Course Syllabus
Course: MATH 1120
Division: Natural Science and MathDepartment: Mathematics
Title: Introduction to Data Science
Semester Approved: Fall 2020
Five-Year Review Semester: Fall 2025
End Semester: Spring 2026
Catalog Description: Students will learn about the interaction between statistical and mathematical reasoning and their application to the collection, preparation, and presentation of data. In addition to traditional structured data analysis, this course will also consider unstructured data such as natural language and image processing. Access to a computer is required.
This course fulfills the Math GE requirement. The course will also serve as a prerequisite to later data science courses, i.e., Math 2080/3080. The course is designed to support students interested in pursuing data heavy degrees/careers.
General Education Requirements: Quantitative Literacy (MA)
Semesters Offered: TBA
Credit/Time Requirement: Credit: 3; Lecture: 3; Lab: 0
Prerequisites: Math 850 or Math 1010 with a C or better course grade, ACT math score 22 or higher or appropriate placement test score.
Justification: The tech industry is the fastest growing economic sector in the U.S. and Data Science jobs are growing the fastest within this industry. Additionally, data science jobs have high relative earning potential compared to other industry positions. This course is an exploration of data science and a subset of the tools used in this field. Upon completion of this course, students will have a good idea of possible career options in data science.
This course is offered as a college level mathematics course that accomplishes the objectives of the State of Utah Quantitative Literacy requirement and is an option for students seeking to fulfill the mathematics requirement for the AA and AS degrees.
General Education Outcomes:
1: A student who completes the GE curriculum has a fundamental knowledge of human cultures and the natural world. These days everything can be turned into data. Data Science is “the ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it” (Hal Varian, Chief economist at Google). Whether it be analyzing human behavior in a psychology experiment or studying the effectiveness of a certain drug at treating an illness, the best decisions are informed by a careful analysis of the data. This course provides students with the foundation to understand how such claims are arrived at as well as how to analyze data themselves. This ability to analyze data about the natural world will be assessed through quizzes, exams, and student projects.
2: A student who completes the GE curriculum can read and research effectively within disciplines. To make data meaningful, students need to see the entire life-cycle of data. From collecting, to formatting, to analyzing, to visualizing and presenting. In this class, students will do each of these tasks. This will develop student’s skill in working with data. After working with the data, students will report their analysis, typically on quizzes (paper or electronic), exams, and student projects.
3: A student who completes the GE curriculum can draw from multiple disciplines to address complex problems. Whether it is medicine, business, or politics, the ability to make accurate decisions about large groups without having to survey/inspect every member is a vital skill. Statistical proficiency allows people to determine whether a sample is likely to be representative and whether the results are significant. It also allows effective and succinct communication of methods and outcomes. This outcome will typically be assessed through homework, exams, quizzes, and/or student projects.
4: A student who completes the GE curriculum can reason analytically, critically, and creatively. In a data-rich world, it is important to be able to interpret and analyze statistical claims. By the end of the course, successful students will be proficient at computing confidence intervals and hypothesis tests for one and two populations. This course will go beyond these analyses to include unstructured data. After analyzing these data, students will be able to correctly interpret these results in real world terms. Problems to analyze will come from a variety of areas, such as: business, human behavior, and medicine. Mastery of these skills will be assessed via quizzes, exams, and student projects.
6: A student who completes the GE curriculum can reason quantitatively. As an introduction to the analysis and interpretation of data, this course will ask students to understand the impact that data collection has on themselves and the world around them. For example, companies like Cambridge Analytica used millions of individuals’ data to nefarious ends, something of which every educated person should be aware. Each student's mastery of this skill will be assessed through quizzes, exams, and student projects.
General Education Knowledge Area Outcomes:
1: Students will be exploring data by building mathematical/statistical models and then visualizing the results on a computer. After they analyze the result of this work, they will be required to write about what they observe. Students will be assessed through assignments, class projects, quizzes, exams and class discussion; instructor will provide feedback.
Students will be exploring data by building mathematical/statistical models and then visualizing the results on a computer. After they analyze the result of this work, they will be required to write about what they observe. Students will be assessed through assignments, class projects, quizzes, exams and class discussion; instructor will provide feedback.
2: Convert relevant information into various mathematical forms (e.g., equations, graphs, diagrams, and tables). As students build mathematical/statistical models to analyze data, they will convert data into a form usable by the model. They will then transform the data using the model to gain new insights. Students will be assessed through class projects, assignments and quizzes; instructor will provide feedback.
3: Demonstrate the ability to successfully complete basic calculations to solve problems. Students will often need to complete quick calculations to check that they computer models make sense. Students will be assessed through assignments and quizzes; instructor will provide feedback.
4: Demonstrate the ability to problem solve using quantitative literacy across multiple disciplines. Make judgments and draw appropriate conclusions based on quantitative analysis of data, recognizing the limits of this analysis. Data is everywhere; thus, a data science course will naturally incorporate many disciplines. As students find projects to work on, they will inevitably find themselves exploring the social sciences, computer science, business, education, chemistry and biology, and more. The extent to which students draw from multiple disciplines will be measured by student projects, homework, and exams. The instructor will provide feedback.
5: After students analyze the results of their models, they will make conclusions and use their quantitative work to make arguments supporting their conclusions.
Students will be assessed through assignments, class projects, quizzes, exams and class discussion; instructor will provide feedback.
Student Learning Outcomes:
Content:
This course will introduce students to the idea of data science and it’s applications including: causality & experiments, programming in Python, data types, sequences, tables, visualization, functions and tables, randomness, sampling & empirical distributions, testing hypotheses, comparing two samples, estimation, regression to the mean, prediction, classification, and updating predictions.
Students will also be introduced to common tools applied in research and industry to acquire, clean, analyze, and visualize data.
Key Performance Indicators:
Student learning will be evaluated through:
Attendance/Participation 0 to 15%
Class Group Activities 10 to 15%
Computer Projects 20 to 50%
Quizzes 0 to 20%
Homework 5 to 25%
Midterm Exams / Tests 20 to 40%
Final Examination 15 to 35%
Representative Text and/or Supplies:
The course will use the free online data science textbook provided by California State University at Berkeley: https://www.inferentialthinking.com/chapters/intro.html
A Jupyter Notebook server (called a Jupyter Hub) will be deployed on Snow College servers. This service will allow students to create their own instance of Jupyter Notebooks from any computer on or off-campus. These notebooks will contain all of the project’s students will work on over the semester. Note: A small lab fee may be assessed to support the maintenance of this server.
Pedagogy Statement:
This course will focus heavily on “real-world” applications. For this reason, lecture will be minimized in place of project-based learning. From a pedagogical standpoint, this means that the instructor will give minimal explanations to students. Those explanations that are given, should be in the spirit of an apprenticeship. That is, the instructor should show the students how to interact with the technical tools of the course, then let the students explore those tools. Finally, the instructor will assess student’s proficiency through homework, quizzes, and exams, but special attention should be given to student performance on projects. These projects will demonstrate students’ ability to apply the technical tools of the course.
Instructional Mediums:
Lecture
IVC
Hybrid
Maximum Class Size: 25
Optimum Class Size: 20