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Barsha Saha
In this 2-hour long project-based course, you will learn how to create topic models from a given corpus, relevant to gain business insights. Topic modeling can be applied to business reports, trending topics on social media platforms, or even reviews in e-commerce websites to extract latent themes associated with it. For illustration purposes, the custom dataset to be used in this project is built on articles concerned with Digital Economy. Assume, that an entity is interested to understand what does the term mean, and how the entity could be benefited from it. The method to build a topic model will be divided into the following...
Read more
In this 2-hour long project-based course, you will learn how to create topic models from a given corpus, relevant to gain business insights. Topic modeling can be applied to business reports, trending topics on social media platforms, or even reviews in e-commerce websites to extract latent themes associated with it. For illustration purposes, the custom dataset to be used in this project is built on articles concerned with Digital Economy. Assume, that an entity is interested to understand what does the term mean, and how the entity could be benefited from it. The method to build a topic model will be divided into the following...
Read more
In this 2-hour long project-based course, you will learn how to create topic models from a given corpus, relevant to gain business insights. Topic modeling can be applied to business reports, trending topics on social media platforms, or even reviews in e-commerce websites to extract latent themes associated with it. For illustration purposes, the custom dataset to be used in this project is built on articles concerned with Digital Economy. Assume, that an entity is interested to understand what does the term mean, and how the entity could be benefited from it. The method to build a topic model will be divided into the following steps, beginning with data cleaning, visual exploratory analysis with WordCloud, preparation of corpus to build a topic model, and finally, represent the themes with an interactive visualization. This project will lightly cover the basic conceptual framework of topic modeling as a text mining tool (and where can it be used), along with statistical measures to estimate the goodness of a topic model.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/izvnn9/introduction
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for individuals seeking insights from business documents or online conversations
Ideal for professionals in marketing, data analysis, or business intelligence who want to enhance their topic modeling skills
Accessible to learners with no prior knowledge of topic modeling, making it suitable for beginners
Provides a practical approach to topic modeling through a hands-on project
Covers both theoretical foundations and practical implementation of topic modeling
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Reviews summary
Advanced topic modeling for business
This 2-hour long project-based course is suitable for learners based in North America who have some experience with topic modeling. The course is suitable for learners who are experienced with topic modeling and working with code, but may not be appropriate for beginners.
Hands-on experience with topic modeling.
"In this 2-hour long project-based course..."
Visualization code needs updating
"The visualisation codes need to be updated as the module pyLDAvis.gensim is no longer available in the newer version."
May be too advanced for beginners
"Very rich content, not easy for a beginner !"
Code is not always explained well
"she is just typing the code without explaining anything"
"Very little explanation is given of the function/libraries used or of their parameters"
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 Introduction to Topic Modeling for Business with these
activities:
Review the classic text on Topic Modeling
Show steps
Learning the foundational concepts of topic modeling from this comprehensive book will help you grasp the building blocks of the topic model you will build in this course.
View
Taming Text: How to Find, Organize, and...
on Amazon
Show steps
-
Read the first three chapters of the book to build foundational understanding of topic modeling
-
Apply the learned concepts to analyze a small corpus of your choice
Practice NLP techniques in the context of topic modeling
Show steps
By practicing NLP techniques, you will strengthen your understanding of how topic models are built and how they can be used to extract meaningful insights from text data.
Browse courses on
Natural Language Processing
Show steps
-
Practice text cleaning and preprocessing
-
Practice identifying stop words and stemming words
-
Practice creating bag-of-words and TF-IDF matrices
Attend an online Meetup or conference on topic modeling
Show steps
Participating in a Meetup or conference provides an opportunity to connect with other professionals in the field, learn about the latest advancements in topic modeling, and gain valuable insights.
Browse courses on
Topic Modeling
Show steps
-
Find an upcoming Meetup or conference on topic modeling
-
Register for the event and attend
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a project to demonstrate the power of topic modeling
Show steps
By building a project that showcases the insights gained from topic modeling, you will have a deeper understanding of its real-world applications.
Browse courses on
Text Mining
Show steps
-
Identify a business problem that can be solved using topic modeling
-
Create a custom corpus to use to train the topic model
-
Build and evaluate multiple topic models to find the optimal number of topics
-
Present project findings in a presentation deck
Create a data visualization to present the results of your topic modeling project
Show steps
Crafting a data visualization will reinforce your understanding of the topics extracted from the text data, making the insights more accessible and impactful.
Browse courses on
Data Visualization
Show steps
-
Choose an appropriate visualization technique to represent the topics
-
Use a data visualization tool to create the visualization
Start a personal project to apply topic modeling to a real-world dataset
Show steps
Initiating a personal project will enable you to explore topic modeling beyond the course, fostering a deeper understanding and practical application of the concepts.
