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Credits: 3 Prerequisites: 17:610:550. See course description for a further explanation of prerequisites. Corequisites: None
This course gives students a hands-on introduction to the key web programming concepts, technologies, and software tools used to create web applications. Students will learn how to use major front- and back-end frameworks to design multi-device user experiences.
Prerequisites: Students who have not successfully completed the course 17:610:550 may request to waive the course prerequisite if they can demonstrate that they have the knowledge/expertise to:
- Create and upload HTML web pages that are standards-compliant and contain relative and absolute links, tables, images, and embedded video or data widgets.
- Design external cascading style sheets that control the layout and visual appearance of a set of web pages.
- Use basic programming concepts (variables, arrays, loops, and functions).
- Understand the basics of JavaScript to create or customize simple functions.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Code using key web programming concepts, structures, and processes
- Explain Full Stack development and its related front- and back-end concepts and tools.
- Develop code using JavaScript and ECMAScript 6 that makes advanced use of functions, loops, event control, array processing, and DOM manipulation.
- Create templates, modules, and components using front-end frameworks.
- Use code editors and workflow tools to effectively develop and debug code.
- Employ version control systems, such as Git, to manage source code changes and use and share code on repository hosting services, such as GitHub.
- Use key code libraries and front- and back-end frameworks
- Create responsive page layouts using front-end frameworks, such as Bootstrap.js and its grid system and components
- Use Model View Controller (MVC) design patterns, two-way data binding, and create templates, modules, and services
- Implement single page application using front-end frameworks such as React.js or Angular.js
- Design single page application using a back-end framework such as Node.js to exchange with a database
- Connect to databases and data sources
- Implement NoSQL database such as Mongo DB that works within a back-end framework such as Node.js
- Use online documentation, forums, and code created by others
- Debug code by using online documentation and forums
- Interact with other developers online to troubleshoot code and interact with local developers by joining a MeetUp
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Credits: 3 Prerequisites: None Corequisites: None
This course provides students with a practical introduction to the field of Data Science and familiarizes them with the essential facets of the data scientist profession. This includes a grounding on data-based reasoning, problem formulation, data collection, data pre-processing, data analytics, visualization, and the ethics surrounding the use of data and its processing.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Assess and articulate the relevance of data for a particular business or societal problem.
- Collect, store, and retrieve data originating from multiple sources.
- Preprocess diverse data into standardized formats.
- Undertake exploratory data analysis to generate insights from the data.
- Visualize data into charts and other visual representations for generating insights and supporting decision making.
- Describe the ethical, legal, and social implications of such data collection and processing.
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Credits: 3 Prerequisites: None Corequisites: None
Data analytics linked to storage, curation, management, and mining with attention to alternative methodological approaches. The course will demonstrate various methods to explore how big data might be analyzed, stored, and retrieved.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Use models and structures to depict large data sets within a data analytics environment.
- Apply statistical models to data using SPSS and related software programs.
- Explain, determine, debate, compare and contrast: predictive analytic issues, language issues, algorithms, analytic methods, curation issues, and applications to social networks.
- Create a tutorial guide to assist others in data analytic endeavors to include structuring data sets, using software to analyze data, and depositing and making accessible big data sets.
- Assess how different statistical models might be used to extract meaning from particular data sets.
- Understand how clustering and other statistical models can be used to extract meaning from data sets which will then lead to decision models on particular sub-groups.
- Specify how to manage and curate large sets within a particular application area.
- Address storage, security, and privacy issues as they apply to different types of large data sets.
- Implement appropriate frameworks to identify the overall model for a big data set, how such data might be analyzed, curated, placed in a repository to provide appropriate access points for data set retrieval.
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Credits: 3 Prerequisites: 17:610:550; 17:610:560 Corequisites: 17:610:561
This course offers students a practical introduction to the field of Data Science and common methods for quantitative and computational analytics, through which they can have an overview of key concepts, skills, and technologies used by data scientists. While the course covers several programming languages and tools, the focus is on solving problems. Students will be introduced to real-life problems that involve collecting and analyzing data.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Apply programming languages and tools such as UNIX commands, MySQL, Python, and R to collect, clean, process, and analyze data.
- Exhibit familiarity with data science methods by learning and experiencing essential algorithms and approaches.
- Use statistical methods and visualization techniques to explore and analyze data, and visualize and present the results.
- Identify data-driven analytics problems and design solutions and applications to solve them.
