Skip to content

Syllabus

Part 1: Course Information

Class Time and Location: Online Asynchronous

Instructor:

Xiaomeng Jin
Department of Environmental Sciences
Office: ENR 230
Email: xiaomeng.jin@rutgers.edu
Office Hour: TBD

Part 2: Overview

This course will introduce data analysis techniques for applications in environmental sciences. The course will teach students scientific programming in Python, statistical analysis, visualization, spatial analysis techniques that are commonly used to process and interpret environmental datasets. The course is designed to be accessible for graduate and upper-level undergraduate students in environmental sciences or other related disciplines.

Part 3: Course Structure

Format: This is an online asynchronous course, meaning that we do not ‘meet’, not even via the web. Therefore, you decide when to do the work. To prevent you from procrastinating too much, you will have an assignment due each week for the first 12 weeks. Your assignment each week is to follow the instructions to complete a Jupyter notebook. By the end of the semester, you should have a notebook collection that you can use as coding recipe for your final project and your future research/work.
Textbook: There is no required textbook. All materials will come from free online resources and the course website itself.
Computers: Students will have the option to use their laptop, Amarel (the university’s high performance computing cluster), or Google’s Colaboratory (https://colab.research.google.com) to work on their assignments and final project.

Part 4: AI Use Policy

AI-use policies vary by assignment, and you should follow the specific instructions provided for each assignment.

For the first several Python assignments, you should refrain from using AI because you are still learning basic programming syntax and problem-solving skills. Using AI too early may interfere with the development of these foundational skills.

Later in the semester, we will introduce AI-focused modules and assignments that encourage you to use AI to explore more advanced approaches and solve more challenging problems. For these assignments, AI should function as a tutor and collaborator, not a solution generator. You are responsible for understanding your work and should be transparent about how AI was used.

Permitted uses include:

  • Explaining programming and data-analysis concepts
  • Interpreting error messages and suggesting debugging strategies
  • Helping you learn programming syntax and functions
  • Improving code readability, including comments, formatting, and variable names

Part 5: Grading Policy

Weekly Assignments (70%)

Type 1: Python Programming (50%)

• Complete Jupyter Notebooks, including coding examples, exercise and assignment questions.

Type 2: Weekly Quiz (20%)

• Multiple choice or short answer questions

Final Project (30%)

The goal of the final project is to assess your ability to combine and apply the skills you have learned in class in the context of a real-world research problem. Our class has mostly focused on tools for environmental data analysis, so this must be the focus of your final project. Specifically, we seek to assess your ability to do the following tasks:
• Discover and download real datasets in standard formats (e.g. CSV, netCDF)
• Load the data into pandas or xarray, performing any necessary data cleanup (dealing with missing values, proper time encoding, etc.) along the way.
• Perform realistic scientific calculation involving, for example tasks such as data grouping, aggregating, correlation analysis, trend analysis.
• Visualize your results in well-formatted plots.
• Clearly document your analysis to make it reproducible.
• Publish your final project as a GitHub repository.

Grading

• Total: 100
• Data: 30
• Statistical analysis: 30
• Visualization: 20
• Clean, efficient, reproducible code: 20