Ut ischool courses6/13/2023 By learning the underlying concepts, students will be better prepared to help design networked systems that not only work well today, but also develop systems that can be easily adapted for the needs and technologies of tomorrow. The primary objective is to provide a conceptual understanding of the topics of the day through concrete hands-on examples of implementation. While students are expected to have basic computer competencies per the School of Information Sciences admissions requirements, the goal of the course is to provide practical detailed knowledge of the technology for all levels of competency. The course also explores alternatives for administering IT and how to assess emerging technologies and their applicability to library settings. The course steps students through choosing, installing, and managing computer hardware and operating systems, as well as networking hardware and software. Hands-on introduction to technology systems for use in information environments. IS 380 Consulting for Information Professionals Students will be exposed to a range of opportunities to apply storytelling thinking as a tool to identify the audience, design means to communicate with them, and develop dynamic triangle of people sharing stories, engaging in constructive dialogs and reinterpreting etc. It also aims to draw students to explore how a story foregrounds bridge-building dialog, affects the power of information, and thus maximize human potential. The goal is to introduce students to the philosophical, social, and relational dynamics of "story" among people as all human storyteller as well as organizations of all sizes across a wide spectrum of fields including library as storytelling organizations. In the context of our school, the iSchool at the University of Illinois, we propose to approach strategic communication from the perspective of storytelling thinking. Restricted to Sophomore, Junior, or Senior standing.Ĭommunicating with the right audience in the right way connotes creation of ways and approaches that can serve diverse populations within and beyond a particular or specific culture. Students should also be comfortable with basic geometry concepts such as points, lines, and distances. Either STAT/CS/ IS 107, IS 205, INFO 407, or at least 1 semester of programming experience using Python and Pandas is recommended as a prerequisite. Prerequisite: Students should be familiar with the concepts of tabular data (tables) and data types (categorical, ordinal, continuous, etc.) and be able to implement these concepts in Python using Pandas. Students will learn how to use the scikit-learn Python library for machine learning during this course. Most of the course activities will use Python with the Pandas library, which students should already be proficient using. The course will include lectures, readings, homework assignments, exams, and a class project. We situate the course components in the "data science life cycle" as part of the larger set of practices in the discovery and communication of scientific findings. Lastly, we will discuss deep neural networks and other methods at the forefront of machine learning. We will cover classification and regression using these models, as well as methods needed to handle large datasets. Model types will include decision trees, linear models, nearest neighbor methods, and others as time permits. We will focus on concepts and methods for predictive learning: estimating models from data to predict unknown outcomes. Machine learning covers predictive and descriptive learning, and bridges theoretical and empirical ideas across disciplines. A dramatic increase in computing power has enabled new areas of data science to develop in statistical modeling and artificial intelligence, often called Machine Learning.
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