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Unsupervised Text Classification for Marketing Analytics
  • Level Professional
  • المدة 13 ساعات hours
  • الطبع بواسطة University of Colorado Boulder
  • Offered by

عن

Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment. Files are stored in Google Drive. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

الوحدات

Introduction

    • 2 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself!

2 Readings

  • Earn Academic Credit for your Work!
  • Course Support

Lectures

    • 1 Videos
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1 Videos

  • Topic Modeling Lecture 1

Reference Docs

    • 2 Readings
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2 Readings

  • Introduction to Using Google Colab for this Course
  • Dr. Vargo's Topic Modeling Approach to YikYak Data

Assessments

    • 1 Programming
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1 Programming

  • Homework 1: Segmenting by Sentiment

Lectures

    • 2 Videos
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2 Videos

  • Topic Modeling Lecture 2
  • Topic Modeling Lecture 3

Reference Docs

    • 1 Readings
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1 Readings

  • Dr. Vargo’s Chapter on How Topic Modeling Compares with Lexicon-based Approaches

Assessments

    • 1 Quiz
    • 1 Programming
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1 Programming

  • Homework 2: Build a Topic Model

1 Quiz

  • Topic Modeling Quiz

Lectures

    • 2 Videos
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2 Videos

  • Topic Modeling Lecture 4
  • Topic Modeling Lecture 5

Lecture Notebooks

    • 1 Readings
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1 Readings

  • Lecture Notebook Links

Assessments

    • 1 Readings
    • 1 Quiz
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1 Readings

  • Coding Lab 1: Segmenting Data

1 Quiz

  • Lab 1 Quiz

Lectures

    • 2 Videos
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2 Videos

  • Topic Modeling Lecture 6
  • Topic Modeling Lecture 7

Lecture Notebooks

    • 1 Readings
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1 Readings

  • Lecture Notebook Links

Assessments

    • 1 Readings
    • 1 PeerReview
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1 Peer Review

  • Lab 2 Peer Review: Submit Visualization Screenshot

1 Readings

  • Lab 2: Classification and Visualization

Lectures

    • 3 Videos
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3 Videos

  • Topic Modeling Lecture 8
  • Topic Modeling Lecture 9
  • Topic Modeling Lecture 10

Lecture Notebooks

    • 1 Readings
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1 Readings

  • Lecture Notebook Links

Reference Docs

    • 1 Readings
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1 Readings

  • Papers (1, 2, and 3) on Topic Modeling Fit Statistics

Assessments

    • 1 Readings
    • 1 PeerReview
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1 Peer Review

  • Lab 3 Peer Review: BERTopic Visualizations

1 Readings

  • Lab 3: Topic Modeling with BERTopic

Auto Summary

Unlock marketing insights with "Unsupervised Text Classification for Marketing Analytics." This professional-level course, instructed by Coursera, focuses on leveraging unsupervised deep learning for analyzing vast marketing data. Dive into real-world datasets with Python tutorials using Jupyter Notebooks and Google Colab. Spanning 780 minutes, it's part of CU Boulder's MS-DS program, ideal for those with backgrounds in computer science, information science, mathematics, or statistics. Available under the Starter subscription, this course is perfect for aspiring data scientists and marketing analysts.

Chris J. Vargo
Instructors

Chris J. Vargo

Scott Bradley
Instructors

Scott Bradley