QL 1.1 - Mathematical Thinking and Quantitative Reasoning

Course Description

The goal of Mathematical Thinking and Quantitative Reasoning is to help students develop intellectual mathematical abilities as well as see mathematics as a method to address current problems including social problems. Understanding how people learn from data will change the way students think about the world. Students will use state of the art data exploratory methods to analyze real data. Topics will include modern statistical reasoning, statistical modeling, linear regression, statistical inference, logarithmic and exponential modeling, and the conditions for inference which must hold in order to use statistical procedures. Other topics include logical reasoning, analysis of arguments, probability.

Prerequisites:

None

Course Specifics

Course Delivery: online | 7 weeks | 14 sessions

Course Credits: 3 units | 37.5 Seat Hours | 75 Total Hours

Learning Outcomes

By the end of the course, you will be able to ...

1. Develop capacities of quantitative reasoning to interpret, analyze, apply, and explain data (information) presented in mathematical forms.
2. Recognize and evaluate assumptions in estimation, modeling, and data analysis.
3. Calculate mathematical problems and communicate quantitative evidence in support of an argument.
4. Apply quantitative reasoning skills using data analysis, probability, and statistics through examples related to current world debates, inquiries, and problems.
5. Gain and act with confidence to work through problems using quantitative reasoning.

Critical Skills

Explain, use, and implement the following statistical tools using Python

1. Use Mean, Mode, Median, Range, and Standard Deviation to describe a data series
2. Diagram and use the Histogram, PDF, and CDF of a data series
3. Calculate the Correlation and Covariance of two features of a data set
4. Calculate the Probability and Conditional Probability of an event or events
5. Define what vectors and matrixes are, and complete matrix-vector multiplication
6. Explain what derivatives and partial derivates are and use them to perform linear regression on a dataset.

Schedule

Course Dates: Monday, January 20 – Wednesday, March 4, 2020 (7 weeks)

Class Times: Monday and Wednesday at 2:30–5:15pm (13 class sessions)

Assignment Schedule

Evaluation

To pass this course you must meet the following requirements:

• Complete all required assignments, including Homework, the Data Analysis Project, and the Math & Social Issues Project
• Pass all projects according to the associated project rubric
• Pass the final summative assessment according to the rubric as specified in this class
  • This will be further explained in the study guide
• Actively participate in class and abide by the attendance policy
• Make up all classwork from all absences

Information Resources

Any additional resources you may need (online books, etc.) can be found here. You can also find additional resources through the library linked below:

• make.sc/library

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