Preface

This bookdown compiles lectures I wrote for my first attempt to teach a Data Science course to Food and Ag Science students at Virginia Tech. In general, the material here is aimed at advanced undergrads or first-year graduate students who are working in quantitative sciences, have taken 1-2 statistics courses, but do not have formal experience with coding or data science.

If you want to know more about how this was built, you can take a look at the github repo through this link or by clicking the edit button above.

If you want to know more about the course, you can take a look at the short description and learning objectives below:

Course description

Programming, algorithmic thinking, and data analysis for research projects in food and agricultural sciences. Writing R scripts to import, clean, and wrangle data. Data visualization, exploratory data analysis, and inference using programmatic approaches.

Data Analytics for Food and Ag Science introduces graduate students in Food Science & Technology to modern principles of coding for data wrangling and analysis. Students will develop competency in the R programming and data analysis language. They will learn basic principles of coding including control loops and functions, apply these skills to the acquisition, importing, and cleaning of data, and develop intuitive approaches to the exploratory and inferential analysis of data from food and agricultural sciences. Students will apply these competencies to several experiential projects in which they will be responsible for both defining and achieving analytical goals related to data they are assigned and will generate themselves.

Course Learning Objectives

  1. Achieve fluency in the use of R for data analysis including proficient use of integrated development environment tools, data transformation, control flow logic, troubleshooting, and debugging.
  2. Implement transparent, reproducible data analysis workflows for agricultural applications including data collection and manipulation, visualization, statistical analyses, and presentation of conclusions.
  3. Differentiate among appropriate analytical techniques to make informed operational decisions in agricultural settings.
  4. Develop long-term durability in problem solving and critical thinking by “learning to learn” different techniques and strategies for data analysis.

What should I do with this material?

This is the first time I have built a bookdown. The material here is the first time I taught a course on coding, and the first time I tried to teach it using R Markdown. That’s a lot of firsts! I am sure I have made many weird choices (read: mistakes), and so this is very much a “learn by doing” situation for me.

Please feel free to use this material if it’s helpful for you. I would appreciate feedback on it–you can email me at jlahne at vt dot edu with comments, suggestions, or to let me know how you’re using the material.