Course Syllabus

MATH 1120 Introduction to Data Science

Course 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 or image processing. Access to a computer is required.

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. 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.
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, analyzing, visualizing, and presenting. In this class, students will do each of these tasks. This will develop a student’s skill in working with data. After working with the data, students will learn principles to report their analysis.
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.
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.
5. A student who completes the GE curriculum can reason quantitatively.  Data science requires an analysis of the results to reason whether the results make sense quantitatively. Students taking this class will be able to explore the impact that data collection has on themselves and the world around them.

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 communicate what they observe. 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 communicate what they observe.
2. MATHEMATIZATION: Convert quantitative or mathematical information into appropriate mathematical representations and/or models such as equations, graphs, diagrams, or tables, including making and evaluating important assumptions as needed. 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.
3. CALCULATION: Use algebraic skills and techniques to solve problems, including the ability to identify and correct errors in calculations and understanding the role and proper use of technology in assisting with calculations. Students will often need to complete quick calculations to check that they computer models make sense.
4. ANALYSIS: Draw appropriate conclusions through quantitative or mathematical analysis of data or models, including understanding and evaluating important assumptions in order to recognize the limits of the analysis. After students analyze the results of their models, they will make conclusions and use their quantitative work to make arguments supporting their conclusions.
5. APPLICATION / CREATION: Solve concrete and abstract problems across multiple disciplines.  Data are 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.

Course Content

This course provides a comprehensive introduction to the principles and methodologies of data science. Emphasis is placed on developing a conceptual and practical understanding of how data are collected, organized, analyzed, and interpreted to support evidence-based decision-making across disciplines.Topics include causality and experimental design, data organization and management using spreadsheets, and an introduction to data programming in a reputable language. Students will examine core concepts such as data types, summary functions and tables, and data visualization. The course further explores statistical and computational techniques, including regression analysis, comparison of populations, prediction, classification, and recursion.In addition, students will gain exposure to industry-standard tools and techniques used in the acquisition, cleaning, analysis, and visualization of data. Upon completion, students will demonstrate both the theoretical understanding and practical competencies necessary to apply data science methods in academic research and professional contexts.