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Synchronous Online, Summer, Not Offered Summer 2024

1 Credit

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:
•���� ��Overview of qualitative and quantitative research methods and tools
•���� ��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

1.�� ����Be able to use a common set of data science tools (R, RStudio IDE, tidyverse, Quarto) to illustrate and communicate the utility of solutions for water, sanitation, air quality, and global health.
2.���� ��Be able to apply open research principles to a data analysis and communication project using Git version control system and GitHub for collaboration.

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 will be provided through Canvas.

Weekly Structure

Mon:��Homework assignment is due
Tue:�� Lecture
Wed:��Feedback (grading) on assignment
Thur:��Student hours on Zoom
Fri:���� ��Learning reflections are due

Assignments

Homework assignments: Each week will have at least one homework assignment. These assignments are delivered as Quarto documents with instructions and some sample code. Students are required to submit their work through Canvas. Homework assignments are graded. The scoring will be presented on Canvas.

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 graded. Scoring will be presented on Canvas.

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: 75 percent
•���� ��Learning reflections: 25 percent

Grades will be recorded in Canvas throughout the semester. 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 semester score rounded to the nearest single decimal place:

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.

Late work policy

Late work will be accepted up to 2 working days after the deadline with 25% penalty for each day. That is Wednesday after deadline for the homework assignments and Tuesday after deadline for the learning reflections. Work that is handed in more than two working days after the due date will be graded with 0 percent unless a documented reason for special circumstances is provided.

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.