diff --git a/.DS_Store b/.DS_Store index a90dbef..5fdcd05 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/activities.md b/activities.md index b4fc71d..410028c 100644 --- a/activities.md +++ b/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) diff --git a/assets/.DS_Store b/assets/.DS_Store index 56eb35d..60bed6f 100644 Binary files a/assets/.DS_Store and b/assets/.DS_Store differ diff --git a/datasets.md b/datasets.md index 3a88e52..611a04a 100644 --- a/datasets.md +++ b/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). diff --git a/overview.md b/overview.md index 4baffad..9a33bf2 100644 --- a/overview.md +++ b/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
**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 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 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 not 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 not 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) \ No newline at end of file +> +> * 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/schedule.md b/schedule.md index 5d2d2a6..48b0e87 100644 --- a/schedule.md +++ b/schedule.md @@ -7,7 +7,7 @@ parent: Syllabus # Schedule -Assigned readings, activities, and projects are linked below: all work is due on the date in the same row as the required reading or activity, and if you encounter any issues accessing materials, give me holler so I can fix it. +Assigned readings, activities, and projects are linked below. All work is due on the date in the same row as the required reading or activity. If you encounter an issue accessing materials, then give me holler! | Date | Topic/Theme | Critical Readings | Technical Readings | Due by Class | | ------------ | -------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | @@ -20,26 +20,27 @@ Assigned readings, activities, and projects are linked below: all work is due on | 09/17 (Tue) | Methods, Files, & Encoding | | Melanie Walsh: [Data Types](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/05-Data-Types.html);
[String Methods](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/06-String-Methods.html) | | | 09/19 (Thur) | Metaphors of Data | Annette Markham: [Undermining 'data'](https://firstmonday.org/ojs/index.php/fm/article/view/4868/3749) | Walsh: [Files and Character Encoding](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/07-Files-Character-Encoding.html) | —Annotate reading(s) | | 09/24 (Tue) | Control Flow | | Walsh: [Comparisons and Conditionals](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/08-Comparisons-Conditionals.html) | | -| 09/26 (Thur) | Computational Thinking | Jeanette Wing: [Computational Thinking](/assets/pdf/computational-thinking.pdf) | | **Activity 1**: [Building Blocks](/assets/activities/activity_1.ipynb) | +| 09/26 (Thur) | Computational Thinking | Jeanette Wing: [Computational Thinking](https://www.cs.cmu.edu/~15110-s13/Wing06-ct.pdf) | | **Activity 1**: [Building Blocks](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-1-building-blocks) | | 10/03 (Tue) | No Classes Scheduled | | | | -| 10/08 (Tue) | GitHub as a Platform | Nadia Eghbal: [GitHub as a Platform](https://www.google.com/books/edition/Working_in_Public/zxjBEAAAQBAJ?hl=en&gbpv=1&pg=PT6&printsec=frontcover) | Walsh: [Lists and Loops](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/10-Lists-Loops-Part2.html) | —Annotate reading
—Sign up for [GitHub](https://github.com) | +| 10/08 (Tue) | GitHub as a Platform | Nadia Eghbal: [GitHub as a Platform](https://www.google.com/books/edition/Working_in_Public/zxjBEAAAQBAJ?hl=en&gbpv=1&pg=PT6&printsec=frontcover) (PDF) | Walsh: [Lists and Loops](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/10-Lists-Loops-Part2.html) | —Annotate reading
