Text Mining and Analytics
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| Rating | 4.0★ based on 119 ratings |
|---|---|
| Length | 7 weeks |
| Starts | Jul 3 (43 weeks ago) |
| Cost | $79 |
| From | University of Illinois at Urbana-Champaign via Coursera |
| Instructor | ChengXiang Zhai |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Programming |
| Tags | Data Science Data Analysis Machine Learning |
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What people are saying
text mining and analytics
The content of Text Mining and Analytics is very comprehensive and deep.
This course provides a comprehensive overview of text mining and analytics, which is incredibly useful for academic works and career alike.
It has an important introduction to the most key concepts and techniques for text mining and analytics.
It serves well as a First Class to text mining and analytics!
the course is very helpful in giving the overall flavor of text mining and analytics.
This was a great introduction to Text Mining and Analytics.
Text Mining and Analytics is the fourth course in the Data Mining specialization offered by the University of Illinois at Urbana-Champagne through Coursera.
Text Mining and Analytics is information-packed.
Text Mining and Analytics covers many useful data mining topics, but it has too much lackluster video content for its own good.
I give Text Mining and Analytics 2.5 out of 5 stars: Mediocre.
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very good course
Very good course!!
E Very good course with a lot of essential information about problems correlated with text understanding.
This is a very good course.
E Very good course!
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recommend this course
Highly recommend this course for anyone who intends to be a data science practitioner.
I recommend this course anyone!
I highly recommend this course to anyone who has a ML background and would like to work on NLP problems.
I would recommend this course to everyone who wants to know much more about theoric part of text mining.
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too much
I didnt feel like continuing, I had problem at job for which i enrolled in course,to do efficient text mining..Its all theoretical..Too much information at one short and no examples relating to it very theoretical.
The content is really good but the course has too much theory.
I liked the way I could find out about newest algorithms and trends, but I'd like for the ratio of theory and practice to be at least equal, since it's too much focused on the overview of everything there is.
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programming assignment
Mixing it with some practical programming assignments would have been very nice I think the course has very limited practical problems; so for beginners in NLP and text analytics, it is very difficult to grasp all the theoretical concepts presented in the course.
You as a student cannot see the big picture.The programming assignment was fun but did not help learning the course contents.
Everything was bad: content, presentation, evaluation, programming assignments.
The programming assignment was the cherry on the cake, confusing, badly prepared and didn't make any sense.
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my opinion
It is essential for modern data science practice in my opinion.
Hope the speaker can slow down sometimes.It will be more helpful if give more real-world examples 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.
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optional programming
So, some text books had better be specified.Homework of this course is quiz-based, only one optional programming task.
2) The optional programming exercises are easy to complete, but the environment is very confusing to set it up.
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sentiment analysis
Despite the amount of material to cover, this course did a great job of introducing the right amount of detail for various aspects (motivation, algorithms, algorithmic reasoning, evaluation) on topic modelling, text clustering, text categorization, sentiment analysis, aspect sentiment analysis, evaluation of text and non-text data in context, and more.
Course topics include mining word relations, topic discovery, text clustering, text categorization and sentiment analysis.
data science
This is a great course for data science.
A most know & understand unit for all students of Data Science.
theoretical concepts
It also does not explain these theoretical concepts in detail enough.
It was difficult for me as a new learner in the text analytics field to follow such dense theoretical concepts.
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Copy editor, text writer, weekly online columnist $44k
Data 1 2 $50k
Data 2 $50k
Text editor/verifier $62k
Text Editor $67k
Senior Copy editor, text writer, weekly online columnist $75k
Assistant Open Text CS Admin $82k
Data Scientist - Data Curation $92k
Team Open Text CS Admin Lead $110k
Data Analyst/Data Modeler $116k
Senior Product Manager for Text Systems $145k
Senior Software Engineer - Text Analytics $147k
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| Rating | 4.0★ based on 119 ratings |
|---|---|
| Length | 7 weeks |
| Starts | Jul 3 (43 weeks ago) |
| Cost | $79 |
| From | University of Illinois at Urbana-Champaign via Coursera |
| Instructor | ChengXiang Zhai |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Programming |
| Tags | Data Science Data Analysis Machine Learning |
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