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Credits: 3 Prerequisites: 17:610:550 and 17:610:560 and 17:610:562 Corequisites: None
This course offers students a practical introduction to using Machine Learning algorithms, tools, and techniques for solving problems that fall under the umbrella of Data Science. It is structured around learning concepts from the field of Machine Learning and applying them on data-intensive problems. While the course covers theories of Machine Learning and tools such as R, the focus is on using them for solving data-driven problems. Students will be introduced to several real-life problems that involve analyzing data for prediction, classification, organization, estimations, and pattern recognition.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Exhibit familiarity with Machine Learning methods by learning and experiencing essential algorithms and approaches as they relate to data problems and information processing strategies;
- Use Machine Learning techniques to explore and analyze data, and derive decision-making insights;
- Identify data-driven analytics problems as they relate to organizational or individual information needs;
- Design innovative solutions and applications to solve problems using Machine Learning techniques and strategies.
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Credits: 3 Prerequisites: 17:610:550 Corequisites: None
Collecting, making available, and preserving digital materials, including data resources as well as text resources. This is not about conversion to digital but about the organization and long-term preservation of both converted and born-digital materials.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Describe the history and current state of digital archives/museums;
- Investigate the major questions that affect how an institution should gather and organize digital materials;
- Design a new digital curation program including its principles for selection of content, the kinds of tools that might be used for a variety of materials, an organization of the library that is appropriate, and relevant legal issues;
- Identify possible funding sources and sustainability.
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Credits: 3 Prerequisites: None Corequisites: None
This course introduces students to the concepts of information security management. Both technical and administrative aspects of enforcing and managing the security of information assets will be covered through requirements analysis, planning and designing of information security management systems and development of information security plans and policies.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Articulate the importance of information security to organizations and individuals and learn how information security can be incorporated in any system;
- Evaluate information assets in order to define the level of security needed, information security strategy, model and architecture to suit organizational security needs;
- Identify possible threats to assets, and possible countermeasures to reduce vulnerabilities and avert risks and their suitability for implementation;
- Practice best practices concerning information security behavior to help protect information assets;
- Utilize standard processes and procedures to analyze security requirements and design and document information security plans.
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Credits: 3 Prerequisites: None Corequisites: None
This course evaluates the interconnectedness of digital preservation, digitization, copyright, and curation of digital collections. Digitized formats and media and born digital content are crucial to the success of cultural heritage digital collections. Digital preservation ensures this content is accessible and stable for many lifetimes. The goal of this course is to give students an advanced look into the world of cultural heritage digital preservation. This course allows students to explore their own interests in typical cultural heritage formats and media held by museums, libraries, and archives.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Write a grant proposal focused on digital preservation and digitization of collections
- Assess copyright of cultural heritage materials for public digital access
- Compare and contrast controlled digital lending practices across an array of institutions
- Evaluate digitization best practices and advanced imaging techniques in cultural heritage institutions
- Use frameworks such as Open Archival Information System (OAIS) and National Digital Stewardship Alliance (NDSA) levels to assess digital preservation strategies
- Utilize time management techniques to understand the intertwined nature of digital preservation and project management
- Develop a personal digital collection while ensuring its long term accessibility and digital preservation status
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Credits: 3 Prerequisites: None Corequisites: None
This course provides an introduction to the current state of management theory, ethics, and practice focusing on leadership and the management of information organizations.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Examine, evaluate, and apply contemporary management concepts, theories, and functions.
- Demonstrate an understanding of the applications of managerial functions including planning, organizing, and leading.
- Explore existing information organizations and critically analyze the ways in which management activities and functions are carried out in those environments.
- Analyze and evaluate key organizational concepts and issues.
- Develop and apply problem-solving skills in a managerial situation.
- Contribute to collaborative projects and initiatives that are relevant in today's interactive and technologically mediated work environments.
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Credits: 3 Prerequisites: None Corequisites: None
Today's libraries are in the midst of transformative change requiring a workforce that is able to lead, manage, and interact with others outside of traditional structures long associated with organizational hierarchies. This course will provide students with the foundational values underlying the library profession, explore leadership topics, and focus on skills and attributes that are essential for the delivery of effective and dynamic library service.
Learning Objectives
Upon successful completion of this course, students will be able to:
- Critically analyze and evaluate key leadership theories, models, and perspectives and apply them in a library context.
- Integrate library core values, competencies, transformative leadership approaches, and attributes of library leaders in diverse library settings.
- Formulate a library leadership philosophy and development plan to adopt in professional endeavors based on a self-assessment of one's leadership traits, competencies, styles, strengths, and areas for future growth.
- Adopt principles and models of transformational leadership in practical library contexts as a result of knowledge gained though inquiry, discovery, discussion, observation, and self-introspection throughout the course.
- Work effectively in interactions and communications with others such as in teams and other collaborative endeavors, planning and decision making, coaching/mentoring, dealing with challenges, and promoting diversity in the community in which they serve.