CSDA

Overview

Overview

In today’s data-driven world, the ability to analyze, interpret, and apply insights from data is essential across all industries. As organizations increasingly rely on data to inform strategy and solve problems, professionals with data analytics skills gain a distinct competitive advantage.

To meet this growing demand, Woodbury University offers a Minor in Data Analytics open to all students outside of the CSDA department—regardless of college or major. This interdisciplinary minor equips students with practical tools and analytical techniques to enhance their research, improve decision-making, and add value in any professional field.

Learning Outcomes

Learning Outcomes

Upon completion of the Data Analytics minor, students will be able to:

1. Formulate meaningful lines of inquiry using data—numerical, textual, spatial, or visual—within the context of their primary field of study, and explore these through collection, organization, visualization, and analysis.
2. Demonstrate an understanding of what data are, how they are collected, the role of metadata, and how to assess data quality and reliability.
3. Attain intermediate proficiency in acquiring and organizing data for analysis.
4. Visualize data at an intermediate level to effectively communicate information and reveal patterns.
5. Apply introductory-level statistical and machine learning tools to ask questions and explore patterns in data.
6. Communicate, both orally and in writing, the limitations of a dataset, methods of data acquisition, interpretation of visualizations, and results of statistical analysis.
7. Reflect on the ethical dimensions of data analysis, including the framing of research questions, data collection methods, analytical approaches, and the rhetoric used in communicating findings.
   

Program Learning Outcomes

Students completing the Minor in Data Analytics will demonstrate the following competencies across five topic categories:

1. Problem Solving in Computer Science and Data Analysis

• Apply data analytics methodologies to solve real-world problems by analyzing large and complex datasets.
• Develop an analytical mindset and apply logical reasoning to evaluate and interpret data-driven solutions.

2. Databases

• Demonstrate familiarity with both relational and non-relational databases and apply them appropriately in analytical tasks.
• Apply commonly used statistical techniques in conjunction with database systems to extract meaningful insights.

• Exhibit a strong understanding of ethical considerations in data collection, analysis, and reporting.
• Uphold professional standards in data handling, ensuring accuracy, transparency, and privacy.

• Develop a foundational understanding of data analytics principles and how they apply across disciplines.
• Engage in data exploration and discovery using appropriate techniques to identify patterns and generate insights.

5. Tools and Techniques

• Gain hands-on experience with industry-standard tools, programming languages, and platforms used in data analytics.
• Apply appropriate techniques and methodologies to clean, visualize, and analyze data effectively.

• Required Courses (12 units):
  CORE 101, CORE 102, CORE 201, and MATH 226
• Elective Courses (at least 6 units):
  CORE 301, CSDA 205, CSDA 209, CSDA 210, CSDA 410, CSDA 415, CSDA 480, CSDA 490,
  MATH 252, MATH 260, MATH 261, MATH 262, MATH 310, MATH 311, and MATH 312