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Machine Learning with Tableau

95-854

Units: 6

Description

The main purpose of this course is to provide students with a basic understanding of supervised and unsupervised machine learning models. 

This course will face students with real examples and real-world data, as an increasing number of organizations nowadays collect data to support their decision-making process. Learning from data can enable us to better: evaluate sales decisions, make a medical diagnosis, monitor the reliability of IT systems, p market segmentation, improve the success of marketing campaigns, and much, much more.

Learning Outcomes

By the end of this class, students will learn:

1. Be able to produce and comprehend commonly used machine learning techniques with Tableau Desktop.
2. Understand the advantages and disadvantages of multiple machine learning techniques. 
3. Be able to compare the utility of different methods through exercises, homeworks, and a final project.
4. Understand the concepts behind feature engineering, and be able to place them into practice through different types of data, using both Tableau Prep and Tableau Desktop.
5. Be able to choose an appropriate model/s for a dataset and evaluate the performance and reliability of such model/s.
6. Be able to apply methods to real-world data.
7. Develop machine learning models that yield insights for different stakeholders through Tableau sheets, stories, and dashboards.