23,012 recent views

## 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

#### 100% online

Start instantly and learn at your own schedule.

#### English

Subtitles: English, Korean

### Skills you will gain

Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis

## 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

#### 100% online

Start instantly and learn at your own schedule.

#### English

Subtitles: English, Korean

## Syllabus - What you will learn from this course

Content Rating91%(2,374 ratings)
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
Welcome to Text Mining and Analytics!10m
Syllabus15m
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
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
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
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
Week 4 Overview10m
2 practice exercises
Week 4 Practice Quiz1h
Week 4 Quiz1h
4.4
106 Reviews

### Top reviews from Text Mining and Analytics

By JHFeb 10th 2017

Excellent course, the pipeline they propose to help you understand text mining is quite helpful. It has an important introduction to the most key concepts and techniques for text mining and analytics.

By DCMar 25th 2018

The content of Text Mining and Analytics is very comprehensive and deep. More practise about how formula works would be better. Quiz could be not tough to be completed after attending every lectures.

### ChengXiang Zhai

Professor
Department of Computer Science
62,085 Learners
4 Courses

## 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.

## 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....