—Sign up for [GitHub](https://github.com) | | 10/10 (Thur) | Batteries Included | | Walsh: [Functions](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/12-Functions.html) | | | 10/15 (Tue) | No Classes Scheduled - Monday Schedule | | | | -| 10/17 (Thur) | Python Review | | Walsh: [Common Python Errors](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/13-Common-Python-Errors.html) | **Activity 2**: [Python Primer](/assets/activities/activity_2.ipynb) | +| 10/17 (Thur) | Python Review | | Walsh: [Common Python Errors](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/13-Common-Python-Errors.html) | **Activity 2**: [Python Primer](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-2-python-primer) | | 10/22 (Tue) | Data Biographies | Heather Krause: [Data Biographies: Getting to Know Your Data](https://gijn.org/stories/data-biographies-getting-to-know-your-data) | Walsh: [Missing data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/03-Data-Analysis/02-Pandas-Basics-Part2.html#missing-data) | | | 10/24 (Thur) | Structure of the Repository | | Reitz: [Structure of the Repository](https://docs.python-guide.org/writing/structure/) | | | 10/29 (Tue) | 'Cleaning' Data | Rawson & Muñoz: [Against Cleaning](https://dhdebates.gc.cuny.edu/read/untitled-f2acf72c-a469-49d8-be35-67f9ac1e3a60/section/07154de9-4903-428e-9c61-7a92a6f22e51) | Alice Zhao: [Data Cleaning](https://github.com/adashofdata/nlp-in-python-tutorial/blob/master/1-Data-Cleaning.ipynb) | —Annotate reading(s) | | 10/31 (Thur) | Pandas Basics | Anelise Hanson Shrout: [(Re)Humanizing Data: Digitally Navigating the Bellevue Almshouse](https://crdh.rrchnm.org/essays/v01-10-(re)-humanizing-data/) | Walsh: [Panda Basics I](https://melaniewalsh.github.io/Intro-Cultural-Analytics/03-Data-Analysis/01-Pandas-Basics-Part1.html) | | | 11/05 (Tue) | Data Visualized | [Basic data visualizations](https://github.com/GCDigitalFellows/intro-pandas-dri-2022/blob/main/README.md#10-basic-data-visualizations) | Walsh: [Panda Basics I](https://melaniewalsh.github.io/Intro-Cultural-Analytics/03-Data-Analysis/01-Pandas-Basics-Part1.html) (cont'd) | | -| 11/07 (Thur) | Data Ethics | VPRO Documentary: [Shoshana Zuboff on Surveillance Capitalism](https://www.youtube.com/watch?v=hIXhnWUmMvw) (Optional) | Walsh: [Users’ Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/04-Data-Collection/01-User-Ethics-Legal-Concerns.html) | **Activity 3**: [Practicing Pandas](assets/activities/activity_3.ipynb) | -| 11/12 (Tue) | Distant Reading | Stephen Ramsay: [The Hermeneutics of Screwing Around; or What To Do With a Million Books](assets/pdf/Ramsay-Hermeneutics-of-Screwing-Around.pdf) | | —Explore [Voyant Tools](https://voyant-tools.org)
—Annotate reading | -| 11/14 (Thur) | Digital Hermeneutics | | Walsh: [TF-IDF with HathiTrust Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/05-Text-Analysis/02-TF-IDF-HathiTrust.html) | **Activity 4**: [Writing Docs](/assets/activities/activity_4.ipynb) | +| 11/07 (Thur) | Data Ethics | VPRO Documentary: [Shoshana Zuboff on Surveillance Capitalism](https://www.youtube.com/watch?v=hIXhnWUmMvw) (Optional) | Walsh: [Users’ Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/04-Data-Collection/01-User-Ethics-Legal-Concerns.html) | **Activity 3**: [Practicing Pandas](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-3-practicing-pandas) | +| 11/12 (Tue) | Distant Reading | Stephen Ramsay: [The Hermeneutics of Screwing Around; or What To Do With a Million Books](https://web.archive.org/web/20140604085234/http://www.playingwithhistory.com/wp-content/uploads/2010/04/hermeneutics.pdf) | | —Annotate reading | +| 11/14 (Thur) | Digital Hermeneutics | | Walsh: [TF-IDF with HathiTrust Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/05-Text-Analysis/02-TF-IDF-HathiTrust.html) | **Activity 4**: [Writing Docs](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-4-writing-docs) | | 11/19 (Tue) | Sentiment Analysis | Simone Rebora: [Sentiment Analysis in Literary Studies: A Critical Survey](https://www.digitalhumanities.org/dhq/vol/17/2/000691/000691.html) | Walsh: [Sentiment