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@@ -11,26 +11,29 @@ This GitHub directory serves as the website and resource hub for **CSC 10800: Fo | |
| - Instructor: Zach Muhlbauer (he/him/his) <br /> | ||
| - Email: [[email protected]](mailto:[email protected]) <br /> | ||
| - Office Hours: **Wednesday 3 - 5pm via Zoom, or in person by appointment** <br /> | ||
| - Location: **TBD** | ||
| - Location: | ||
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| ### Quick Links | ||
| ### Important Links | ||
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| Linked below are resources and documents essential to your success in the course: | ||
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| - [Website](https://zmuhls.github.io/CCNY-Data-Science/) | ||
| - [Syllabus](https://github.com/zmuhls/TEST-CCNY-Data-Science/blob/main/syllabus) | ||
| - [Schedule](https://github.com/zmuhls/TEST-CCNY-Data-Science/blob/main/schedule) | ||
| - [Activities](https://github.com/zmuhls/TEST-CCNY-Data-Science/blob/main/activity) | ||
| - [Policies](https://github.com/zmuhls/TEST-CCNY-Data-Science/blob/main/policies) | ||
| - [Technology](https://github.com/zmuhls/TEST-CCNY-Data-Science/blob/main/technology.md) | ||
| - [Slides](https://github.com/zmuhls/TEST-CCNY-Data-Science/blob/main/slides) | ||
| - [Reading Group](https://hypothes.is/groups/yKvGZkjg/csc10800-annotation-group) | ||
| - [Additional Resources](https://github.com/zmuhls/TEST-CCNY-Data-Science/blob/main/additional_resources) | ||
| - [Syllabus](https://zmuhls.github.io/CCNY-Data-Science/syllabus/) | ||
| - [Schedule](https://zmuhls.github.io/CCNY-Data-Science/schedule/) | ||
| - [Policies](https://zmuhls.github.io/CCNY-Data-Science/policies/) | ||
| - [Technology](https://zmuhls.github.io/CCNY-Data-Science/technology) | ||
| - [Activities](https://zmuhls.github.io/CCNY-Data-Science/activities/) | ||
| - [Portfolio](https://zmuhls.github.io/CCNY-Data-Science/portfolio/) | ||
| - [Datasets](https://zmuhls.github.io/CCNY-Data-Science/datasets/) | ||
| - [Jupyter Notebooks](https://zmuhls.github.io/CCNY-Data-Science/notebooks/) | ||
| - [Hypothesis Group](https://hypothes.is/groups/yKvGZkjg/csc10800-annotation-group) | ||
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| ## Course Description | ||
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| This course introduces the fundamental concepts and computational techniques of data science to all students, including those majoring in the Arts, Humanities, and Social Sciences. Students engage with data arising from real-world phenomena—including literary corpora, spatial datasets, and social networks data—to learn analytical skills such as inferential thinking and computational thinking. | ||
| Over the semester, students will engage with a variety of datasets—ranging from literary corpora to social networks—to develop skills in computational and inferential thinking. The course is structured around key themes such as data ethics, digital humanities, and network analysis, with practical activities and projects reinforcing these concepts. Students will learn to use essential tools like GitHub, Jupyter Notebooks, and Python libraries, enabling them to navigate and contribute to data-driven fields. | ||
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| The competencies learned in this course will provide students with skills that will be of use in their professional careers, as well as tools to better understand, quantitatively and qualitatively, the social world around them. Finally, by teaching critical concepts and skills in computer programming and statistical inference, the class prepares students for further coursework in technology-aware fields of study, from Python programming and cultural analytics to the big umbrella of the Digital Humanities. The course is therefore designed for students who are new to statistics and programming. Students will make use of the Python programming language, but no computer science pre-requisites are required. | ||
| Each class session builds on previous material, with readings, coding exercises, and critical discussions that deepen students' understanding of both the technical and theoretical dimensions of data science. The course also emphasizes critical perspectives on data practices, encouraging students to interrogate the social, ethical, and cultural implications of data science in today's world. | ||
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| The course schedule includes activities such as Python scripting, data visualization, and sentiment analysis, culminating in a final project where students create a social coding portfolio. By the end of the course, students will be equipped with the technical skills and critical insights necessary for advanced study in technology-aware disciplines, including digital humanities and cultural analytics. | ||
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| This course does **not** satisfy degree requirements for Computer Science students, who should *not* be enrolled in this course. | ||