Browse courses on
Topic Modeling
Show steps
-
Identify a dataset that aligns with your interests or professional goals
-
Clean and preprocess the dataset
-
Build and evaluate multiple topic models to extract meaningful insights
Explore advanced tutorials on topic modeling with Python
Show steps
Engaging with advanced tutorials will supplement your course learning and provide additional insights into the intricacies of topic modeling using Python.
Browse courses on
Python
Show steps
-
Find tutorials that cover advanced topic modeling techniques like hierarchical topic modeling or dynamic topic modeling
-
Follow the tutorials and implement the techniques in your own Python code
Attend a workshop on topic modeling to gain hands-on experience
Show steps
A workshop will provide you with practical experience in applying topic modeling techniques, complementing the theoretical knowledge gained in the course.
Browse courses on
Topic Modeling
Show steps
-
Find a workshop on topic modeling that aligns with your skill level
-
Sign up for the workshop and attend
Review the classic text on Topic Modeling
Show steps
Learning the foundational concepts of topic modeling from this comprehensive book will help you grasp the building blocks of the topic model you will build in this course.
View
Taming Text: How to Find, Organize, and...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book to build foundational understanding of topic modeling
- Apply the learned concepts to analyze a small corpus of your choice
Practice NLP techniques in the context of topic modeling
Show steps
By practicing NLP techniques, you will strengthen your understanding of how topic models are built and how they can be used to extract meaningful insights from text data.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Practice text cleaning and preprocessing
- Practice identifying stop words and stemming words
- Practice creating bag-of-words and TF-IDF matrices
Attend an online Meetup or conference on topic modeling
Show steps
Participating in a Meetup or conference provides an opportunity to connect with other professionals in the field, learn about the latest advancements in topic modeling, and gain valuable insights.
Browse courses on
Topic Modeling
Show steps
Show steps
Show steps
- Find an upcoming Meetup or conference on topic modeling
- Register for the event and attend
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a project to demonstrate the power of topic modeling
Show steps
By building a project that showcases the insights gained from topic modeling, you will have a deeper understanding of its real-world applications.
Browse courses on
Text Mining
Show steps
Show steps
Show steps
- Identify a business problem that can be solved using topic modeling
- Create a custom corpus to use to train the topic model
- Build and evaluate multiple topic models to find the optimal number of topics
- Present project findings in a presentation deck
Create a data visualization to present the results of your topic modeling project
Show steps
Crafting a data visualization will reinforce your understanding of the topics extracted from the text data, making the insights more accessible and impactful.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose an appropriate visualization technique to represent the topics
- Use a data visualization tool to create the visualization
Start a personal project to apply topic modeling to a real-world dataset
Show steps
Initiating a personal project will enable you to explore topic modeling beyond the course, fostering a deeper understanding and practical application of the concepts.
Browse courses on
Topic Modeling
Show steps
Show steps
Show steps
- Identify a dataset that aligns with your interests or professional goals
- Clean and preprocess the dataset
- Build and evaluate multiple topic models to extract meaningful insights
Explore advanced tutorials on topic modeling with Python
Show steps
Engaging with advanced tutorials will supplement your course learning and provide additional insights into the intricacies of topic modeling using Python.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find tutorials that cover advanced topic modeling techniques like hierarchical topic modeling or dynamic topic modeling
- Follow the tutorials and implement the techniques in your own Python code
Attend a workshop on topic modeling to gain hands-on experience
Show steps
A workshop will provide you with practical experience in applying topic modeling techniques, complementing the theoretical knowledge gained in the course.
Browse courses on
Topic Modeling
Show steps
Show steps
Show steps
- Find a workshop on topic modeling that aligns with your skill level
- Sign up for the workshop and attend
Career center
Learners who complete Introduction to Topic Modeling for Business will develop knowledge and skills
that may be useful to these careers:
Digital Marketing Manager
Digital Marketing Manager
A Digital Marketing Manager plans and executes digital marketing campaigns to achieve business goals. This course helps build a foundation for Digital Marketing Managers by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of digital marketing problems, such as analyzing customer feedback, identifying trends in social media data, and understanding the competitive landscape. This course can help Digital Marketing Managers develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Customer Success Manager
Customer Success Manager
A Customer Success Manager is responsible for the satisfaction and retention of customers. This course helps build a foundation for Customer Success Managers by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of customer success problems, such as analyzing customer feedback, identifying trends in customer behavior, and understanding the competitive landscape. This course can help Customer Success Managers develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Market Researcher
Market Researcher
A Market Researcher collects and analyzes data to understand customer needs and preferences. This course helps build a foundation for Market Researchers by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of market research problems, such as analyzing customer feedback, identifying trends in social media data, and understanding the competitive landscape. This course can help Market Researchers develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Social Media Manager
Social Media Manager
A Social Media Manager is responsible for the development and execution of social media strategies. This course helps build a foundation for Social Media Managers by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of social media problems, such as analyzing customer feedback, identifying trends in social media data, and understanding the competitive landscape. This course can help Social Media Managers develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Content Strategist
Content Strategist
A Content Strategist develops and executes content strategies to achieve business goals. This course helps build a foundation for Content Strategists by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of content strategy problems, such as identifying content gaps, optimizing content for search engines, and understanding the competitive landscape. This course can help Content Strategists develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Sales Manager
Sales Manager
