InfoSci Curriculum College Park

  • Preparing the next generation of information professionals and changemakers.

InfoSci at College Park - Curriculum, Courses, Syllabi

The InfoSci degree requires a total of 120 credits, including 40 credits in General Education and 45 credits in the Information Science major. In addition to the ten core courses, 15 credits (five courses) of upper level major electives are required to complete the Information Science degree.

Enrolled Students: be sure to consult the Undergraduate Academic Policies, Forms, & Handbooks.

Program Structure

► InfoSci Benchmark Courses

Benchmark courses are "indicator courses" that help advisors chart your progress in the major. Completing the benchmark courses on time, and with good grades, means you are making satisfactory progress through the major.

Failure to complete the benchmark courses with a C- or better within two attempts, will require you to change out of the the major. If you are having challenges in the benchmark courses it may be a sign that the major is not a good fit, and you should speak to an advisor.

Benchmark I (Must be completed within the first 30 credits after declaring the major).

  • MATH 115 (or higher) - Precalculus (3 credits)
  • PSYC 100 - Introduction to Psychology (3 credits)

Benchmark II (Must be completed within the first 60 credits after declaring the major).

  • STAT 100 - Elementary Statistics and Probability (3 credits)
  • INST 126 - Intro to Programming for Information Science (3 credits)
  • INST 201 - Introduction to Information Science: Heroes and Villains in the Age of Information (3 credits)

► InfoSci Core Courses

The program requires ten courses of Core and five courses of upper level (300-400 level) Major Electives.

INST Core Courses

  • INST 201 Introduction to Information Science: Heroes and Villains in the Age of Information
    Examining the effects of new information technologies on how we conduct business, interact with friends, and go through our daily lives. Understanding how technical and social factors have influenced the evolution of information society. Evaluating the transformative power of information in education, policy, and entertainment, and the dark side of these changes..
  • INST 311 Information Organization
    Examines the theories, concepts, and principles of information, information representation and organization, record structures, description, and classification. Topics to be covered in this course include the methods and strategies to develop systems for storage, organization, and retrieval of information in a variety of organizational and institutional settings, as well as policy, ethical, and social implications of these systems.
  • INST 314 Statistics for Information Science
    Basic concepts in statistics including measure construction, data exploration, hypothesis development, hypothesis testing, pattern identification, and statistical analysis. The course also provides an overview of commonly used data manipulation and analytic tools. Through homework assignments, projects, and in-class activities, you will practice working with these techniques and tools to create information resources that can be used in individual and organizational decision-making and problem-solving.
  • INST 326 Object-Oriented Programming for Information Science
    An introduction to programming, emphasizing understanding and implementation of applications using object-oriented techniques. Topics to be covered include program design and testing as well as implementation of programs.
  • INST 327 Database Design and Modeling
    Introduction to databases, the relational model, entity-relationship diagrams, user-oriented database design and normalization, and Structured Query Language (SQL). Through labs, tests, and a project, students develop both theoretical and practical knowledge of relational database systems.
  • INST 335 Teams and Organizations
    Team development and the principles, methods and types of leadership will be a focus with an emphasis on goal setting, motivation, problem solving, and conflict resolution. This course examines the principles of managing team projects in organizations through planning and execution including estimating costs, managing risks, scheduling, staff and resource allocation, communication, tracking, and control.
  • INST 346 Technologies, Infrastructures and Architecture
    Examines the basic concepts of local and wide-area computer networking including an overview of services provided by networks, network topologies and hardware, packet switching, client/server architectures, network protocols, and network servers and applications. The principles and techniques of information organization and architecture for the Web environment will be covered along with such topics as management, security, authentication, and policy issues associated with distributed systems.
  • INST 352 Information User Needs and Assessment
    Focuses on use of information by individuals, including the theories, concepts, and principles of information, information behavior and mental models. Methods for determining information behavior and user needs, including accessibility issues will be examined and strategies for using information technology to support individual users and their specific needs will be explored.
  • INST 362 User-Centered Design
    Introduction to human-computer interaction (HCI), with a focus on how HCI connects psychology, information systems, computer science, and human factors. User-centered design and user interface implementation methods discussed include identifying user needs, understanding user behaviors, envisioning interfaces, and utilizing prototyping tools, with an emphasis on incorporating people in the design process from initial field observations to summative usability testing.
  • INST 490 Integrative Capstone
    The capstone provides a platform for Information Science students where they can apply a subset of the concepts, methods, and tools they learn as part of the Information Science program to addressing an information problem or fulfilling an information need.

► Cybersecurity and Privacy Specialization

Apply your Major Elective courses to the Cybersecurity and Privacy Specialization.

Students equip themselves with human-centered cybersecurity skills and perspectives, and prepare to launch careers in the cybersecurity field with particular emphasis on management, policy, and governance-related functions. (Beginning Fall 2019)

A total of 5 courses are required to complete the Cybersecurity Specialization - 3 courses are predetermined and 2 are self-selected: 


Complete 3 Courses below:  

  • INST 364 Human-Centered Cybersecurity
  • INST 365 Ethical Hacking
  • INST 366 Privacy, Security, and Ethics for Big Data


Choose 2 Courses From Below

  • INST 464 Decision-Making for Cybersecurity
  • INST 466 Technology, Culture, and Society
  • INST 467 Practical Hacking for Policy Making

► Data Science Specialization

Apply your Major Elective courses to the Data Science Specialization.

