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Advanced Data Analytics
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.
Dataset
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
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.
Sources
Discuss sources of bias, performance issues, and ethical and regulatory considerations and how these sources will affect stakeholders and others