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.
None
Course Delivery: online | 7 weeks | 13 sessions
Course Credits: 3 units | 37.5 Seat Hours | 75 Total Hours
By the end of the course, you will be able to ...
- Develop capacities of quantitative reasoning to interpret, analyze, apply, and explain data (information) presented in mathematical forms.
- Recognize and evaluate assumptions in estimation, modeling, and data analysis.
- Calculate mathematical problems and communicate quantitative evidence in support of an argument.
- Apply quantitative reasoning skills using data analysis, probability, and statistics through examples related to current world debates, inquiries, and problems.
- Gain and act with confidence to work through problems using quantitative reasoning.
Explain, use, and implement the following statistical tools using Python
- Use Mean, Mode, Median, Range, and Standard Deviation to describe a data series
- Diagram and use the Histogram, PDF, and CDF of a data series
- Calculate the Correlation and Covariance of two features of a data set
- Calculate the Probability and Conditional Probability of an event or events
- Define what vectors and matrixes are, and complete matrix-vector multiplication
- Explain what derivatives and partial derivates are and use them to perform linear regression on a dataset.
Course Dates: Monday, May 31 – Friday, July 16, 2021 (7 weeks)
Class Times: Monday, Wednesday, Friday at 9:30am–11:15am (19 class sessions)
| Class | Date | Topics |
|---|---|---|
| - | Mon, May 31 | No Class - Memorial Day |
| 1 | Wed, June 2 | Data Storytelling & Journalism |
| 2 | Fri, June 4 | Spreadsheets: Financial Models & Stock Portfolio |
| 3 | Mon, June 7 | Statistical Vs. Non Statistical Questions |
| 4 | Wed, June 9 | CSV Project Lab Day |
| 5 | Fri, June 11 | Intro to Jupyter Notebooks & Kick off Titanic |
| 6 | Mon, June 14 | Descriptive Stats: Mean, Median, Mode |
| 7 | Wed, June 16 | Percentile & Correlation |
| 8 | Fri, June 18 | Data Journalism |
| 9 | Mon, June 21 | 3 Types of Bias |
| 10 | Wed, June 23 | How to Lie with Data |
| 11 | Fri, June 25 | Scientific Fabilism & Falsifiability |
| 12 | Mon, June 28 | The Replication Crisis & Intro to Experimental Design |
| 13 | Wed, June 30 | Confounders & Null Hypothesis (p-values) |
| 14 | Fri, July 2 | Intro to Probability & Monte Carlo Models |
| - | Mon, July 5 | No Class - Independence Day Observed |
| 15 | Wed, July 7 | Predictive Stats: Linear Regression |
| 16 | Fri, July 9 | Linear Regression & Topical Blog Post Work Day |
| 17 | Mon, July 12 | Topical Blog Post Writing Day |
| 18 | Wed, July 14 | Topical Blog Post Draft Exchange & Feedback |
| 19 | Fri, July 16 | Unconference |
See more at: make.sc/ql-t5
To pass this course you must meet the following requirements:
- Complete all required assignments
- Actively participate in class and abide by the attendance policy.
- Complete all work from any absences
- "Elementary Statistics: A Step By Step Approach" by Allan Bluman.
- "The Manga Guide to Statistics" by Shin Takahashi, Co Ltd Trend.
- "The Manga Guide to Regression Analysis" by Shin Takahashi, Iroha Inoue, Co Ltd Trend.
- Khan Academy
Any additional resources you may need (online books, etc.) can be found here. You can also find additional resources through the library linked below:
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