fix overview / add images

zmuhls · Aug 28, 2024 · e491ac1 · e491ac1
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diff --git a/.DS_Store b/.DS_Store
diff --git a/assets/.DS_Store b/assets/.DS_Store
diff --git a/assets/notebooks/.DS_Store b/assets/notebooks/.DS_Store
diff --git a/assets/notebooks/pandas_2.ipynb → assets/notebooks/pandas_ex.ipynb b/assets/notebooks/pandas_2.ipynb → assets/notebooks/pandas_ex.ipynb
diff --git a/assets/pdf/SNA-slides.pdf b/assets/pdf/SNA-slides.pdf
diff --git a/cover.png b/cover.png
diff --git a/icon.jpg b/icon.jpg
diff --git a/overview.md b/overview.md
@@ -4,40 +4,40 @@ layout: default
 nav_order: 01
 parent: Syllabus
 ---
-> # *Overview 📋*
->
-> ## Course Details 📌
->
-> **Section**: CSC 10800 (LEC): Foundations of Data Science <br />**Dates**: Tue/Thu, 3:30-4:45pm, Aug 28 - Dec 21<br />**Location**: Marshak Science Building, Rm 410  <br />**Instructor**: Prof. Zach Muhlbauer, [zmuhlbauer@gc.cuny.edu](mailto:zmuhlbauer@gc.cuny.edu)<br />**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 <strong>not</strong> 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)
+# Overview 📋
+
+## Course Details 📌
+
+**Section**: CSC 10800 (LEC): Foundations of Data Science <br />**Dates**: Tue/Thu, 3:30-4:45pm, Aug 28 - Dec 21<br />**Location**: Marshak Science Building, Rm 410  <br />**Instructor**: Prof. Zach Muhlbauer, [zmuhlbauer@gc.cuny.edu](mailto:zmuhlbauer@gc.cuny.edu)<br />**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 <strong>not</strong> 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:<br />
+
+Collaborative Annotations: 150 pts (15%)<br />
+
+Programming Activities: 500 pts (50%)<br />
+
+* 100 pts (10%) for notebook and reflection  
+
+Social Coding Portfolio: 250 pts (25%)<br />
+
+Participation & Attendance: 100 pts (10%)<br />
+
+ **Total Available Points:** 1000 (100% or A)
diff --git a/portfolio.md b/portfolio.md
@@ -2,7 +2,6 @@
 title: Portfolio
 layout: default
 nav_order: 04
-has_children: false
 ---
 # Social Coding Portfolio