diff --git a/.DS_Store b/.DS_Store index e3f198c..4398d1b 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/assets/.DS_Store b/assets/.DS_Store index 932ade3..782d341 100644 Binary files a/assets/.DS_Store and b/assets/.DS_Store differ diff --git a/assets/notebooks/.DS_Store b/assets/notebooks/.DS_Store index ca753fa..b1b37ed 100644 Binary files a/assets/notebooks/.DS_Store and b/assets/notebooks/.DS_Store differ diff --git a/assets/notebooks/pandas_2.ipynb b/assets/notebooks/pandas_ex.ipynb similarity index 100% rename from assets/notebooks/pandas_2.ipynb rename to assets/notebooks/pandas_ex.ipynb diff --git a/assets/pdf/SNA-slides.pdf b/assets/pdf/SNA-slides.pdf new file mode 100644 index 0000000..67d67c7 Binary files /dev/null and b/assets/pdf/SNA-slides.pdf differ diff --git a/cover.png b/cover.png new file mode 100644 index 0000000..28b4b23 Binary files /dev/null and b/cover.png differ diff --git a/icon.jpg b/icon.jpg new file mode 100644 index 0000000..8b7c479 Binary files /dev/null and b/icon.jpg differ diff --git a/overview.md b/overview.md index 41ad7e5..1b67793 100644 --- a/overview.md +++ b/overview.md @@ -4,40 +4,40 @@ layout: default nav_order: 01 parent: Syllabus --- -> # *Overview đź“‹* -> -> ## Course Details đź“Ś -> -> **Section**: CSC 10800 (LEC): Foundations of Data Science
**Dates**: Tue/Thu, 3:30-4:45pm, Aug 28 - Dec 21
**Location**: Marshak Science Building, Rm 410
**Instructor**: Prof. Zach Muhlbauer, [zmuhlbauer@gc.cuny.edu](mailto:zmuhlbauer@gc.cuny.edu)
**Office Hours**: Wed 3-5pm over Zoom, or in person by appointment -> -> ## Course Description 📄 -> -> 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. -> -> 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. -> -> This course does not satisfy degree requirements for Computer Science students, who should *not* be enrolled in this course. - -> ## Course Materials 🗂️ -> -> All required reading materials, activities, and instructions are provided on the [Schedule](https://zmuhls.github.io/CCNY-Data-Science/schedule/) page. Additionally, datasets are provided on the [Datasets](https://zmuhls.github.io/CCNY-Data-Science/datasets/) page, and assets for the course website are hosted [here](https://github.com/zmuhls/ccny-data-science). -> -> **Technical Readings**: These readings draw from Melanie Walsh's open-access [Introduction to Cultural Analytics and Python ](https://melaniewalsh.github.io/Intro-Cultural-Analytics/welcome.html)(2021), an online textbook written for students in humanities and social sciences to gain a practical introduction to the Python programming language within the context of cultural analysis. The textbook demonstrates how Python can be applied to a wide range of cultural materials, such as magazine articles, classic novels, TV scripts, technical manuals, social networks, and so more. -> -> **Critical Readings**: These readings engage with the complex social and political dimensions of "big data" in contemporary U.S. society. Through them, we will explore how data has evolved into the world's most valuable commodity. Authors of these pieces will therefore challenge us to critically engage with the ethical concerns, power imbalances, and hidden costs associated with today's data-driven economy. -> -> ## Grading Distribution 🧮 -> -> The grading distribution below offers a glimpse of how your work will be evaluated over the semester: -> -> * Collaborative Annotations: 150 pts (15%) -> -> * Programming Activities: 500 pts (50%) -> -> * 100 pts (10%) for notebook and reflection -> -> * Social Coding Portfolio: 250 pts (25%) -> -> * Participation & Attendance: 100 pts (10%) -> -> > **Total Available Points:** 1000 (100% or A) \ No newline at end of file +# Overview 📋 + +## Course Details 📌 + +**Section**: CSC 10800 (LEC): Foundations of Data Science
**Dates**: Tue/Thu, 3:30-4:45pm, Aug 28 - Dec 21
**Location**: Marshak Science Building, Rm 410
**Instructor**: Prof. Zach Muhlbauer, [zmuhlbauer@gc.cuny.edu](mailto:zmuhlbauer@gc.cuny.edu)
**Office Hours**: Wed 3-5pm over Zoom, or in person by appointment + +## Course Description 📄 + +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. + +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. + +**NB**: This course does not satisfy degree requirements for Computer Science students, who should *not* be enrolled in this course. + +## Course Materials 🗂️ + +All required reading materials, activities, and instructions are provided on the [Schedule](https://zmuhls.github.io/CCNY-Data-Science/schedule/) page. Additionally, datasets are provided on the [Datasets](https://zmuhls.github.io/CCNY-Data-Science/datasets/) page, and assets for the course website are hosted [here](https://github.com/zmuhls/ccny-data-science). + +**Technical Readings**: These readings draw from Melanie Walsh's open-access [Introduction to Cultural Analytics and Python ](https://melaniewalsh.github.io/Intro-Cultural-Analytics/welcome.html)(2021), an online textbook written for students in humanities and social sciences to gain hands-on experience with the Python programming language within the context of critical-cultural analysis. The textbook demonstrates how Python can be applied to a wide range of cultural materials, such as magazine articles, classic novels, TV scripts, technical manuals, social networks, and so more. + +**Critical Readings**: These readings engage with the complex social and political dimensions of "big data" in contemporary U.S. society. Through them, we will explore how data has evolved into the world's most valuable commodity. Authors of these pieces will therefore challenge us to critically engage with the ethical concerns, power imbalances, and hidden costs associated with today's data-driven economy. + +## Grading Distribution 🧮 + +The grading distribution below offers a glimpse of how your work will be evaluated over the semester:
+ +Collaborative Annotations: 150 pts (15%)
+ +Programming Activities: 500 pts (50%)
+ +* 100 pts (10%) for notebook and reflection + +Social Coding Portfolio: 250 pts (25%)
+ +Participation & Attendance: 100 pts (10%)
+ + **Total Available Points:** 1000 (100% or A) \ No newline at end of file diff --git a/portfolio.md b/portfolio.md index 3e25065..b179714 100644 --- a/portfolio.md +++ b/portfolio.md @@ -2,7 +2,6 @@ title: Portfolio layout: default nav_order: 04 -has_children: false --- # Social Coding Portfolio