Data Carpentry for Biologists

WIS 6934, 3 Credits, Fall 2017


Dr. Ethan White and Kristina Riemer

Office: Room 1 in Building 150 (just north of Newins-Zeigler)

Email (best way to contact us):,

Phone: 352-294-2081

Times & Location

Note: class is in different locations depending on the day of the week

Tuesdays, 4:05-4:55, 222 Newins-Ziegler Hall

Fridays, 11:45-1:40, 219 Newins-Ziegler Hall

Office Hours

Times: Monday 2-3:15 and Wednesday 11-12

Location: Newins-Zeigler 203

Or by appointment. Note: my schedule gets very busy during the semester so please try to schedule appointments as far in advance as possible. In general it will be very difficult to set up appointments less than 24 hours in advance.

Course TA

Andrew Marx



The syllabus and other relevant class information and resources will be posted at Changes to the schedule will be posted to this site so please try to check it periodically for updates.

Course Communications


Required Texts

There is no required text book for this class.

Course Description

Computers are increasingly essential to the study of all aspects of biology. Data management skills are needed for entering data without errors, storing it in a usable way, and extracting key aspects of the data for analysis. Basic programming is required for everything from accessing and managing data, to statistical analysis, to modeling. This course will provide an introduction to data management, manipulation, and analysis, with an emphasis on biological problems. Class will typically consist of short introductions or question & answer sessions, followed by hands on computing exercises. The course will be taught using R and SQLite, but the concepts learned will easily apply to all programming languages and database management systems. No background in programming of databases is required.

Prerequisite Knowledge and Skills

Knowledge of basic biology.

Purpose of Course

In this course you will learn all of the fundamental aspects of computer programming that are necessary for conducting biological research. By the end of the course you will be able to use these tools to import data into R, perform analysis on that data, and export the results to graphs, text files, and databases. By learning how to get the computer to do your work for you, you will be able to do more science faster.

Course Goals and Objectives

Students completing this course will be able to:

Course Project

Projects offer an opportunity to work with bigger data-related computing tasks and learn specific computing tools you need for your research. Projects can involve programming, databases, or both. They should be on something you are excited about.

As a rough guideline projects should represent ~30-40 hours of work. Some class time will be provided for working on projects.

Get more details about the project from the Projects Introduction.

How this course relates to the Student Learning Outcomes in Wildlife Ecology and Conservation

This course contributes to the ‘Quantitative Skills’ and ‘Conducting and Analyzing Independent/Original Research’ Student Learning Outcomes specified in the Ph.D. and MS in Wildlife Ecology and Conservation Academic Assessment Plans, by providing students the skills and knowledge they need to manage and analyze the data used in research.

Teaching Philosophy

This class is taught using a flipped, learner-centered, approach, because learning to program and work with data requires actively working on computers. Flipped classes work well for all kinds of content, but I think they work particularly well for computer oriented classes. If you’re interested in knowing more take a look at this great info-graphic.

Instructional Methods

As a flipped classroom, students are provided with either reading or video material that they are expected to view/read prior to class. Classes will involve brief refreshers on new concepts followed by working on exercises in class that cover that concept. While students are working on exercises the instructor will actively engage with students to help them understand material they find confusing, explain misunderstandings and help identify mistakes that are preventing students from completing the exercises, and discuss novel applications and alternative approaches to the data analysis challenges students are attempting to solve. For more challenging topics class may start with 20-30 minute demonstrations on the concepts followed by time to work on exercises.

Course Policies

Attendance Policy

Attendance will not be taken or factor into the grades for this class. However, experience suggests that students who regularly miss class struggle to learn the material.

Quiz/Exam Policy

There are no quizzes or exams in this course.

Make-up policy

Late assignments will be docked 20% and will not be accepted more than 48 hours late except in cases of genuine emergencies that can be documented by the student or in cases where this has been discussed and approved in advance. This policy is based on the idea that in order to learn how to use computers well, students should be working with them at multiple times each week. Time has been allotted in class for working on assignments and students are expected to work on them outside of class. It is intended that you should have finished as much of the assignment as you can based on what we have covered in class by the following class period. Therefore, even if something unexpected happens at the last minute you should already be close to done with the assignment. This policy also allows rapid feedback to be provided to students by returning assignments quickly.

Assignment policy

Assignments are due Monday night by 11:59 pm Eastern Time. Assignments should be submitted via Canvas.

Course Technology

Students are required to provide their own laptops and to install free and open source software on those laptops (see Setup for installation instructions). Support will be provided by the instructor in the installation of required software. If you don’t have access to a laptop please contact the instructor and they will do their best to provide you with one.

UF Policies

University Policy on Accommodating Students with Disabilities

Students requesting accommodation for disabilities must first register with the Dean of Students Office ( The Dean of Students Office will provide documentation to the student who must then provide this documentation to the instructor when requesting accommodation. You must submit this documentation prior to submitting assignments or taking the quizzes or exams. Accommodations are not retroactive, therefore, students should contact the office as soon as possible in the term for which they are seeking accommodations.

University Policy on Academic Misconduct

Academic honesty and integrity are fundamental values of the University community. Students should be sure that they understand the UF Student Honor Code at

Netiquette and Communication Courtesy

All members of the class are expected to follow rules of common courtesy in all email messages, threaded discussions and chats.

Getting Help

For issues with technical difficulties for E-learning in Canvas, please contact the UF Help Desk at:

Any requests for make-ups due to technical issues MUST be accompanied by the ticket number received from LSS when the problem was reported to them. The ticket number will document the time and date of the problem. You MUST e-mail your instructor within 24 hours of the technical difficulty if you wish to request a make-up.

Other resources are available at for:

Should you have any complaints with your experience in this course please visit to submit a complaint.

Most importantly, if you are struggling for any reason please come talk to me and I will do my best to help.

Grading Policies

Grading for this course will revolve around a combination of assignments (75%) and an independent project (25%).

There will be 11 equally weighted assignments. One problem from each assignment (selected at the instructors discretion after the assignments have been submitted) will receive a thorough code review and a detailed grade. Other problems will be graded as follows:

Independent projects may focus on databases, programming, or a combination or the two.

Grading scale

Course Schedule

The details course schedule is available on the course website at:

Disclaimer: This syllabus represents my current plans and objectives. As we go through the semester, those plans may need to change to enhance the class learning opportunity. Such changes, communicated clearly, are not unusual and should be expected.