麻豆社

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Data Science

Bachelor of Science

Degree Type:
B.S.
College:
College of Arts & Sciences
Department:
Math, Computer Science, & Data Science

Business runs on data.

Businesses of all shapes and sizes rely on quality data to make informed, strategic decisions. That’s why you’ll find data science professionals in every imaginable industry.

Prepare for this high-demand field with a major in data science at 麻豆社. Gain proficiency in the programs and tools used to collect, store, manage, protect, and analyze data. And learn how to visualize, present, and communicate findings to stakeholders of all kinds.

At JCU, you will develop more than technical skills. Rooted in the Jesuit liberal arts tradition, our program explores the ethics of data collection and privacy—and the complex relationships between humans and technology.

Hands-on Learning

Learn data by doing in lab classes. Collaborate with peers on a data project for a real-world client. Participate in competitions.

Immersive Internships

Gain experience and make connections. Our region is full of innovative companies where you can hone your data skills, such as Progressive, Sherwin-Williams, and Cleveland Clinic.

  • Hands-on Learning

    Learn data by doing in lab classes. Collaborate with peers on a data project for a real-world client. Participate in competitions.

  • Immersive Internships

    Gain experience and make connections. Our region is full of innovative companies where you can hone your data skills, such as Progressive, Sherwin-Williams, and Cleveland Clinic.

  • Undergraduate Research

    Pursue a research project or assist a faculty member with theirs. Present on campus at DISCOVER or at an academic conference.

  • 36% expected increase in demand for data scientists through 2033 (BLS.gov)

  • #8 on the 100 Best Jobs in America List - Data Scientist (U.S. News & World Report, 2025)

  • $105,990 Median annual wage for computer and information technology occupations (BLS.gov)

  • Top 10 fastest-growing market for tech talent in the U.S. (Destination Cleveland)

Program Overview & Outcomes

As a data science major at John Carol, you will take a mix of statistics, computer science, and mathematics courses. Through electives you can learn how to apply data science knowledge to specific fields, from sociology to sports leadership.

In this program, you will learn to:

  • Apply foundational skills in computer science, mathematics, and statistics that are essential for data science
  • Explain and use data science methodology
  • Communicate about and collaborate on data science applications and projects
  • Exhibit ethical and professional awareness

Curriculum: What You Will Learn

You'll develop expertise in data analytics, programming, and statistical methods. The program combines computer science foundations with advanced data science techniques including machine learning, data mining, and big data analytics, preparing you for careers in data-driven industries.

View Program Catalog

  • Major Required Courses

    Sci: Problem Solving With Programming

    3 Credits

    CS1280

    Introduction to computer science fundamentals, with focus on problem solving using high-level programming language. Topics include algorithm design, number representation, data types, expressions, control structures (sequential, conditional, iterative), functions, arrays, and strings. Suitable for students with no prior background in computing. Corequisite: CS 1281. Offered: Fall, Spring.

    Cs1281

    CS1281

    Programming laboratory intended to provide hands-on experience in applying the programming concepts learned in CS 1280. Experience in learning the process of program development, with emphasis on techniques for testing and debugging. Corequisite: CS 1280. Offered: Fall, Spring

    Introduction To Object-Oriented Programming

    3 Credits

    CS1290

    Continuation of CS 1280 emphasizing the benefits of object-oriented languages: modularity, adaptability, and extensibility. Object-oriented programming concepts include objects, classes, methods, constructors, message passing, interfaces, inheritance, and polymorphism. Note: A grade of C- or higher in CS 1290 is required to register for any course that has CS 1290 as a prerequisite. Prerequisite: CS 128 or 1280. Corequisite: CS 1291. Offered: Fall, Spring.

    Cs1291

    CS1291

    Object-Oriented programming laboratory intended to provide hands-on experience in applying the programming concepts learned in CS 1290. Corequisite: CS 1290. Offered: Fall, Spring.

    Cs2290

    CS2290

    Introductory overview of data structures and algorithms, highlighting the connection between algorithms and programming. Topics include algorithm complexity, generic programming, linked lists, stacks, queues, recursion, trees, searching and sorting. Prerequisite: CS 129 or 1290 (min grade C-).

