| date | week | topic | note |
|---|---|---|---|
| 05 June 2023 | 1 | Get ready for the course | no lecture |
| 12 June 2023 | 2 | Welcome! & Data Science Life-cycle | lecture 1 |
| 19 June 2023 | 3 | Exploratory data analysis using visualization & Data organization in spreadsheets | lecture 2 |
| 26 June 2023 | 4 | Data transformation and descriptive statistics | lecture 3 |
| 03 July 2023 | 5 | Concept of tidy data, vectors and pivoting | lecture 4 |
| 10 July 2023 | 6 | Joining data & data communication using Quarto | lecture 5 |
| 17 July 2023 | 7 | Capstone Project | no lecture |
| 28 July 2023 | 8 | Capstone Project submission due date | no lecture |
Course Overview
CVEN 5837: Special Topics - Data Analytics for Development
Summer Online - Session C - June 5, 2022 - July 28, 2023; Monday 12:00 - 2:30pm MT
Instructor: Lars Schöbitz
Course Information
This course provides students with skills in using the collection of R tidyverse packages as a tool for data analysis, reproducible research and communication. Lectures will be delivered through participatory live coding for students to learn how to write code in code-along exercises. We will use publicly available data related to waste management, air quality, and sanitation. Students will learn how to help themselves and others to build upon the obtained skills to apply them to their data analysis projects.
Topics include:
- The data science life-cycle
- Data organization in spreadsheets
- Exploratory data analysis using visualization
- Concept of tidy data and data tidying
- Data transformation and descriptive statistics
- Data communication using the Quarto open-source scientific and technical publishing system
Learning Goals
Be able to use a common set of data science tools (R, RStudio IDE, Git, GitHub, tidyverse, Quarto) to illustrate and communicate the utility of solutions for water, sanitation, air quality, and global health.
Learn to use the Quarto file format and the RStudio IDE visual editing mode to produce scholarly documents with citations, footnotes, cross-references, figures, and tables.
Textbooks and Materials
We will rely entirely on open source and open access material for this course. We will use “R for Data Science” by Hadley Wickham, and “Tidyverse Skills for Data Science” by Carrie Wright, Shannon E. Ellis, Stephanie C. Hicks and Roger D. Peng, as complementary reading and learning material for this course. Additional readings will consist of blog posts, journal articles, and reports. All required readings and class material will be provided through this website.
Course Calendar
Weekly Structure
| Monday | Lecture |
| Tuesday | |
| Wednesday | Feedback (grading) on assignments from previous week |
| Thursday | Student hours on Zoom (10 am to 12 pm CEST) |
| Friday | Homework assignment and learning reflection are due |
Assignments
Homework assignments: Each week will have at least one homework assignment. All assignments, but those for Week 1 are delivered as Quarto documents with instructions and some sample code. Students are required to submit their work through GitHub. Homework assignments are graded as pass/fail (100 points).
Readings/Learning reflections: Additional readings will be provided. Some are required, others are optional. Students will be asked to write 100 word reflections on the material that they have learned. These reflections are delivered as Quarto documents and graded as pass/fail (100 points). Students are required to submit their work through GitHub.
Capstone Project: A graded capstone project provides students with an opportunity to apply their skills and techniques to real-world data sets. Detailed instructions and a grading rubric for the capstone project will be provided during the course. The project will be delivered as Quarto documents and graded with an achieved number of points out 100 points. Students are required to submit their work through GitHub.
Attendance
We hope you can participate in all classes. Class participation is an essential component for successful completion of this course. If you have a valid reason to miss a class, we expect you to inform us before the beginning of the class.
Grading scheme
Your overall course grade will be comprised of the following components, and their weights:
- Homework assignments: 40 percent
- Learning reflections: 20 percent
- Capstone Project: 40 percent
Grades will be recorded in Canvas throughout the semester. Homework assignments and learning reflections receive a pass/fail only (100 points). The Capstone Project receives a number of points out of 100. At the end of the term, the scores on all assignments are weighted by the percentages given above to determine a semester score. Student grades will be determined as follows based on their score rounded to the nearest single decimal place:
| Conversion from percent to grades. | |
| grade | percent |
|---|---|
| A+ | 97 |
| A | 93 |
| A- | 90 |
| B+ | 87 |
| B | 83 |
| B- | 80 |
| C+ | 77 |
| C | 73 |
| C- | 70 |
| D+ | 67 |
| D | 63 |
| D- | 60 |
| F | 0 |
Late work, extensions, and special circumstances
Due dates are set and all work is due on the stated date. This helps students to keep pace through the course and allow staff to return marks and feedback timely. Submission on the due date might not always be possible when something gets in the way. We drop the lowest score for each of the assignments or learning reflections. That means you can miss one assignment or learning reflection and still achieve maximum score.
Special circumstances
If you have a documented reason for why you are unable to to complete an assignment in the course, the reason will be assessed at the end of the course by the course committee.
Classroom behavior
Both students and faculty are responsible for maintaining an appropriate learning environment in all instructional settings, whether in person, remote or online. Those who fail to adhere to such behavioral standards may be subject to discipline. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with race, color, national origin, sex, pregnancy, age, disability, creed, religion, sexual orientation, gender identity, gender expression, veteran status, political affiliation or political philosophy. For more information, see the policies on classroom behavior and the Student Conduct & Conflict Resolution policies.
Preferred student names and pronouns
CU Boulder recognizes that students’ legal information doesn’t always align with how they identify. Students may update their preferred names and pronouns via the student portal; those preferred names and pronouns are listed on instructors’ class rosters. In the absence of such updates, the name that appears on the class roster is the student’s legal name.
Honor code
All students enrolled in a University of Colorado Boulder course are responsible for knowing and adhering to the Honor Code academic integrity policy. Violations of the Honor Code may include, but are not limited to: plagiarism, cheating, fabrication, lying, bribery, threat, unauthorized access to academic materials, clicker fraud, submitting the same or similar work in more than one course without permission from all course instructors involved, and aiding academic dishonesty. All incidents of academic misconduct will be reported to the Honor Code (honor@colorado.edu); 303-492-5550). Students found responsible for violating the academic integrity policy will be subject to nonacademic sanctions from the Honor Code as well as academic sanctions from the faculty member. Additional information regarding the Honor Code academic integrity policy can be found on the Honor Code website.