Advanced Data Analytics for Organizations

Douglas Dean, PhD

Choosing the right machine learning algorithm matters as much as running it, and this resource teaches students how to choose. Rather than treating algorithms as black boxes, students examine how each one works so they can see its strengths and weaknesses and know which problems it suits best. They build hands-on experience with the major techniques, including decision trees, multiple and logistic regression, time series forecasting, k-nearest neighbors, neural networks, gradient boosting, random forests, cluster analysis, and support vector machines, while learning to avoid overfitting and detect anomalies. Students work across Python, Jupyter Notebook, Google Colab, SAS JMP, Excel, and Azure Machine Learning Studio, then bring it all together in a capstone project. They leave with the knowledge to apply machine learning to real organizational problems.