Data Analytics

Director

Marissa Masden

Program Description

Data Makes a Difference diagram by Marissa Masden

The Data Analytics minor provides an introduction to different aspects of data science and data analysis to a wide range of students. An educational background in data analytics equips our students with highly marketable skill sets that are increasingly in demand across industries. In today’s world, the ability to analyze, model, interpret, and communicate insights hidden in data has become essential in addressing challenges in fields as diverse as: sociology, the physical sciences, economics, business, public health, and education. 

There is an increasing reliance on data-driven decision-making in many economic sectors. Employers seek candidates who can combine mathematical and computational rigor, with technical proficiency and ethical decision-making, making graduates with data analytics backgrounds uniquely competitive for the workforce. Moreover, the interdisciplinary nature of data science fosters adaptability, enabling students to tackle diverse problems and collaborate across various fields. This versatility not only broadens career opportunities but also positions graduates as valuable assets in an evolving job market increasingly influenced by computation, artificial intelligence, and big data.

What You’ll Learn

  • Introduction to data-related programming, including types of tasks that can be done with data
  • Communication about data
  • Contemporary ethics and legal issues related to data and AI
  • Exposure to the use of data in a discipline
  • Broaden skills in quantitative and ethical reasoning

Who You Could Be

  • Data scientist
  • Statistician
  • Business/Market analyst
  • Bioinformatician
  • Health data analyst
  • Urban planner
  • Social Media analyst
  • Sports data analyst
Sample Courses

This course focuses on social, economic, legal, and ethical issues that arise from the collection, analysis, and use of large data sets, especially when these processes are automated or embedded within artificial intelligence systems. The course explores the design of ethical algorithms by considering questions like the following: what kinds of biases are ethically problematic and how can they be avoided? what are the effects of automation on jobs and inequality? what are the privacy considerations that arise when collecting and using data? what is the ethical significance of transparency in automation? who owns data sets and who has the right to access information? who is responsible for actions that result from artificial intelligence systems? In thinking about these complex questions, students consider specific case studies of controversial uses of data and algorithms in fields such as medicine, biotechnology, military, advertising, social media, finance, transportation, and criminal justice, among others. In addition to relevant ethical theories, students are introduced to philosophical, legal, and scientific theories that play a central role in debates regarding the ethics of data and artificial intelligence. Readings are drawn from a number of classic and contemporary texts in philosophy, science and technology studies, law, public policy, and the emerging fields of "data ethics" and "robot ethics".

Code
Artistic and Humanistic Perspectives

This course covers the fundamentals of conducting statistical analyses, with particular emphasis on regression analysis and linear models. Students learn to use sophisticated computer software as a tool to analyze and interpret data.

Code
Natural Scientific and Mathematical Perspectives
Prerequisites
MATH 160, MATH 181, PSYC 201, Advanced Placement Statistics, or the equivalent of one of these.

This course concerns application of statistical theory to the analysis of economic questions. Students learn the tools of regression analysis and apply them in a major empirical project.

Code
Social Scientific and Historical Perspectives
Prerequisites
ECON 101, 102, at least one 200-400 level economics course, and MATH 160.

This course introduces the student to the techniques of artificial intelligence. Students learn strategies for uninformed and informed (heuristic) search, knowledge representation, problem-solving, and machine learning. Additional topics may include motion planning, probabilistic reasoning, natural language understanding, and philosophical implications.

Code
Natural Scientific and Mathematical Perspectives
Prerequisites
MATH 180 and CSCI 361 (may be taken concurrently) with a grade of C- or higher, or permission of the instructor.

This course covers experimental and quasi-experimental design, the design of social surveys, and techniques of data analysis appropriate for each type of design. Individual student research projects are required.

Code
Social Scientific and Historical Perspectives
Prerequisites
SOAN 101 or 102 or permission of the instructor.
New Courses for the Data Analytics Program

An introductory course to data analytics in Python giving a first-pass answer to the questions, what do people do with data, why do they do it, and how can I do it? Topics include the data analysis cycle, foundational ethical questions regarding data use, and a survey of modern data analysis tasks including summarization, anomaly detection, regression, classification, and clustering. The laboratory portion of the course involves implementing data tasks using contemporary data analysis toolkits built for the programming language Python and constructing visualization dashboards. No background in programming is assumed; basic control structures in Python will be introduced.

An intermediate course on data analytics in Python. Students begin by developing the skill to obtain data from web APIs; all projects in the course are based on web-obtained data. The first major topic of the course emphasizes an attention to time/space complexity through the lens of k-nearest neighbors algorithms and decision trees. The second major topic is intermediate time series analysis with ARIMA. The last portion of the course addresses the basic ideas behind optimization and randomization-based approaches to classification and regression in high-dimensional settings where classical algorithms fail. Throughout the course, students learn the underlying mathematical principles behind the way basic data analytics algorithms work.

Prerequisite: DATA 160 and either MATH 180 or AP Calculus AB credit, or the equivalent of one of these

Meet the Faculty
Marissa Masden
Assistant Professor, Mathematics and Computer Science and Director, Data Analytics

Prof. Masden researches the topological "shape" of data and neural networks in machine learning. Her general interests are in the application of topology, geometry, and combinatorics in the support of the computational and natural sciences.

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David Chiu
Professor, Mathematics and Computer Science

Prof. Chiu works on problems in large-scale data processing and cloud computing. He enjoys teaching project-based courses in the introductory sequence, databases, and operating systems.

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Lea Fortmann
Associate Professor, Economics

Both Prof. Fortmann’s research and teaching focus on international sustainable development and environmental economics. She teaches both introductory and upper-level economics classes.

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