Text Mining and Analytics

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5/10 stars
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Course Details

Cost

FREE,
Add a Verified Certificate for $79

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Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4715 reviews

Course Description

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.
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Reviews 5/10 stars
5 Reviews for Text Mining and Analytics

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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

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4/10 starsTaking Now
2 years, 8 months ago
The lecturer hypnotized me in every video. The content is hard to follow and out of context at times. Too much reading out theory and no application. I will complete it nonetheless since I have paid for it. Money aside, I would have spent my time more productively had I taken Standard's NLP course. Which I will definitely do so to start building my skill in this area.
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Kristina Šekrst profile image
Kristina Šekrst profile image
8/10 starsCompleted
  • 102 reviews
  • 102 completed
3 years, 10 months ago
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. I've learned new concepts, but more R/Py examples could help.
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Greg Hamel profile image
Greg Hamel profile image
4/10 starsCompleted
  • 116 reviews
  • 107 completed
4 years, 1 month ago
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 builds upon the second course in the specialization, Text Retrieval and Search Engines. Course topics include mining word relations, topic discovery, text clustering, text categorization and sentiment analysis. The course lists programming proficiency (especially in C++) and knowledge of probability and statistics. Keeping with the system established by other data mining specialization track courses, grading is based entirely upon 4 multiple choice quizzes with 10 questions apiece. You only get one attempt at the quizzes. Text Mining and Analytics is information-packed. Each week has 2.5 to 4 hours of lecture content in video segments that generally range from 10 to 20 minutes. The videos quality is satisfactory but the explanations and content on the slides coul... 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 builds upon the second course in the specialization, Text Retrieval and Search Engines. Course topics include mining word relations, topic discovery, text clustering, text categorization and sentiment analysis. The course lists programming proficiency (especially in C++) and knowledge of probability and statistics. Keeping with the system established by other data mining specialization track courses, grading is based entirely upon 4 multiple choice quizzes with 10 questions apiece. You only get one attempt at the quizzes. Text Mining and Analytics is information-packed. Each week has 2.5 to 4 hours of lecture content in video segments that generally range from 10 to 20 minutes. The videos quality is satisfactory but the explanations and content on the slides could be a bit clearer. Despite the long videos, there are no comprehension questions or exercises to interact with during or after lecture segments to reinforce learning. By the time you reach the quiz at the end of the unit, you may find yourself having to go back review certain videos to answer the questions. There is an optional programming assignment. Text Mining and Analytics covers many useful data mining topics, but it has too much lackluster video content for its own good. I can’t help but feel like a better course would have been able to condense the videos down to cover the same topics more clearly in half the time, leaving room for more quizzes and exercises. This course could serve as useful as reference material but students watching straight through may find a lot of information going in one ear and out the other. I give Text Mining and Analytics 2.5 out of 5 stars: Mediocre.
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Richard Taylor profile image
Richard Taylor profile image
2/10 starsCompleted
  • 29 reviews
  • 28 completed
4 years, 1 month ago
This course was a major disappointment for me. Everything was bad: content, presentation, evaluation, programming assignments. You name it. I will start with the content of the course. Text Mining is a very exciting area of computer science getting a lot of attention these days, for some reason the algorithms and ideas that are being used today and are the state of the art weren't covered in this course. There's nothing about things such as deep learning, wordvec, glove, recurrent neural networks or LSTM that are the most important tools used today for natural language processing. So I think the content is outdated and I felt I was getting a review of things the instructor likes instead of things really used in the field. Next the presentation: really terrible. The instructor dances around formulas and papers without a single working example to check how things work or any attempt to simplify the topics or explain the most impo... This course was a major disappointment for me. Everything was bad: content, presentation, evaluation, programming assignments. You name it. I will start with the content of the course. Text Mining is a very exciting area of computer science getting a lot of attention these days, for some reason the algorithms and ideas that are being used today and are the state of the art weren't covered in this course. There's nothing about things such as deep learning, wordvec, glove, recurrent neural networks or LSTM that are the most important tools used today for natural language processing. So I think the content is outdated and I felt I was getting a review of things the instructor likes instead of things really used in the field. Next the presentation: really terrible. The instructor dances around formulas and papers without a single working example to check how things work or any attempt to simplify the topics or explain the most important concepts, just references to papers and reading out loud formulas. The evaluation is also problematic you have only 1 chance at each quiz so effectively you have four exams. This wouldn't be a problem if the lectures contained anything remotely similar to what the quizzes ask. If you are going to give students one attempt at each quiz you need to at least do some examples of excercises in your lectures. The programming assignment was the cherry on the cake, confusing, badly prepared and didn't make any sense. The META system used is really terrible to work with and is a tool that as far as I know is not used at all in the real world. There are plenty of interesting things and tools to use in this area but for some reason META was chosen. I think that was a very bad choice. At the end I completed all quizzes and passsed the course and felt I learned almost nothing and the very few things I remember about the lectures are things that I know won't be useful in the real world. I would have given it 0 stars but I have no such an option.
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Hamideh Iraj profile image
Hamideh Iraj profile image
6/10 starsCompleted
  • 70 reviews
  • 60 completed
4 years, 1 month ago
I was very happy when I heard about the course because it was my first MOOC in text-mining. The course contents are too vague and the instructor is not suitable for teaching. His voice is monotonous and boring and he is very difficult to follow. The course lacks context and appetite wetting and you don't know what are you going to do with these algorithms. You as a student cannot see the big picture.The programming assignment was fun but did not help learning the course contents. I will complete the course just because the topic of this course is very important and it is difficult to learn this topic from books, either. A video-based MOOC is anyhow better than studying this subject from books.
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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.