    Qa: Elementary Statistics

    3 Credits

    DATA1220

    Collecting data, describing data by graphs and tables, descriptive statistics, sampling distributions, confidence intervals, tests of hypotheses for one and two means and proportions, and simple linear regression. Methods are illustrated in the context of quantitative research, with applications in disciplines such as sports, psychology, and social and natural sciences. Use of the statistical software R. Students who took DATA 228 or DATA 2280 may not be allowed to take DATA 1220. For program requirements and prerequisites, equivalent courses include DATA 2280, EC 2210, and PO 1500. This course was formerly offered as MT 122 / DATA 122. Offered: Fall, Spring.

    Data1500

    DATA1500

    Relational database design and implementation, structure query language (SQL), entity relationship (ER) modeling, and database normalization. This course was formerly offered as CS 150. Offered: Fall, Spring.

    Data2100

    DATA2100

    This course introduces the principles of data science. Students will learn essential data analysis techniques including data collection, cleaning and processing using Pythons core data science libraries like Pandas, NumPy, Matplotlib, NLTK, and scikit-learn. The course emphasizes practical, hands-on experience, enabling students to create effective visualizations, process text data with NLP techniques, and analyze social networks. By the end of the course, students will be equipped with essential skills to apply Python-based data science tools in real-world scenarios. Prerequisites: CS1280 AND CS1281.

    Data2280

    DATA2280

    Exploratory data analysis, probability fundamentals, sampling distributions and the Central Limit Theorem, interval estimations, tests of hypotheses, one-factor analysis of variance (one-way ANOVA), linear regression, introduction to categorical analysis, including contingency tables and chi-square tests, using the statistical software R. Course content in biology context. Offered: Fall, Spring.

    Data3440

    DATA3440

    This course introduces the fundamental concepts and models of Machine Learning (ML) with a practical treatment of design, analysis, implementation, and applications of algorithms that learn from examples. Students will focus on the theory underlying a range of learning algorithms to build/train ML models that support AI applications in real-world use cases such as smart robots (perception/control), computer vision, bioinformatics, audio, data mining, etc. Prerequisite: DATA2100 OR CS2290.

    Data3510

    DATA3510

    This course offers a comprehensive foundation in Big Data and Cloud Computing. Students will explore the principles and practices of big data processing while leveraging cloud-based platforms to analyze and manage large datasets efficiently. Through hands-on experience, learners will gain insights into the scalability and flexibility that cloud computing provides for big data solutions. In the final project, students will apply their skills by developing a big data product using real-world data, demonstrating the integration of cloud technology in big data applications. Prerequisite: DATA 1500.

    Data4240

    DATA4240

    Multiple linear regression, collinearity, model diagnostics, variable selection, model comparisons, applications of prediction and explanation; use of the statistical software R. Prerequisite: DATA 1220 OR DATA 2280. Offered: Fall.

    Data4500

    DATA4500

    This course offers an introduction to data mining techniques, focusing on uncovering meaningful patterns from large datasets. Core topics may include key techniques in data preprocessing, exploratory data analysis, dimensionality reduction, pattern discovery, and clustering. Students will also explore methods for outlier detection, classification, and regression. This course will equip students with the foundational skills necessary to apply data mining techniques across various domains, preparing them for careers in data science and analytics. Prerequisite: DATA3250 OR DATA3440 OR CS3440.

    Data4700

    DATA4700

    Simulation of the environment of the professional data scientist working in a team on a large data project for a real client. Students will encounter a wide variety of issues that naturally occur in a project of scale, using their skills, ingenuity, and research abilities to address all issues and deliver a usable data product. To be taken during the student's final year. Prerequisite: DATA 300 or DATA 3000; EN 125 or EN 1250 (or equivalent). Prerequisite or corequisite: DATA 4240 (previously DATA 424). Permission of Department Chair. Offered: Fall.

  • Support Courses

    Qa: Political Statistics And Analysis

    3 Credits

    PO1500

    Introduces students to foundational quantitative analysis in a political context, specifically describing and representing data, posing precise and testable questions, drawing inferences from data, analyzing data, and understanding appropriate statistical software.

Related Programs

Still deciding on your program of study at JCU or considering a second major? Here are some other academic areas to explore.

Complementary Minors

  • “During a summer internship, I developed a web app that integrated multiple machine learning models. The experience was incredible. JCU’s network and internship opportunities led to greater skills, abilities—and a job.”

    Daniel Droder ’21 Senior Machine Learning Engineer, Oracle

Where Our Alumni Go

JCU data science majors find success in a variety of roles in industries of all kinds, from healthcare and government to technology and finance. They also start companies or pursue graduate degrees in related fields.

Progressive
Cleveland Clinic

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