MIE 1626 is an intensive course designed to actively engage students and equip them with fundamental skills necessary for practical data science projects. The curriculum covers combining, preprocessing, and cleaning data, exploratory data analysis, and visualizing data using Python and its libraries. The course also emphasizes predictive modeling, data-driven analysis, statistical learning methods, and the application of machine learning techniques like tree-based models and support vector machines. Students will learn to implement advanced regression models, analyze networked data, and enhance the interpretability of black-box models using ML explainability methods.

• Understand data as the foundation of quantitative analysis and reasoning.
• Acquire skills in modeling, analyzing data, and making inferences using advanced statistical methods.
• Understand and apply the statistical foundations of data science and machine learning.
• Replace black-box models with explainable and justifiable predictive models.
• Implement principles, methods, and techniques using Python and its libraries across a range of applications.
• Interpret results of statistical analyses and detect false narratives resulting from improper analyses.
• Plan, execute, and deliver successful data science projects using scientifically sound methods.

• Weekly Lectures: Emphasize theoretical understanding and application of data science methods.
• Practical Sessions: Include tutorials and Q&A sessions to provide hands-on experience with Python and relevant libraries.

• Projects: Two major projects that require individual effort to apply course concepts to real-world data scenarios.
• Reading Assignments: Eight quizzes based on the reading material to reinforce comprehension and application of the concepts discussed in lectures.
• Discussion and Engagement: Utilization of Piazza for course-related discussions to foster a collaborative learning environment.

• Main channel of communication through Piazza, encouraging interactions among students and between students and instructors.
• Course materials, announcements, and resources are accessible on Quercus and through selected online links provided during the course.

• The course features structured deadlines for assignments, projects, and exams to ensure consistent engagement and progress throughout the term.
• Assessments are designed to evaluate understanding and application of the course material in both theoretical and practical contexts.