Analysis](https://melaniewalsh.github.io/Intro-Cultural-Analytics/05-Text-Analysis/04-Sentiment-Analysis.html) | | | 11/21 (Thur) | Network Analysis | | Walsh: [Network Analysis](https://melaniewalsh.github.io/Intro-Cultural-Analytics/06-Network-Analysis/01-Network-Analysis.html) | —Explore [Social Network Datasets](https://github.com/melaniewalsh/sample-social-network-datasets) | -| 11/26 (Tue) | Network Visualization | | Walsh: [Interactive Network Visualization](https://melaniewalsh.github.io/Intro-Cultural-Analytics/06-Network-Analysis/01-Network-Analysis.html) | **Activity 5**: [Data Visualization](/assets/activities/activity_5.ipynb) | +| 11/26 (Tue) | Network Visualization | | Walsh: [Interactive Network Visualization](https://melaniewalsh.github.io/Intro-Cultural-Analytics/06-Network-Analysis/01-Network-Analysis.html) | **Activity 5**: [Data Visualization](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-5-data-visualization) | | 11/28 (Thur) | No Class. College Closed. | | | | | 12/03 (Tue) | Reddit API Workshop | | [Reddit Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/04-Data-Collection/14-Reddit-Data.html) | | | 12/05 (Thur) | Coworking Lab | | | | | 12/10 (Tue) | Lightning Talks | | | Send slides by Sun | | 12/12 (Thur) | Lightning Talks | | | Send slides by Tue | -| 12/18 (Wed) | Last day to submit work | | | **Final**: **[Social Coding Portfolio](/portfolio.md)** | \ No newline at end of file +| 12/18 (Wed) | Last day to submit work | | | **Final**: **[Social Coding Portfolio](/portfolio.md)** | + diff --git a/syllabus.md b/syllabus.md index a340971..1660cba 100644 --- a/syllabus.md +++ b/syllabus.md @@ -7,7 +7,7 @@ has_children: true # Syllabus 📋 ## 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 1-3pm over Zoom, or in person by appointment +**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 📄 @@ -19,23 +19,19 @@ This course does not satisfy degree requirements for Computer S ## Course Materials 🗂️ -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). +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** 🔧 +**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 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. +**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 + - 100 pts (10%) for notebook and reflection - Social Coding Portfolio: 250 pts (25%) - Participation & Attendance: 100 pts (10%) @@ -44,11 +40,11 @@ The grading distribution below provides a glimpse of how your work will be evalu ## Schedule 📅 -Assigned readings, activities, and projects are linked below: all work is due on the date in the same row as the required reading or activity, and if you encounter any issues accessing materials, give me holler so I can fix it. +Assigned readings, activities, and projects are linked below. All work is due on the date in the same row as the required reading or activity. If you encounter an issue accessing materials, then give me holler! | Date | Topic/Theme | Critical Readings | Technical Readings | Due by Class | | ------------ | -------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | -| 08/29 (Thur) | Introductions | | | | +| 08/29 (Thur) | Introduction | | | | | 09/02 (Mon) | No Class. College Closed. | | | | | 09/03 (Tue) | What is Data Science? | Data 8: [What is Data Science? (Ch. 1.1-1.2)](https://inferentialthinking.com/chapters/01/what-is-data-science.html) | Melanie Walsh: [The Command Line](https://melaniewalsh.github.io/Intro-Cultural-Analytics/01-Command-Line/01-The-Command-Line.html) | —Join Hypothesis group via invite link & annotate readings | | 09/05 (Thur) | Whose Data Science? | D'Ignazio & Klein: [Why Data Science Needs Feminism](https://data-feminism.mitpress.mit.edu/pub/frfa9szd/release/6) | Walsh: [How to Use Jupyter Notebooks](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/02-How-to-Use-Jupyter-Notebooks.html) | —Annotate reading(s) | @@ -57,23 +53,23 @@ Assigned readings, activities, and projects are linked below: all work is due on | 09/17 (Tue) | Methods, Files, & Encoding | | Melanie Walsh: [Data Types](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/05-Data-Types.html);
