Text Mining and Analytics from Coursera

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.

What's inside

Syllabus

Orientation

You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.

Week 1

During this module, you will learn the overall course design, an overview of natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications, and word association mining with a particular focus on mining one of the two basic forms of word associations (i.e., paradigmatic relations).

Week 2

During this module, you will learn more about word association mining with a particular focus on mining the other basic form of word association (i.e., syntagmatic relations), and start learning topic analysis with a focus on techniques for mining one topic from text.

Week 3

During this module, you will learn topic analysis in depth, including mixture models and how they work, Expectation-Maximization (EM) algorithm and how it can be used to estimate parameters of a mixture model, the basic topic model, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.

Week 4

During this module, you will learn text clustering, including the basic concepts, main clustering techniques, including probabilistic approaches and similarity-based approaches, and how to evaluate text clustering. You will also start learning text categorization, which is related to text clustering, but with pre-defined categories that can be viewed as pre-defining clusters.

Week 5

During this module, you will continue learning about various methods for text categorization, including multiple methods classified under discriminative classifiers, and you will also learn sentiment analysis and opinion mining, including a detailed introduction to a particular technique for sentiment classification (i.e., ordinal regression).

Week 6

During this module, you will continue learning about sentiment analysis and opinion mining with a focus on Latent Aspect Rating Analysis (LARA), and you will learn about techniques for joint mining of text and non-text data, including contextual text mining techniques for analyzing topics in text in association with various context information such as time, location, authors, and sources of data. You will also see a summary of the entire course.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores statistical approaches to text mining that can be applied to any natural language

In-depth coverage of topic analysis, including mixture models, Expectation-Maximization (EM) algorithm, PLSA, and LDA

Provides a solid foundation in text clustering and categorization, with focus on probabilistic and similarity-based approaches

Examines sentiment analysis and opinion mining, including a detailed introduction to ordinal regression for sentiment classification

Introduces techniques for joint mining of text and non-text data, including contextual text mining for analyzing topics in association with various context information

Reviews summary

In-depth text mining and analytics course

According to students, Text Mining and Analytics is a well-received course that provides a thorough and comprehensive overview of the field. Learners highlight the detailed lectures, engaging content, and knowledgeable instructor as key strengths of the course. They appreciate the practical focus of the course, including the use of real-world examples and programming assignments. However, some learners note that the course could benefit from updated materials and a clearer presentation style.

Covers a wide range of topics in text mining.

"The content of Text Mining and Analytics is very comprehensive and deep."

"It covers quite a few techniques that are usually not covered in other machine learning courses..."

Well-structured lectures with clear explanations.

"Great lectures. Thorough explanations on the concepts. Thank you!"

"Very detailed course. Helps in gaining complete understanding of text mining "

Instructor is an expert in the field.

"Prof. Zhai's textbook is well-worth the added investment. His Coursera lectures helped me to "read between the lines.""

"I would like to thank Prof Cheng for imparting his knowledge. Thank you, Coursera."

Some advanced topics may be challenging for beginners.

"Very difficult, especially when it comes to logic and using math equations. You'll have a lot to learn from this course."

"This course is a mixed bag... there is only a single optional programming assigment in C++. Learning materials like these is often more thorough with programming assignments..."

Some materials may need to be updated.

"Excellent Course, but it may becoming a little dated given that most of the references are pre c2010."

"This was a good introduction to the topic and the reading materials and lectures were very good. The only change I would recommend would be to update the programming assignments..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Text Mining and Analytics with these activities:

Review basics of statistics

Show steps

Help you refresh your understanding of the statistical concepts that are covered in the course.

Browse courses on Statistical Inference

Show steps

Review the basics of probability
Review the basics of statistical inference
Review the basics of regression

Follow tutorials on additional text mining tools

Show steps

Provide you with hands-on experience with different text mining tools to complement the course content.

Show steps

Identify a text mining tool that you are interested in
Find a tutorial on how to use the tool
Follow the tutorial and complete the exercises
Reflect on what you learned and how you can apply it to your own projects

Write an article on your favorite text mining topic

Show steps

Help you synthesize your knowledge of text mining and demonstrate your ability to communicate your understanding in a coherent and structured manner.

