糖心logo在线入口

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 糖心logo在线入口. 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 糖心logo在线入口, 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.

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.

  • 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.

    Problem Solving With Programming Laboratory

    1 Credits

    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.

    Introduction to Object-Oriented Programming Laboratory

    1 Credits

    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.

    Introduction to Data Structures and Algorithms

    3 Credits

    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-).

    Business Decision Optimization

    3 Credits

    DATA1220

    Application of mathematical optimization to decision-making. Uses MS-Excel and several add-ins as tools to find optimal solutions to a wide variety of business problems. Topics include linear programming, network models, non-linear programming, goal programming, decision trees, and simulation. Prerequisites: (EC210 or EC2210 or MT122 or MT1220 or MT228 or MT2280 or DATA122 or DATA1220 or DATA228 or DATA2280) AND (BI200 or BI2200).

    Database Systems

    3 Credits

    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.

    Foundations of Data Science and Visualization Techniques

    3 Credits

    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.

    Advanced Ecology

    4 Credits

    DATA2280

    Three hours of lecture/discussion per week. Topics include predator-prey interactions, global change, niche theory, competition, null models, and community assembly rules. Prerequisite: BL222 or BL 2220, and DATA228 or DATA2280. Corequisite: BL4445. Additional fees apply to this course. Please see the section schedule for amount details.

    Data Mining

    3 Credits

    DATA3440

    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.

    Big Data and Cloud Computing

    3 Credits

    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.

    Applied Regression Analysis

    3 Credits

    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.

    Data Mining

    3 Credits

    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.

Where Our Alumni Go

糖心logo在线入口 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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