[String Methods](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/06-String-Methods.html) | | | 09/19 (Thur) | Metaphors of Data | Annette Markham: [Undermining 'data'](https://firstmonday.org/ojs/index.php/fm/article/view/4868/3749) | Walsh: [Files and Character Encoding](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/07-Files-Character-Encoding.html) | —Annotate reading(s) | | 09/24 (Tue) | Control Flow | | Walsh: [Comparisons and Conditionals](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/08-Comparisons-Conditionals.html) | | -| 09/26 (Thur) | Computational Thinking | Jeanette Wing: [Computational Thinking](/assets/pdf/computational-thinking.pdf) | | **Activity 1**: [Building Blocks](/assets/activities/activity_1.ipynb) | +| 09/26 (Thur) | Computational Thinking | Jeanette Wing: [Computational Thinking](https://www.cs.cmu.edu/~15110-s13/Wing06-ct.pdf) | | **Activity 1**: [Building Blocks](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-1-building-blocks) | | 10/03 (Tue) | No Classes Scheduled | | | | -| 10/08 (Tue) | GitHub as a Platform | Nadia Eghbal: [GitHub as a Platform](https://www.google.com/books/edition/Working_in_Public/zxjBEAAAQBAJ?hl=en&gbpv=1&pg=PT6&printsec=frontcover) | Walsh: [Lists and Loops](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/10-Lists-Loops-Part2.html) | —Annotate reading
—Sign up for [GitHub](https://github.com) | +| 10/08 (Tue) | GitHub as a Platform | Nadia Eghbal: [GitHub as a Platform](https://www.google.com/books/edition/Working_in_Public/zxjBEAAAQBAJ?hl=en&gbpv=1&pg=PT6&printsec=frontcover) (PDF) | Walsh: [Lists and Loops](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/10-Lists-Loops-Part2.html) | —Annotate reading
—Sign up for [GitHub](https://github.com) | | 10/10 (Thur) | Batteries Included | | Walsh: [Functions](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/12-Functions.html) | | | 10/15 (Tue) | No Classes Scheduled - Monday Schedule | | | | -| 10/17 (Thur) | Python Review | | Walsh: [Common Python Errors](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/13-Common-Python-Errors.html) | **Activity 2**: [Python Primer](/assets/activities/activity_2.ipynb) | +| 10/17 (Thur) | Python Review | | Walsh: [Common Python Errors](https://melaniewalsh.github.io/Intro-Cultural-Analytics/02-Python/13-Common-Python-Errors.html) | **Activity 2**: [Python Primer](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-2-python-primer) | | 10/22 (Tue) | Data Biographies | Heather Krause: [Data Biographies: Getting to Know Your Data](https://gijn.org/stories/data-biographies-getting-to-know-your-data) | Walsh: [Missing data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/03-Data-Analysis/02-Pandas-Basics-Part2.html#missing-data) | | | 10/24 (Thur) | Structure of the Repository | | Reitz: [Structure of the Repository](https://docs.python-guide.org/writing/structure/) | | | 10/29 (Tue) | 'Cleaning' Data | Rawson & Muñoz: [Against Cleaning](https://dhdebates.gc.cuny.edu/read/untitled-f2acf72c-a469-49d8-be35-67f9ac1e3a60/section/07154de9-4903-428e-9c61-7a92a6f22e51) | Alice Zhao: [Data Cleaning](https://github.com/adashofdata/nlp-in-python-tutorial/blob/master/1-Data-Cleaning.ipynb) | —Annotate reading(s) | | 10/31 (Thur) | Pandas Basics | Anelise Hanson Shrout: [(Re)Humanizing Data: Digitally Navigating the Bellevue Almshouse](https://crdh.rrchnm.org/essays/v01-10-(re)-humanizing-data/) | Walsh: [Panda Basics I](https://melaniewalsh.github.io/Intro-Cultural-Analytics/03-Data-Analysis/01-Pandas-Basics-Part1.html) | | | 11/05 (Tue) | Data Visualized | [Basic data visualizations](https://github.com/GCDigitalFellows/intro-pandas-dri-2022/blob/main/README.md#10-basic-data-visualizations) | Walsh: [Panda Basics