Beginning in late March, student-consultants in groups of four will spend ~200 hours per team performing a thorough investigation of data sets to address a business problem or opportunity of your choosing.

You can provide internal data, or ask that students leverage open source data to address the problem.

Tools such as Python and Tableau will be leveraged for data modelling, machine learning, and visualization.

When they start this course, students will have learned advanced techniques in machine learning, programming, and big data tools, and will be able to leverage open-source tools such as python or R, and machine learning libraries such as scikit-learn and Keras.

Students will execute the following steps during this course to solve a machine learning problem and provide a solution to your organization:

1. Research and frame an analytical problem, and implement a machine learning solution to the challenge faced by your organization.

2. Manage the workflow for a machine learning pipeline/solution based on the best in-class practices in industry, taught during the course and tracked through the project.

3. Author a technical document/report for your organization that gives an in-depth overview into the problem and the technical solution

Potential business challenges/opportunities might include, but are not limited to:

• Predicting a customer's lifetime value
• Text or numeric data classification to help solve a business problem
• Recommendation engines
• Fraud detection
• ...and many more