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Advanced Data Analytics


3.0 Credit, 3 Lecture, 0 Lab


Advanced machine-learning algorithms for predicting numeric and categorical outcomes. Data dimension reduction. Forecasting. Clustering and segmentation, anomaly detection, and model quality evaluation.

Know Strengths & Limitations of Data Mining Methods

To gain a working knowledge of the strengths and limitations of modern data mining methods (algorithms).

Identify and Adress Problems with Data Mining Methods

To learn to identify problems that can profitably be addressed via data mining methods.


Inform a meaningful problem space by using a real-world dataset

Learn to Set Up Data for Experiments

To learn how to set up data for data mining experiments.


Collaborate effectively in a team setting using a machine learning pipeline that produces value in the learning space

Identify Appropriate Methods

For a given problem, to be able to identify what methods are appropriate.

Model choice

Effectively defend model choice that meets established criteria to both data-savvy and non-technical stakeholders

Proficiency in Evaluating and Comparing Model Performance

To become proficient in methods of evaluating and comparing model performance.


Discuss sources of bias, performance issues, and ethical and regulatory considerations and how these sources will affect stakeholders and others