BUS 6131 - Predictive Analytics

3 lecture hours 0 lab hours 3 credits

Course Description
Within this course, students learn to identify appropriate tools and/or combinations of tools to address decision-making scenarios within an organization. Special attention is paid to the application of analytics to predict future trends and probabilities. Students will learn the current tools and methods for predictive analytics, as well as the limitation of these methods. Ongoing focus is placed on communicating the interpretation of analytical results to a range of audiences. The use of the Python programming language is emphasized to expose the student to contemporary analytic processing environments on computing clusters. Machine learning, including supervised and unsupervised learning fundamentals are used to demonstrate the power of statistical learning on prediction in the era of Big Data.
Prereq: BUS 6121
Note: None

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Evaluate, propose, and apply appropriate predictive analytics techniques for a given decision-making scenario
Illustrate the benefits of business analytics for individuals, stakeholders, and organizations
Develop and deliver clear and persuasive messages based on the outcomes of analytics projects
Build decision models using supervised and unsupervised learning algorithms
Implement predictive models using supervised learning methods of linear, polynomial and logistic regression, support vector machines, decision trees and forests
Implement predictive models using unsupervised learning methods of clustering, k-means analysis, hierarchical clustering, and DBSCAN

Course Topics

Python language environments
- Installation, configuration, and maintenance of Python environments using shared environments (Rosie, Google CoLab, etc.) and single-user environments (Anaconda) with Jupyter Notebooks
Python language review
- Expressions, operators (arithmetic, logical and relational), data types (None, integers, floating point numeric and object), compound expressions leading to statements
- Flow of control in Python (sequential, branching (if/elif/else), iteration (for loops, while loops, ranges, else parts to loops, iteration over collections), functions and lambda expressions). Points of syntax - importance of colons and tab stops
- Collections in Python (lists, tuples, dictionaries, sets) indexing and slicing, iteration over collections using comprehensions, iterable objects and generators
- Classes and object-oriented programming in Python (instance variables, methods, operator overloading, inheritance, polymorphism)
- Numeric Python (NumPy), Scientific Python (SciPy) and Pandas Libraries
Machine learning basics
- Linear regression fundamentals, simple and multivariate linear models
- Closed form equation solution for linear regression on small data sets
- Problems related to underfitting and overfitting
- Python Libraries for machine learning and scikit-learn functions
Multivariate regression models
- Polynomial regression and curve fitting techniques
- Model evaluation in regression models
- Train and test, training accuracy and out-of-sample accuracy, and K-fold cross-validation concept
- Regression evaluation metrics
- Logistic regression for binary decision-making
Support Vector Machines (SVMs)
- Classification with SVM (Support Vector Machines)
- Data transformation
- Using SVM to find the hyperplane
- Advantages and disadvantages of SVM
- SVM applications
Decision trees
- Decision tree classifiers
- Atribute selection in tree building
- Entropy measure
- Ensemble learning with random forests
Unsupervised learning
- Clustering, k-nearest neighbors, k-means clustering
- Similarity/dissimilarity metrics
- Hierarchical clustering
- Agglomerative clustering applications
- Density Based Spatial Application with Noise (DBSCAN)

Coordinator
Dr. Ernesto Guerra-Vallejos

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