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.

NOTE: This course is primarily intended and designed for MSIT students. Other Heinz students in full-time programs should enroll in 94-819 Data Analytics with Tableau and will not be permitted to enroll in this course.

Learning Outcomes

-    Be able to produce and comprehend commonly used machine learning techniques with Tableau Desktop.
-    Understand the advantages and disadvantages of multiple machine learning techniques.
-    Be able to compare the utility of different methods through exercises, homeworks, and a final project.
-    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.
-    Be able to choose an appropriate model/s for a dataset and evaluate the performance and reliability of such model/s.
-    Be able to apply methods to real-world data.
-    Develop machine learning models that yield insights for different stakeholders through Tableau sheets, stories, and dashboards.