Browse courses on Topic Modeling

Show steps

Choose your favorite text mining topic
Research the topic to gather information
Organize your thoughts and outline your article
Write the first draft of your article
Revise and edit your article

One other activity

Expand to see all activities and additional details

Show all four activities

Mentor a junior student or fellow learner

Show steps

Help you reinforce your own understanding of text mining concepts while also helping others learn.

Show steps

Identify a junior student or fellow learner who is interested in learning about text mining
Offer to mentor them and provide guidance on their learning journey
Meet with them regularly to discuss their progress and answer their questions

Career center

Learners who complete Text Mining and Analytics will develop knowledge and skills that may be useful to these careers:

Data Scientist

As a Data Scientist, you will develop and deploy models and algorithms, which may include implementing statistical and text-mining techniques to resolve complex business problems. This course will help you build a foundation in text data analysis, natural language processing, and topic modeling, which are essential skills for a Data Scientist.

See salaries and explore the career path for Data Scientist

Market Researcher

As a Market Researcher, you will be responsible for collecting, analyzing, and interpreting data to understand market trends. This course can be helpful for Market Researchers who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze customer feedback, social media data, and other forms of unstructured text data.

See salaries and explore the career path for Market Researcher

Business Analyst

As a Business Analyst, you will analyze business processes and systems to identify areas for improvement. This course can help Business Analysts develop skills in text data analysis, which can be used to analyze customer feedback, identify trends, and improve business processes.

See salaries and explore the career path for Business Analyst

Content Analyst

As a Content Analyst, you will analyze and interpret text data to identify trends and patterns. This course will help you build a foundation in text data analysis, natural language processing, and topic modeling, which are essential skills for a Content Analyst.

See salaries and explore the career path for Content Analyst

Information Architect

As an Information Architect, you will design and organize websites and other digital platforms to ensure that users can easily find and access information. This course will help you build a foundation in text data analysis, natural language processing, and topic modeling, which can be used to improve the user experience of websites and other digital platforms.

See salaries and explore the career path for Information Architect

UX Researcher

As a UX Researcher, you will conduct research to understand user needs and improve the user experience of products and services. This course can be helpful for UX Researchers who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze user feedback and identify areas for improvement.

See salaries and explore the career path for UX Researcher

Technical Writer

As a Technical Writer, you will create instruction manuals, white papers, and other technical documents. This course can be helpful for Technical Writers who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze user feedback and improve the clarity and effectiveness of technical documents.

See salaries and explore the career path for Technical Writer

Product Manager

As a Product Manager, you will be responsible for the development and launch of new products and services. This course can be helpful for Product Managers who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze customer feedback, identify market trends, and improve product development.

See salaries and explore the career path for Product Manager

Digital Marketing Manager

As a Digital Marketing Manager, you will be responsible for developing and executing digital marketing campaigns. This course can be helpful for Digital Marketing Managers who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze customer feedback, identify trends, and improve marketing campaigns.

See salaries and explore the career path for Digital Marketing Manager

Social Media Manager

As a Social Media Manager, you will be responsible for managing social media accounts and developing social media content. This course can be helpful for Social Media Managers who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze customer feedback, identify trends, and improve social media content.

See salaries and explore the career path for Social Media Manager

Customer Success Manager

As a Customer Success Manager, you will be responsible for ensuring that customers are satisfied with their products and services. This course can be helpful for Customer Success Managers who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze customer feedback and identify areas for improvement.

See salaries and explore the career path for Customer Success Manager

Sales Manager

As a Sales Manager, you will be responsible for leading and managing a sales team. This course can be helpful for Sales Managers who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze customer feedback, identify sales trends, and improve sales strategies.

See salaries and explore the career path for Sales Manager

Account Manager

As an Account Manager, you will be responsible for managing relationships with key customers. This course can be helpful for Account Managers who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze customer feedback and identify areas for improvement.

See salaries and explore the career path for Account Manager

Consultant

As a Consultant, you will provide advice and guidance to clients on a variety of business issues. This course can be helpful for Consultants who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze client data and identify areas for improvement.

See salaries and explore the career path for Consultant

Entrepreneur

As an Entrepreneur, you will start and run your own business. This course can be helpful for Entrepreneurs who want to gain a deeper understanding of text data analysis techniques, which can be used to analyze customer feedback, identify market trends, and improve business strategies.

See salaries and explore the career path for Entrepreneur