Students develop understanding and skills for managing, manipulating, and mobilizing data to develop insight, create value, and achieve organizational goals in a wide range of sectors.


  • INST 354 Decision-Making for Information Science
    Examines the use of information in organizational and individual decision-making, including the roles of information professionals and information systems in informed decision-making through techniques such as data analysis and regression, optimization, sensitivity analysis, decision trees, risk analysis and business simulation models.
  • INST 377 Dynamic Web Applications
    An exploration of the basic methods and tools for developing dynamic, database-driven websites, including acquiring, installing, and running web servers, database servers, and connectability applications.
  • INST 414 Advanced Data Science
    An exploration of how to extract insights from large-scale datasets. The course will cover the complete analytical funnel from data extraction and cleaning to data analysis and insights interpretation and visualization. The data analysis component will focus on techniques in both supervised and unsupervised learning to extract information from datasets. Topics will include clustering, classification, and regression techniques. Through homework assignments, a project, exams and in-class activities, students will practice working with these techniques and tools to extract relevant information from structured and unstructured data.
  • INST 447 Data Sources and Manipulation
    Examines approaches to locating, acquiring, manipulating, and disseminating data. Imperfection, biases, and other problems in data are examined, and methods for identifying and correcting such problems are introduced. The course covers other topics such as automated collection of large data sets, and extracting, transforming, and reformatting a variety of data and file types.
  • INST 462 Introduction to Data Visualization
    Exploration of the theories, methods, and techniques of visualization of information, including the effects of human perception, the aesthetics of information design, the mechanics of visual display, and the semiotics of iconography.


► Digital Curation Specialization

Apply your Major Elective courses to the Digital Curation Specialization.

With this specialization, students can launch careers in which they collect, digitize, appraise, curate, and disseminate information assets effectively and efficiently. (Beginning Fall 2019)


  • INST 341 Introduction to Digital Curation
    This course explores various dimensions and contexts for digital curation. For the purpose of this class, digital curation encompasses all activities involving the management, representation, and preservation of both born-digital and digitized information. Focus will be on current efforts that respond to the opportunities, challenges, and demands of ever increasing digital data and networked information infrastructure. The course explores the infrastructure necessary for handling digital collections as well as the knowledge and skills necessary for effective management of digital curation systems and programs.
  • INST 441 Tools and Methods for Digital Curation
    The purpose of this course is to develop knowledge and skills in the application of digital curation tools and methods in diverse organizational settings, academic disciplines, and economic sectors. Using the digital curation lifecycle as the foundational concept, students will use digital collections to explore tools, workflows, and standards required to successfully access, preserve, and reformat data contained in digital collections. Students will learn how to develop and implement data management plans, and how to design and use workflows and tools for creating, acquiring, or ingesting content.
  • INST 442 Information Ethics and Policy
    Students will explore via case studies the legal, ethical, and technological challenges in developing and implementing policies for managing digital assets and information. Emphasis will be on access questions pertinent to managing sensitive information, and the roles and responsibilities of information professionals.
  • INST 443 Curation in the Sciences, Humanities, and Social Sciences
    Students will learn how to apply digital curation principles, tools, and strategies in managing diverse data collections and digital information in different disciplinary settings. In particular, this course will explore differences among data curation principles and practices across diverse settings, ranging from scientific organizations (such as business and academic research laboratories and computational science settings), to humanities-based institutions (such as cultural heritage organizations), to social science-based institutions (such as data-intensive professional environments).
  • INST 448 Digital Curation Research in Cultural Big Data Collections
    This course focuses on introducing students to principles, methods, and technologies involved in the digital curation of large cultural data collections. Students will learn these concepts in class lectures, discussions, and participating on project teams in the Digital Curation Innovation Center (DCIC).

Additional Information

► Detailed Course Descriptions & Syllabi

View the full iSchool undergraduate course catalog for detailed course descriptions, course and section numbers, instructors, and syllabi.

► Python Coursework Preparation

Recommended Python focus topics, books, and tutorials

Essential Python Topics

  • Statements, variables, basic data types & operators
  • Conditional statements
  • Loops
  • Lists, dictionaries, tuples
  • Input and output
  • Reading and writing files
  • Functions, libraries, modules
  • Testing & debugging

Python Books

Online Python Courses and Tutorials

► Academic Policies

Every student in the Information Science major must follow the policies of the program and college. If you have questions about a policy, please contact your advisor.

  • Required Number of Credits:
    InfoSci students are required to take 45 credits within the major. 30 credits of which must be approved major coursework with the INST prefix. Students must take 15 credits of approved upper-level [300-400 level] electives.
  • Benchmark and Major courses:
    Students who have declared InfoSci as their major, must take benchmark and InfoSci core courses at UMD.
  • Benchmark Courses Taken Concurrently with Major Courses:
    InfoSci students must successfully (C- or higher) complete all benchmark courses before taking InfoSci Core coursework. The College will allow InfoSci students to start taking InfoSci core courses in their last semester of benchmark coursework.
  • Major Electives:
    In order to apply non-INST UMD courses towards the InfoSci major elective requirements, students must take courses that are approved by the InfoSci program after declaring InfoSci as their major. Students must obtain approval for non-INST courses before enrolling in them in order for them to be counted as major electives.
  • Declared InfoSci Students:
    Students that declare InfoSci as their major must complete all benchmark courses prior to enrolling in major electives.
  • Advanced Placement Credits:
    Advanced Placement (AP) credits that have been accepted and transferred to UMD successfully may be used to satisfy corresponding InfoSci benchmark requirements.