I](https://melaniewalsh.github.io/Intro-Cultural-Analytics/03-Data-Analysis/01-Pandas-Basics-Part1.html) (cont'd) | | -| 11/07 (Thur) | Data Ethics | VPRO Documentary: [Shoshana Zuboff on Surveillance Capitalism](https://www.youtube.com/watch?v=hIXhnWUmMvw) (Optional) | Walsh: [Users’ Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/04-Data-Collection/01-User-Ethics-Legal-Concerns.html) | **Activity 3**: [Practicing Pandas](assets/activities/activity_3.ipynb) | -| 11/12 (Tue) | Distant Reading | Stephen Ramsay: [The Hermeneutics of Screwing Around; or What To Do With a Million Books](assets/pdf/Ramsay-Hermeneutics-of-Screwing-Around.pdf) | | —Explore [Voyant Tools](https://voyant-tools.org)
—Annotate reading | -| 11/14 (Thur) | Digital Hermeneutics | | Walsh: [TF-IDF with HathiTrust Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/05-Text-Analysis/02-TF-IDF-HathiTrust.html) | **Activity 4**: [Writing Docs](/assets/activities/activity_4.ipynb) | +| 11/07 (Thur) | Data Ethics | VPRO Documentary: [Shoshana Zuboff on Surveillance Capitalism](https://www.youtube.com/watch?v=hIXhnWUmMvw) (Optional) | Walsh: [Users’ Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/04-Data-Collection/01-User-Ethics-Legal-Concerns.html) | **Activity 3**: [Practicing Pandas](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-3-practicing-pandas) | +| 11/12 (Tue) | Distant Reading | Stephen Ramsay: [The Hermeneutics of Screwing Around; or What To Do With a Million Books](https://web.archive.org/web/20140604085234/http://www.playingwithhistory.com/wp-content/uploads/2010/04/hermeneutics.pdf) | | —Annotate reading | +| 11/14 (Thur) | Digital Hermeneutics | | Walsh: [TF-IDF with HathiTrust Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/05-Text-Analysis/02-TF-IDF-HathiTrust.html) | **Activity 4**: [Writing Docs](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-4-writing-docs) | | 11/19 (Tue) | Sentiment Analysis | Simone Rebora: [Sentiment Analysis in Literary Studies: A Critical Survey](https://www.digitalhumanities.org/dhq/vol/17/2/000691/000691.html) | Walsh: [Sentiment Analysis](https://melaniewalsh.github.io/Intro-Cultural-Analytics/05-Text-Analysis/04-Sentiment-Analysis.html) | | | 11/21 (Thur) | Network Analysis | | Walsh: [Network Analysis](https://melaniewalsh.github.io/Intro-Cultural-Analytics/06-Network-Analysis/01-Network-Analysis.html) | —Explore [Social Network Datasets](https://github.com/melaniewalsh/sample-social-network-datasets) | -| 11/26 (Tue) | Network Visualization | | Walsh: [Interactive Network Visualization](https://melaniewalsh.github.io/Intro-Cultural-Analytics/06-Network-Analysis/01-Network-Analysis.html) | **Activity 5**: [Data Visualization](/assets/activities/activity_5.ipynb) | +| 11/26 (Tue) | Network Visualization | | Walsh: [Interactive Network Visualization](https://melaniewalsh.github.io/Intro-Cultural-Analytics/06-Network-Analysis/01-Network-Analysis.html) | **Activity 5**: [Data Visualization](https://zmuhls.github.io/CCNY-Data-Science/activities/#activity-5-data-visualization) | | 11/28 (Thur) | No Class. College Closed. | | | | | 12/03 (Tue) | Reddit API Workshop | | [Reddit Data](https://melaniewalsh.github.io/Intro-Cultural-Analytics/04-Data-Collection/14-Reddit-Data.html) | | | 12/05 (Thur) | Coworking Lab | | | | @@ -93,13 +89,13 @@ Need a spare laptop? Try CCNY's [Laptop Loaner Program](https://portal.ccny.cuny ### Software 🖥️ -**Jupyter Notebook:** In-class lessons and homeworks are done in Jupyter Notebook so that we can use [markdown]() and Python simultaneously. The notebooks assume a Python 3 installation with the standard modules from the **Anaconda** **installation** (e.g. NLTK, Pandas, Numpy and Matplotlib) linked on the [schedule](/syllabus.md). +**Jupyter Notebook:** In-class lessons and homeworks are done in Jupyter Notebook so that we can use markdown and Python simultaneously. The notebooks assume a Python 3 installation with