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.

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Core Courses – 9 Credits

IST 343 | 3 CREDITS
Critically examine how individuals, groups, and societies create and are created by digital data and algorithms. You will analyze the social, political, legal, and environmental impacts of data and data-driven technologies across varying contexts including social media and (generative) AI.

IST 387 | 3 CREDITS
Introduction to using data science across many different situations. Covers concepts such as data management, transformation, analysis, and machine learning, using R. No programming experience required. Hands-on projects and real-world problem-solving help identify when data science is useful, with emphasis on ethically applying data science.

IST 414 | 3 CREDITS
Theories and techniques for real-world research into human phenomena across various contexts (business, society, friendships, politics). Learn about asking good questions, matching methods with questions, designing ethical studies, and gathering and analyzing both qualitative and quantitative data.

Electives – 9 credits

IST 356 | 3 CREDITS
Approaches for building pipelines in data analytics using the Python programming language; data cleaning, extraction, wrangling, API’s, web scraping. Building data products. Programming experience required.

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 machine-learning techniques and their underlying algorithms. Students learn to build machine learning pipelines that transform raw data into machine learning models that yield actionable insights using real-world data. Hands on programming experience in the Python language with industry standard technologies.
PREREQ IST 387

IST 418 | 3 CREDITS
Learn to develop actionable insights from big data using open-source tools (Python and Spark). This course prepares students to build scalable data analytics pipelines and apply advanced machine learning techniques, culminating in a hands-on project tackling real-world challenges.
PREREQ IST356 and IST387

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