A Sales Manager is responsible for the development and execution of sales strategies. This course helps build a foundation for Sales Managers by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of sales problems, such as analyzing customer feedback, identifying trends in sales data, and understanding the competitive landscape. This course can help Sales Managers develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Business Analyst
Business Analyst
A Business Analyst identifies and solves business problems using data and analysis. This course helps build a foundation for Business Analysts by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of business problems, such as analyzing customer feedback, identifying trends in market research data, and understanding the competitive landscape. This course can help Business Analysts develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Data Analyst
Data Analyst
A Data Analyst uses data to solve business problems and improve decision-making. This course helps build a foundation for Data Analysts by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of business problems, such as analyzing customer reviews, identifying trends in social media data, and understanding the competitive landscape. This course can help Data Analysts develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. This course helps build a foundation for Product Managers by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of product management problems, such as identifying customer needs, understanding the competitive landscape, and developing new product features. This course can help Product Managers develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Operations Manager
Operations Manager
An Operations Manager is responsible for the day-to-day operations of a business. This course helps build a foundation for Operations Managers by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of operations problems, such as analyzing customer feedback, identifying trends in operational data, and understanding the competitive landscape. This course can help Operations Managers develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for the analysis and interpretation of financial data. This course helps build a foundation for Financial Analysts by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of financial analysis problems, such as analyzing financial reports, identifying trends in financial data, and understanding the competitive landscape. This course can help Financial Analysts develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems and improve decision-making. This course may be useful for Data Scientists by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of data science problems, such as analyzing customer feedback, identifying trends in data, and understanding the competitive landscape. This course can help Data Scientists develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models. This course may be useful for Machine Learning Engineers by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of machine learning problems, such as analyzing customer feedback, identifying trends in data, and understanding the competitive landscape. This course can help Machine Learning Engineers develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful for Software Engineers by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of software engineering problems, such as analyzing customer feedback, identifying trends in data, and understanding the competitive landscape. This course can help Software Engineers develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course may be useful for Quantitative Analysts by providing an understanding of topic modeling, a technique for identifying and extracting patterns from large amounts of text data. Topic modeling can be applied to a variety of quantitative analysis problems, such as analyzing financial reports, identifying trends in financial data, and understanding the competitive landscape. This course can help Quantitative Analysts develop the skills needed to effectively use topic modeling to gain valuable insights from text data.
For more career information including salaries, visit:
OpenCourser.com/course/izvnn9/introduction
Reading list
We've selected 13 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 Modeling for Business.
Provides a comprehensive overview of statistical learning, a field that deals with the design and development of algorithms that can learn from data. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of topic models, a powerful statistical technique for discovering hidden themes in text data. It covers the mathematical foundations of topic models, as well as practical applications in a variety of fields.
Save
Provides a comprehensive overview of deep learning, a field of machine learning that deals with the design and development of algorithms that can learn from data. It covers topics such as deep learning models, deep learning architectures, and deep learning applications.
Save
Provides a comprehensive overview of big data analytics, a field that deals with the analysis of large datasets. It covers topics such as data storage, data processing, and data visualization.
Provides a comprehensive overview of data science for business. It covers topics such as data collection, data cleaning, data analysis, and data visualization.
Provides a practical introduction to advanced analytics with Spark, a popular open-source big data processing framework. It covers topics such as data engineering, machine learning, and data visualization.
Provides a comprehensive overview of data mining techniques and their applications in business. It covers topics such as data preparation, feature selection, model building, and evaluation. It valuable resource for anyone who wants to learn more about data mining and its applications in business.
Provides a practical introduction to Python for data analysis. It covers topics such as data cleaning, data manipulation, data analysis, and data visualization.
Save
Provides a practical introduction to data visualization, a field that deals with the graphical representation of data. It covers topics such as data visualization basics, data visualization techniques, and data visualization tools.
Provides a practical introduction to machine learning with R, a popular open-source statistical programming language. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a practical introduction to machine learning, a field of computer science that deals with the design and development of algorithms that can learn from data. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a practical introduction to machine learning, a field of computer science that deals with the design and development of algorithms that can learn from data. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a practical introduction to natural language processing, a field of computer science that deals with the interaction between computers and human (natural) languages. It covers topics such as text preprocessing, tokenization, stemming, and parsing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/izvnn9/introduction
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Barsha Saha
Good to know
Suitable for individuals seeking insights from business documents or online conversations
Ideal for professionals in marketing, data analysis, or business intelligence who want to enhance their topic modeling skills
Accessible to learners with no prior knowledge of topic modeling, making it suitable for beginners
Provides a practical approach to topic modeling through a hands-on project
Covers both theoretical foundations and practical implementation of topic modeling
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