Gies + Data Science

Calculus: Fulfilled by MATH 234 (MATH 220 or 221 can also be used) 
First course in calculus and analytic geometry; basic techniques of differentiation and integration with applications including curve sketching; antidifferentation, the Riemann integral, fundamental theorem, exponential and trigonometric functions. 

Linear Algebra for Data Science: MATH 227
Linear algebra is the main mathematical subject underlying the basic techniques of data science. This course provides a practical computer-based introduction to linear algebra, emphasizing its uses in analyzing data, such as linear regression, principal component analysis, and network analysis. We will also explore some of the strengths and limitations of linear methods. Students will learn how to implement linear algebra methods using Python, making it possible to apply these techniques to large data sets. The course assumes an introductory knowledge of Python, such as students acquire in STAT 107.

Data Science Discovery: STAT/CS/IS 107
Data Science Discovery is the intersection of statistics, computation, and real-world relevance. As a project-driven course, students perform hands-on-analysis of real-world datasets to analyze and discover the impact of the data. Throughout each experience, students reflect on the social issues surrounding data analysis such as privacy and design. 

Data Science Exploration: STAT 207
This course explores the data science pipeline from hypothesis formulation, to data collection and management, to analysis and reporting. Topics include data collection, preprocessing and checking for missing data, data summary and visualization, random sampling and probability models, estimating parameters, uncertainty quantification, hypothesis testing, multiple linear and logistic regression modeling, classification, and machine learning approaches for high dimensional data analysis. Students will learn how to implement the methods using Python programming and Git version control. The course assumes an introductory knowledge of statistical concepts and Python, such as students acquire in STAT 107.

Modeling and Learning in Data Science: CS 307
Introduction to the use of classical approaches in data modeling and machine learning in the context of solving data-centric problems. A broad coverage of fundamental models is presented, including linear models, unsupervised learning, supervised learning, and deep learning. A significant emphasis is placed on the application of the models in Python and the interoperability of the results. 

Algorithms and Data Structures for Data Science: CS 277
An introduction to elementary concepts in algorithms and classical data structures with a focus on their applications in Data Science. Topics include algorithm analysis (ex: Big-O notation), elementary data structures (ex: lists, stacks, queues, trees, and graphs), basics of discrete algorithm design principles (ex: greedy, divide and conquer, dynamic programming), and discussion of discrete and continuous optimization. 

Ethics and Policy for Data Science: IS 467
Learn about common ethical data challenges, including privacy, discrimination, and access to data. These challenges will be explored through real-world cases of corporate settings, non-profits, governments, academic research, and healthcare. The course will also cover common ethical principles, providing a framework to analyze these cases. Students will also be introduced to a range of policy responses. The course is suitable for anyone who plans to work in a professional setting that will involve handling data, or who is seeking a grounding for future study of data and information ethics. 

Data Management, Curation, and Reproducibility: IS 477
We introduce and use the Data Science Life Cycle as an intellectual foundation for understanding Data Management, Curation & Reproducibility in the Data Science context. The Data Science Life Cycle allows us to study how data, software, workflows, computational environments, scientific findings,and other artifacts form linked foundational components of data science research. Topics include research artifact identification and management, metadata, repositories, economics of artifact preservation and sustainability, and data management plans. 

One of the most important skills a student will gain in a X + DS degree will be the ability to present data in meaningful ways. This experience should be developed with an adviser before the end of a student’s sophomore year and result in the creation of one or more artifacts documenting the experience. A minimum of 3 credit hours must be specifically designated to the preparation and the completion of the experience component. Two smaller experiences may be used to fulfill the full experience requirement. 

Examples of possible experiences may include:

• A semester study-abroad with at one or more courses focused on discovery while attending the international institution.
• A multi-semester capstone experience within the student’s area of specialization.
• A semester co-op experience outside of the Champaign-Urbana area focused within the student’s area of specialization.
• A multi-semester undergraduate research experience under the direction of faculty.
• A summer REU program focused within the student's area of specialization.