About this Course

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Learner Career Outcomes

30%

started a new career after completing these courses

38%

got a tangible career benefit from this course

12%

got a pay increase or promotion

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 6 in the

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 33 hours to complete

English

Subtitles: English, Korean

Skills you will gain

Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis

Learner Career Outcomes

30%

started a new career after completing these courses

38%

got a tangible career benefit from this course

12%

got a pay increase or promotion

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 6 in the

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 33 hours to complete

English

Subtitles: English, Korean

Offered by

University of Illinois at Urbana-Champaign logo

University of Illinois at Urbana-Champaign

Start working towards your Master's degree

This course is part of the 100% online Master in Computer Science from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

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Week
1

Week 1

2 hours to complete

Orientation

2 hours to complete
2 videos (Total 15 min), 5 readings, 2 quizzes
2 videos
Course Prerequisites & Completion6m
5 readings
Welcome to Text Mining and Analytics!10m
Syllabus15m
About the Discussion Forums15m
Updating your Profile10m
Social Media10m
2 practice exercises
Orientation Quiz15m
Pre-Quiz26m
4 hours to complete

Week 1

4 hours to complete
9 videos (Total 109 min), 1 reading, 2 quizzes
9 videos
1.2 Overview Text Mining and Analytics: Part 211m
1.3 Natural Language Content Analysis: Part 112m
1.4 Natural Language Content Analysis: Part 24m
1.5 Text Representation: Part 110m
1.6 Text Representation: Part 29m
1.7 Word Association Mining and Analysis15m
1.8 Paradigmatic Relation Discovery Part 114m
1.9 Paradigmatic Relation Discovery Part 217m
1 reading
Week 1 Overview10m
2 practice exercises
Week 1 Practice Quiz1h
Week 1 Quiz1h
Week
2

Week 2

4 hours to complete

Week 2

4 hours to complete
10 videos (Total 116 min), 1 reading, 2 quizzes
10 videos
2.2 Syntagmatic Relation Discovery: Conditional Entropy11m
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 113m
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 29m
2.5 Topic Mining and Analysis: Motivation and Task Definition7m
2.6 Topic Mining and Analysis: Term as Topic11m
2.7 Topic Mining and Analysis: Probabilistic Topic Models14m
2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 110m
2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 213m
2.10 Probabilistic Topic Models: Mining One Topic12m
1 reading
Week 2 Overview10m
2 practice exercises
Week 2 Practice Quiz1h
Week 2 Quiz1h
Week
3

Week 3

10 hours to complete

Week 3

10 hours to complete
10 videos (Total 103 min), 2 readings, 3 quizzes
10 videos
3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110m
3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28m
3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111m
3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 210m
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36m
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110m
3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210m
3.9 Latent Dirichlet Allocation (LDA): Part 110m
3.10 Latent Dirichlet Allocation (LDA): Part 212m
2 readings
Week 3 Overview10m
Programming Assignments Overview10m
2 practice exercises
Week 3 Practice Quiz1h
Quiz: Week 3 Quiz1h
Week
4

Week 4

5 hours to complete

Week 4

5 hours to complete
9 videos (Total 141 min), 1 reading, 2 quizzes
9 videos
4.2 Text Clustering: Generative Probabilistic Models Part 116m
4.3 Text Clustering: Generative Probabilistic Models Part 28m
4.4 Text Clustering: Generative Probabilistic Models Part 314m
4.5 Text Clustering: Similarity-based Approaches17m
4.6 Text Clustering: Evaluation10m
4.7 Text Categorization: Motivation14m
4.8 Text Categorization: Methods11m
4.9 Text Categorization: Generative Probabilistic Models31m
1 reading
Week 4 Overview10m
2 practice exercises
Week 4 Practice Quiz1h
Week 4 Quiz1h

About the Data Mining Specialization

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....
Data Mining

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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