to learn a lot of things.
PM
A great overview of text retrieval methods. Good coverage of search engines. A longer course will cover search engine better (remember this is a 6 weeker)
JS
The content was very useful, and the preparation of the course denoted much care and preparation by the teacher. I would love to see some modern topics like word embeddings covered in the course!
CY
I learned a lot from this lecture. And I believe the lecture is excellent except that if he could become a little bit funny, then it would be perfect. Thanks,Clark
GL
This is a very good course covering all area of clustering. The only thing I feel a little struggle is some algorithm explained too brief, I prefer some detail step by step examples.
IA
The project help me to practice the whole specialization algorithms and techniques.
ZO
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.
BI
Excellent ! Well organized, presented with aptitude to detail. Definitely will recommend and take further units in this specialization. Thanks Prof
WP
Most of the lessons are mathematical formulae in which, in my opinion, I need more real case study/practice to make myself clearly understand on how do those formulae perform.
GL
Excellent course. Now I have a big picture about pattern discovery and understand some popular algorithm. Also professor points out the direction for further study.
SS
Very detailed introduction of Clustering techniques.
SR
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.
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Good course but need some thing to do:
1. More material. Some topics are just looked superficially.
2. Need provide addition video lessons ore provide information to student that he need know some visualization tool. Excel, Tableau, any thing.
3. There is some errors what still not corrected for many months - in week 4 some video is empty, some misunderstanding information on video slide.
Too much background information, not enough information critical to applicable demands.
The fundamental concept part and assignments are good.
But I think since it is related to visualization, the course can spend more time on how to make a visual.
Very generic ideas, not sure if worth it.
most horrible specialization in coursera! Time wasted
Very nice course. It encourages you to learn different data visualization tools for completing the assignments. And, peer graded assignments are too beneficial since, they give you a sight of how other peers are completing their work and all. Lecture videos too are awesome!!
It's always with Coursera providing amazing courses for students to enhance their skills and adding their part to the success of students in their life's.
This Amazing course data visualization starts from very basic, like which colour our eye attracts more to reaches the top of data visualization topics like visualizing complex network graphics.
Thanks for Coursera for this amazing course.
One of the best courses that i have attended during my entire life. I have worked as BI consultant for 15 years and the contents availed in this course is a must to have for anyone working in BI field. Thanks Illinois University for this amazing course
The lectures introduces various ways of visualizing data.
There are fewer details on Tableau software usage and hand-on network graphing tutorial, students will have to search for more knowledge in these parts during the class.
Good course, very well structured and with interesting assignments. Some (especially first) lessons are more of a general culture but most are very helpful and allow to learn a lot of things.
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.
Really like this course which leads me to Tableau. After the course, I take about 8 hours to learn Tableau and I think my ability of data visualization ballooned.
Before learning any BI tools or Visualization library, this course content needs to be known
seemed to me the best course of the specialization
Very solid data visualization class for people interesting in visualization. It explains a lot of knowledge under the hood. Not that fit for people who just want to learn some tools and quick skills. It is computer science.
Bueno para obtener ideas de que hacer, pero necesitas saber programar a nivel basico
Outdated materials, theory is taught and then praxis is tested. Course needs an update to materials and either teach AND test theory, or teach AND test praxis. I passed this class by switching sessions multiple times in order to use the time to learn the practical skills in data visualization I needed to pass the tests.
Additionally, the presented materials and the lecture format are very uninspiring.
too theoretical without enough practical quiz and assignment
waste
Professor has a soft, easy to follow teaching style but the course structure has several pitfalls:
It gets too theoretical in week 3 and 4 and you really don't need complex algebra or statistics to pass the course but it'd be hard to understand the core matters without them. Week 4's assignment is only a quiz and I think there should have been a project to really test student's ability to apply the learned materials in real situations. Besides, you don't need programming skill to complete the assignments, but it takes a lot of time to learn free, ready-made datavis tools and they are too limited to deliver the outcomes we want.
I think if anyone is serious about learning data visualization, you can't get away with not knowing programming and statistics and those two should be prerequisites.