resolve dead links across site

activities.md

@@ -22,29 +22,29 @@ has_children: false
   #### Activity 1: Building Blocks
 
   - Due: 09/26 (Thur) 
-  - Downloadable [Instructions: Activity 1](/assets/activities/activity_1.ipynb)
+  - Downloadable [Instructions: Activity 1](https://colab.research.google.com/github/zmuhls/CCNY-Data-Science/blob/main/assets/activities/activity_1.ipynb)
   - Submission format: .md (Markdown) and .ipynb (Jupyter Notebook)
 
   #### Activity 2: Python Primer
 
   - Due: 10/17 (Thur)
-  - Downloadable [Instructions: Activity 2](/assets/activities/activity_2.ipynb)
+  - Downloadable [Instructions: Activity 2](https://colab.research.google.com/github/zmuhls/CCNY-Data-Science/blob/main/assets/activities/activity_2.ipynb)
   - Submission format: .md (Markdown) and .ipynb (Jupyter Notebook)
 
   #### Activity 3: Practicing Pandas
 
   - Due: 11/07 (Thur)
-  - Downloadable [Instructions: Activity 3](/assets/activities/activity_3.ipynb)
+  - Downloadable [Instructions: Activity 3](https://colab.research.google.com/github/zmuhls/CCNY-Data-Science/blob/main/assets/activities/activity_3.ipynb)
   - Submission format: .md (Markdown) and .ipynb (Jupyter Notebook)
 
   #### Activity 4: Writing Docs
 
   - Due: 11/14 (Thur)
-  - Downloadable [Instructions: Activity 4](/assets/activities/activity_4.ipynb)
+  - Downloadable [Instructions: Activity 4](https://github.com/zmuhls/CCNY-Data-Science/blob/main/assets/activities/activity_4.md)
   - Submission format: .md (Markdown) and .ipynb (Jupyter Notebook) 
 
   #### Activity 5: Data Visualization
 
   - Due: 11/26 (Tue)
-  - Downloadable [Instructions: Activity 5](/assets/activities/activity_5.pdf)
+  - Downloadable [Instructions: Activity 5](https://github.com/zmuhls/CCNY-Data-Science/blob/main/assets/activities/activity_5.pdf)
   - Submission format: .md (Markdown) and .ipynb (Jupyter Notebook)

datasets.md

@@ -15,7 +15,7 @@ For ease of retrieval, please make sure to save the datasets in the same folder
 - [Social Network Datasets](https://github.com/melaniewalsh/sample-social-network-datasets)
 - [Data 8 List of Datasets](https://github.com/data-8/textbook/tree/main/assets/data)
 
-Except for _Command Line Practice_ and _Data 8_, the datasets used in class are retrieved from Melanie Walsh's [Introduction to Cultural Analytics & Python](https://melaniewalsh.github.io/Intro-Cultural-Analytics/00-Datasets/00-Datasets.html) and related work of hers on GitHub. 
+Except for Data 8's list of datasets, the one used in class are retrieved from Melanie Walsh's [Introduction to Cultural Analytics & Python](https://melaniewalsh.github.io/Intro-Cultural-Analytics/00-Datasets/00-Datasets.html) and related work of hers on GitHub. 
 
-The _COVID-19 Vaccine Twitter Archive_ is modified from from [Gabriel Preda's COVID-19 All Vaccines Tweets dataset](https://www.kaggle.com/datasets/gpreda/all-covid19-vaccines-tweets).
+The _COVID-19 Vaccine Twitter Archive_ is modified from [Gabriel Preda's COVID-19 All Vaccines Tweets dataset](https://www.kaggle.com/datasets/gpreda/all-covid19-vaccines-tweets).
 

overview.md

@@ -4,40 +4,40 @@ layout: default
 nav_order: 01
 parent: Syllabus
 ---
-> # Overview 📋
+> # *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 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 1-3pm 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 🗂️
+> 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.
 >
-> All required reading materials, activities, and instructions are provided on the [Schedule](/schedule.md) page. Additionally, datasets are provided on the [Datasets](/datasets) page, and the assets for this course website are [available here](https://github.com/zmuhls/ccny-data-science). 
+> 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.
 >
-> **Technical Readings** 🔧 
+> This course does <strong>not</strong> satisfy degree requirements for Computer Science students, who should *not* be enrolled in this course.
+>
+> ## Course Materials 🗂️
 >
-> 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.  
+> 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). 
 >
-> **Critical Readings** 📚
+> **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.  
 >
-> 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. 
+> **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 provides a glimpse of how your work will be evaluated throughout the semester:
+> 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 each notebook + reflection
-> 
-> - Social Coding Portfolio: 250 pts (25%)
->- Participation & Attendance: 100 pts (10%)
-> 
-> >  **Total Available Points:** 1000 (100% or A)
+>
+> * 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)