Learning Analytics & Data Science (LADS) 

Overview of this Specialization

With this specialization, you'll be able to harness the power of data to improve learning and teaching,  learn how to design and create information and decision-making tools for educational settings that are based on data science, machine learning, and artificial intelligence.

Upon completing this specialization, you'll have the skills to:

• • • Create solutions for educational settings based on your analysis and development of data from learning tools.

    • Use data and analytics to better understand current learning processes and outcomes and improve them for the future.

    • Meaningfully critique and improve the design of learning analytics from technical, practical, social, and ethical perspectives.

    • Effectively communicate how interconnected infrastructure, policy, and societal factors play a role in learning analytics systems.

Requirements

To complete this specialization, you'll take two required courses in our ECT program and choose one NYU course from a menu of options or by advisement.

ECT Required courses

• • • EDCT-GE 2252 Theories & Principles of Learning Analytics (offered in SPRING) 

    • EDCT-GE 2260  Learning Analytics Applications (offered in FALL) 

NYU electives (choose at least 1) or other electives by advisement
* check ALBERT for when these are offered or for other similar courses

• • • • EDCT-GE 2197 Media Practicum: Field Internship (if aligned with specialization)

      • APSTA-GE 2014: Statistical Analysis of Networks 

      • APSTA-GE 2011: Supervised and Unsupervised Machine Learning 

      • DS-GA 1011 Natural Language Processing 

      • CS-GY 6313: Information Visualization 

      • ITPG-GT 2941 Data w/o Borders: Data Science in the Service of Humanity 

      • CEH-GA 3016 Data Rules: How Quantification Shapes Science, Selves, and States

      • INTE-GE 2007 Qualitative Methods in International Education

 **Electives change from semester to semester. You should search ALBERT for LADs relevant courses. ECT has also tried to identify a few relevant courses for your consideration-- visit our student-created ECT "Airtable" Electives Database (click on the "non-ECT electives tab", then 'By Specialization' for electives outside of ECT)

Supplemental learning activities and resources

Though optional, students specializing in LADS may also wish to engage in supplemental learning activities and resources and join professional organizations and communities as described below:

• • NYU-LEARN (Learning Analytics Research Network)

    • Research Internships

    • Independent Study 

    • Faculty projects with a learning analytics focus

    • Events and Workshops

  • Learning Analytics Thesis (design project or paper), strongly recommended

  • Non-credit Data Services classes (NYU Library)

  • Career Resources page (ECT student website, in progress)

  • Meet with Learning Analytics focused faculty advisor (Yoav Bergner, Alyssa Wise, Xavier Ochoa)

  • LinkedIn Learning courses (free to NYU students): Intro course, R or Python

  • Professional Organizations: Solar, International Educational Data Mining Society , AIED

  • Annual Learning Analytics Summer Institute (LASI)

  • Annual Research Conferences: LAK (March), AIED (June), EDM (July)