the standard modules from the **Anaconda** **installation** (e.g. NLTK, Pandas, Numpy and Matplotlib) linked on the [schedule](https://zmuhls.github.io/CCNY-Data-Science/schedule/). **Hypothesis Annotation:** Expect to post 2-3 annotations for most critical readings we do in this class, each about 25-50 words in length and assessed on thoughtfulness, style and craft, and a demonstrated effort to respond to others. Hypothesis can also be a useful research tool and means of information management, so I encourage you to engage with it throughout the course. ## Class Policies ⚐ -**Respect and accountability are crucial to productive class discussions.** As co-producers of knowledge, I am expecting that we will practice respect for each other and be accountable to our words and actions. The classroom space is a learning space that can be, at times, uncomfortable, especially as we speak through our different perspectives and experiences. As long as we strive to be respectful to each other and accountable to the opinions, comments, questions, and concerns we share, this learning space will become a great place for us to nudge our boundaries. +**Respect and accountability are crucial to productive class discussions.**As co-producers of knowledge, I am expecting that we will practice respect for each other and be accountable to our words and actions. The classroom space is a learning space that can be, at times, uncomfortable, especially as we speak through our different perspectives and experiences. As long as we strive to be respectful to each other and accountable to the opinions, comments, questions, and concerns we share, this learning space will become a great place for us to nudge our boundaries. **Attendance and participation is required.** Learning a new programming language requires consistent practice and your understanding of the material will be greatly facilitated by your participation in class. Students are expected to come to class prepared, which includes completing the assigned reading before class and being ready to engage in class discussions. diff --git a/technology.md b/technology.md index 2075ccb..4b48df9 100644 --- a/technology.md +++ b/technology.md @@ -17,6 +17,6 @@ Need a spare laptop? Try CCNY's [Laptop Loaner Program](https://portal.ccny.cuny ## Software 🖥️ -**Jupyter Notebook:** In-class lessons and homeworks are done in Jupyter Notebook so that we can use [markdown]() and Python simultaneously. The notebooks assume a Python 3 installation with the standard modules from the **Anaconda** **installation** (e.g. NLTK, Pandas, Numpy and Matplotlib) linked on the [schedule](/syllabus.md). +**Jupyter Notebook:** In-class lessons and homeworks are done in Jupyter Notebook so that we can use markdown and Python simultaneously. The notebooks assume a Python 3 installation with the standard modules from the **Anaconda** **installation** (e.g. NLTK, Pandas, Numpy and Matplotlib) linked on the [schedule](https://zmuhls.github.io/CCNY-Data-Science/schedule/). -**Hypothesis Annotation:** Expect to post 2-3 annotations for most critical readings we do in this class, each about 25-50 words in length and assessed on thoughtfulness, style and craft, and a demonstrated effort to respond to others. Hypothesis can also be a useful research tool and means of information management, so I encourage you to engage with it throughout the course. \ No newline at end of file +**Hypothesis Annotation:** Expect to post 2-3 annotations for most critical readings we do in this class, each about 25-50 words in length and assessed on thoughtfulness, style and craft, and a demonstrated effort to respond to others. Hypothesis can also be a useful research tool and means of information management, so I encourage you to engage with it throughout the course. Moreover, if you have not yet joined our private annotation group, please reach out to me for the invite link. \ No newline at end of file