Data, Computing, and Quantitative Reasoning

Student Learning Outcomes, Goals, Objectives:

Students successfully completing this course will be able to:

SLO1: Interrelate real-world information with mathematical forms.	●        Understand different types of data, storage, programming, and computational techniques. ●        Recognize the need for algorithmic efficiency in large datasets.
SLO2: Justify conclusions based on quantitative arguments.	●        Apply mathematical / statistical models to data. ●        Evaluate and interpret the results of models.
SLO3: Communicate the quantitative evidence of the argument.	●        Summarize and explain the results obtained from the analysis.

The formative and summative assessments of the course are as follows:

 

SLO 1: Interrelate real-world information with mathematical forms.

1. Understand different types of data, storage, programming, and computational techniques.

2. Recognize the need for algorithmic efficiency in large datasets.

 

Formative Assessment:

1. Quizzes: 2-3 quizzes to test the recall of students in theories related to the types of datasets, data descriptions, storage methods, and algorithms forming the basis of computational methods.

2. Data-Programming Assignments: 2 guided programming assignments. The first will evaluate the student's ability to create identifiers, to create mathematical expressions, to format output, and to use built-in functions. Second, evaluates programming for input – output of data to a program and utilization of a simple data structure.
   

SLO 2: Justify conclusions based on quantitative arguments.

1. Apply mathematical / statistical models to data.

2. Evaluate and interpret the results of models.

 

 Formative Assessment:

1. Quizzes: 2 quizzes to test the recall of students in statistical concepts, such as, understanding the center of data variables (mean, median, mode), hypothesis formulation, testing, and interpretation (p-values), and estimating data.

2. Programming Assignment: 2 guided programming assignments. First, provide a simple toy dataset to understand the statistical properties. Second, utilizes the same dataset to develop hypothesis tests on it. Students will submit results as code to be evaluated.

Summative Assessment:

1. Team Mini Project: Mini projects will form teams into groups, where they will be working real-world datasets. There will be 2 mini-project progress presentations. First, students present an exploratory analysis of the data (evaluate statistical properties). Second presentation evaluates hypothesis formulation and testing. Peer-evaluation (on hackathons and project progress) will also be requested to assess individual student, team communication, and overall project performance.

 

SLO 3: Communicate the quantitative evidence of the argument.

1. Summarize and explain the results obtained from the analysis.

Formative Assessment:

1. Writing Assignment: Students will be guided on how to create a short paper that will communicate quantitative reasoning results. Students will use programming assignments in SLO2 to form the basis of the writing task.

 

Summative Assessment:

1. Team Mini Project Presentation: Evaluation of mini-projects based on final presentation. Students will be evaluated on oral presentation skills, formation of quantitative arguments, and summarization of results.

2. Term paper:  Term paper will evaluate the writing capability of students to present the quantitative observations in their mini projects.

Both the presentation and paper will have a peer evaluation component.

Course Grading Information:

Activity/Performance Measure	Percentage/Points
Class Participation	10%
Quizzes	40%
Assignments	20%
Mini Projects	30% (Project Presentations: 15%, Term Paper: 15%)

Attendance/Participation:

You are expected to attend this class in person. Your grade will be based on your participation in the course via in-class quizzes.

GRADING SCALE:

Grade	Point/Percentage Total
A	94-100
A-	90-93
B+	87-89
B	84-86
B-	80-83
C+	77-79
C	74-76
C-	70-73
D+	67-69
D	64-66
D-	60-63
F	< 59

Course Materials Purchased by the Students:

No course materials are required.