Introduction to Topic Modelling in R from Coursera

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Syllabus

Project Overview

By the end of this project, you will know how to load and pre-process a data set of text documents by converting the data set into a document feature matrix and reducing it’s dimensionality. You will also know how to run an unsupervised machine learning LDA topic model (Latent Dirichlet Allocation). You will know how to plot the change in topics over time as well as explore the distribution of topic probability in each document. This project is aimed at beginners who have a basic familiarity with the statistical programming language R and the RStudio environment, or people with a small amount of experience who would like to learn how to apply topic modelling on textual data.

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Suitable for people who have basic familiarity with the statistical programming language R and the RStudio environment

Specifically designed for people with basic familiarity with the statistical programming language R and the RStudio environment

Useful for people who have a small amount of experience who would like to learn how to apply topic modelling on textual data

Provides hands-on experience with loading and pre-processing a data set of text documents, converting the data set into a document feature matrix, reducing its dimensionality, and running an unsupervised machine learning LDA topic model

Covers essential concepts of topic modeling, including topic distribution, topic probability, and the change in topics over time

Taught by Nicole Baerg, who has expertise in topic modelling and natural language processing

Career center

Learners who complete Introduction to Topic Modelling in R will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts study data using specialized applications and programming languages to extract meaningful insights. They help businesses understand trends, solve complex issues, and improve their operations. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Data Analysts. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, social media data, and other forms of unstructured data. This knowledge can help you make better decisions and drive business growth.

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Market Researcher

Market Researchers gather and analyze data about consumer behavior, market trends, and competitors. They use this information to help businesses make informed decisions about product development, marketing campaigns, and pricing. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Market Researchers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, social media data, and other forms of unstructured data. This knowledge can help you make better decisions and drive business growth.

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Business Analyst

Business Analysts study business processes and systems to identify areas for improvement. They use data analysis and modeling techniques to develop solutions that can help businesses improve their efficiency, productivity, and profitability. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Business Analysts. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, employee surveys, and other forms of unstructured data. This knowledge can help you make better recommendations and drive business growth.

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Data Scientist

Data Scientists use data to solve complex problems and make predictions. They use a variety of statistical and machine learning techniques to analyze data and develop models that can help businesses make better decisions. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Data Scientists. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, social media data, and other forms of unstructured data. This knowledge can help you build better models and drive business growth.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. They use a variety of programming languages and software tools to build models that can learn from data and make predictions. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Machine Learning Engineers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, social media data, and other forms of unstructured data. This knowledge can help you build better models and drive business growth.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use a variety of programming languages and software tools to build applications that meet the needs of users. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Software Engineers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of user feedback, bug reports, and other forms of unstructured data. This knowledge can help you build better software and drive business growth.

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Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Product Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, market research, and other forms of unstructured data. This knowledge can help you make better decisions and drive product success.

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Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. They work with a variety of teams to create and implement marketing plans that reach target audiences and achieve business goals. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Marketing Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, market research, and other forms of unstructured data. This knowledge can help you make better decisions and drive marketing success.

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Sales Manager

Sales Managers are responsible for leading and motivating sales teams. They work with salespeople to develop and execute sales strategies that achieve business goals. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Sales Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, sales data, and other forms of unstructured data. This knowledge can help you make better decisions and drive sales growth.

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Customer Success Manager

Customer Success Managers are responsible for building and maintaining relationships with customers. They work with customers to ensure that they are satisfied with their products or services and that they are getting the most value from them. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Customer Success Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, support tickets, and other forms of unstructured data. This knowledge can help you make better decisions and drive customer success.

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Content Strategist

Content Strategists are responsible for developing and executing content strategies. They work with a variety of teams to create and implement content that meets the needs of target audiences and achieves business goals. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Content Strategists. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, market research, and other forms of unstructured data. This knowledge can help you make better decisions and drive content success.

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Public relations manager

Public Relations Managers are responsible for managing the public relations of an organization. They work with a variety of stakeholders to build and maintain a positive image of the organization. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Public Relations Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of public sentiment, media coverage, and other forms of unstructured data. This knowledge can help you make better decisions and drive public relations success.

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Communications Manager

Communications Managers are responsible for developing and executing communications strategies. They work with a variety of teams to create and implement communications that reach target audiences and achieve business goals. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Communications Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, market research, and other forms of unstructured data. This knowledge can help you make better decisions and drive communications success.

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Technical Writer

Technical Writers are responsible for creating and maintaining technical documentation. They work with a variety of stakeholders to create documentation that is clear, concise, and accurate. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Technical Writers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of user feedback, product documentation, and other forms of unstructured data. This knowledge can help you make better decisions and drive technical writing success.

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Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to Topic Modelling in R.

Binary Neural Networks

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Provides a comprehensive overview of topic models, including the mathematical foundations, algorithms, and applications. It valuable resource for researchers and practitioners who want to learn more about topic modeling.

Binary Neural Networks: Algorithms, Architectures,...

Kindle Edition

$$$

R概率图模型入门与实践

Save

This paper introduces the Latent Dirichlet Allocation (LDA) model, a generative statistical model for collections of discrete data such as text corpora. It is one of the most widely used topic models and has been applied to a variety of problems in natural language processing.

Information Theory, Inference and Learning...

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Provides a practical introduction to generative models, including topic models. It valuable resource for researchers and practitioners who want to learn more about generative models and their applications.

Information Theory, Inference and Learning...

Hardcover

$$$

Natural Language Processing with Python

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Provides a practical introduction to natural language processing with Python. It covers a variety of topics, including topic modeling, part-of-speech tagging, and named entity recognition.

Natural Language Processing with Python: Analyzing...

Paperback

$$$

Natural Language Processing with Python: Analyzing...

Kindle Edition

Text Mining with R

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Provides a practical introduction to text mining with R. It covers a variety of topics, including topic modeling, text classification, and sentiment analysis.

Practical Data Science for Information Professionals

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This paper provides a gentle introduction to topic modeling. It valuable resource for researchers and practitioners who want to learn more about topic modeling and its applications.

Practical Data Science For Information Professionals

Paperback

Practical Text Analytics

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This paper introduces the Probabilistic Latent Semantic Analysis (PLSA) model, a probabilistic model for text data. It valuable resource for researchers and practitioners who want to learn more about PLSA and its applications.

Practical Text Analytics: Maximizing the Value of...

Kindle Edition

$$$

Introduction to Topic Modelling in R

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