Applied Data Analytics
Minor

Apply data-driven insights to your primary field of study.

Every industry and profession needs graduates who can use data to deepen their knowledge, further their research, and make better business decisions. That’s why our minor in Applied Data Analytics is open to all students at Syracuse University outside of the iSchool, regardless of college or major.

  • Learn to use data-driven approaches to generate insights and inform decisions.
  • Examine how individuals, organizations and society are impacted by data and machine learning models.
  • Utilize data science development tools to support the full analytics life cycle.
  • Gain visual, quantitative, qualitative and computational data science skills.
  • Enhance your current field of study with a deeper understanding of data analysis.
Quick Info

6 Courses / 18 Total Credit Hours

Courses & Curriculum

The minor Applied Data Analytics is 18 credits and combines a primary core, with the choice of 9 credits worth of electives to give you a strong data science foundation with a focus of your choosing.

View Full Course Catalog

Core Courses – 9 Credits

IST 343 | 3 CREDITS
Students will critically examine how individuals, groups, and society create and are created by digital data and algorithms.  Students will explore social, political, legal, and professional issues across varying contexts including social media and the Internet of Things.

IST 387 | 3 CREDITS
Introduces students to fundamentals about data and the standards, technologies, and methods for organizing, managing, curating, preserving, and using data. Discusses broader issues relating to data management, quality control and publication of data.

IST 414 | 3 CREDITS
This course introduces students to a variety of approaches to answer questions in a variety of contexts (e.g. business, society, friendships, politics).  Students will learn how to ask good questions and answer those questions ethically using a variety of data-driven approaches, including quantitative, qualitative, and computational.

Electives – 9 credits

IST 256 | 3 CREDITS
Computational literacy and learning to code are critical skills of the 21st century. Students are introduced to Python programming language with emphasis on practical applications relevant to everyday lives and common within the information field.

IST 359 | 3 CREDITS
Data structure, file organization, and principles and concepts of data bases for information retrieval systems. Data analysis, design, models, management, evaluation, and implementation.

IST 407 | 3 CREDITS
Introduction to data mining techniques, familiarity with particular real-world applications, challenges involved in these applications, and future directions of the field. Optional hands-on experience with commercially available software packages.
PREREQ IST 387

IST 418 | 3 CREDITS
A broad introduction to analytical processing tools and techniques for information professionals.  Students will develop a portfolio of resources, demonstrations, recipes, and examples of various analytical techniques.
PREREQ IST387 with minimum grade of B

IST 421 | 3 CREDITS
Introduction to skills and techniques related to information visualization, through various programming and illustration tools, data cleaning techniques, design concepts and ethics.  Develop static data visualizations to explore and communicate findings from a variety of data sources.
PREREQ IST256 or IST359 or IST387 or SAL413

IST 462 | 3 CREDITS
Scripting for the data analysis pipeline. Acquiring, accessing and transforming data In the forms of structured, semi- structured and unstructured data. Additional work for graduate students.
PREREQ IST 256

IST 469 | 3 CREDITS
Analyze relational and non-relational databases and corresponding database management system architectures. Learn to build complex database objects to support a variety of needs from big data and traditional perspectives. Data systems performance, scalability, security. Additional work required for graduate students.
PREREQ IST 359