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Course Description

With your text data effectively cleaned and primed for an algorithm, you're now poised to put it into practical use. While you've created Latent Dirichlet Allocation (LDA) models in prior courses, you've done so using default settings, which may not be ideal for the specific data at hand. To fully ready your models for active portfolio management, you need to train and evaluate them against an industry standard. Only with this assurance can you make associations that are relevant within an investment context, enabling you to construct portfolios of companies that align with a desired industry sector or theme.

In this course, you'll train a variety of LDA topic models in an iterative process to enhance their performance. You'll evaluate their alignment with widely accepted industry classifications to compile lists of comparable companies relevant to a specific investment theme. The process will range from fine-tuning various hyperparameters to optimize the LDA algorithm's learning curve to calculating distance metrics for comparable companies to ascertain their topic similarity with respect to an investment benchmark.

As you progress through the course, you'll conduct an array of comparative analyses to discern the strengths and weaknesses of the LDA approach. Recognizing these aspects is crucial when it comes to the construction and management of investment portfolios. By the end of the course, you'll be adept at training, refining, and applying LDA models, paving the way for smarter, data-driven investment decisions.

The following course is required to be completed before taking this course:

  • Preparing Data for Natural Language Processing
  • Cleaning Text Data to Optimize Model Performance

Faculty Author

Chris Meredith

Benefits to the Learner

  • Tune hyperparameters to optimize LDA topic model performance
  • Compare LDA topic model groupings with an industry standard
  • Map LDA topics to companies for portfolio construction

Target Audience

  • Financial analysts
  • Quant finance investors
  • Market analysts and business analysts
  • Data scientists
  • Software engineers

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in
Type
2 week
Dates
Dec 11, 2024 to Dec 24, 2024
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note that the content in the NLP for Finance course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order; for example, JCB661 prior to JCB662, JCB662 prior to JCB663, etc.
  • In order to be successful in this program, students should have a working knowledge of Python programming as well as sufficient English language fluency as some aspects of the data cleaning have relations to English.
Type
2 week
Dates
Feb 05, 2025 to Feb 18, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note that the content in the NLP for Finance course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order; for example, JCB661 prior to JCB662, JCB662 prior to JCB663, etc.
  • In order to be successful in this program, students should have a working knowledge of Python programming as well as sufficient English language fluency as some aspects of the data cleaning have relations to English.
Type
2 week
Dates
Mar 05, 2025 to Mar 18, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note that the content in the NLP for Finance course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order; for example, JCB661 prior to JCB662, JCB662 prior to JCB663, etc.
  • In order to be successful in this program, students should have a working knowledge of Python programming as well as sufficient English language fluency as some aspects of the data cleaning have relations to English.
Type
2 week
Dates
May 28, 2025 to Jun 10, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note that the content in the NLP for Finance course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order; for example, JCB661 prior to JCB662, JCB662 prior to JCB663, etc.
  • In order to be successful in this program, students should have a working knowledge of Python programming as well as sufficient English language fluency as some aspects of the data cleaning have relations to English.
Type
2 week
Dates
Aug 20, 2025 to Sep 02, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note that the content in the NLP for Finance course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order; for example, JCB661 prior to JCB662, JCB662 prior to JCB663, etc.
  • In order to be successful in this program, students should have a working knowledge of Python programming as well as sufficient English language fluency as some aspects of the data cleaning have relations to English.
Type
2 week
Dates
Nov 12, 2025 to Nov 25, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note that the content in the NLP for Finance course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order; for example, JCB661 prior to JCB662, JCB662 prior to JCB663, etc.
  • In order to be successful in this program, students should have a working knowledge of Python programming as well as sufficient English language fluency as some aspects of the data cleaning have relations to English.
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