Data Science for Agriculture

PLNT 5110, 3 Credits, Fall 2017

Professor

Phillip D. Alderman

Office: 274 Agricultural Hall

Email (best way to contact me): phillip.alderman@okstate.edu

Phone: 405-744-9604

Times & Location

TR, 9-10:15, 401 Agricultural Hall

Office Hours

Times: Tuesday 10:30 am - 12 pm, Wednesday 9:30 - 11 am, , or by appointment

Location: 274 Agricultural Hall

Note: Please try to schedule appointments as far in advance as possible. In general it will be difficult to set up appointments less than 24 hours in advance.

Website

The syllabus and other relevant class information and resources will be posted at http://palderman.github.io/DataSciAg. Changes to the schedule will be posted to this site so please try to check it periodically for updates.

Course Communications

Email: phillip.alderman@okstate.edu

You can expect to receive a response within two business days. To ensure a prompt response, please use the format DSA: X for your subject line, where X is the subject of your email.

Required Texts

There is no required text book for this class.

Course Description

Computers are incredibly powerful tools for managing data and doing scientific research. Yet despite this great potential, many scientists lack the skills needed to use computers to their full potential. This course provides an introduction to some of the basic skills needed for effective scientific computing. The course will provide an overview of:

• Data management, manipulation, and analysis
• Basic programming
• Workflow design

Although the course will be taught primarily using R, the concepts learned through the course should be transferable to other programming languages and database management systems. No background in programming or database management is required.

Purpose of Course

In this course you will learn some fundamental aspects of computer programming needed for conducting scientific research. By the end of the course you will be able to use these tools to import data into R, perform analysis on those data, and export the results as graphs, text files, or whatever else you might need. By learning how to get the computer to do your work for you, you will be able to do more faster.

Course Goals and Objectives

Students completing this course will be able to:

• Manipulate data and conduct data analysis in R
• Write simple computer programs in R
• Systematically track changes in project files
• Create a workflow for automating data processing and analysis
• Apply these tools to address research questions

Teaching Philosophy

Students learn more when they are actively engaged in the learning process. This is especially true when the learning objectives involve mastering skills that students must be able to do. Thus, this course will be taught using a flipped clasroom approach. This means that students will spend most class time actually practicing the scientific computing skills they are meant to learn.

Instructional Methods

As a flipped classroom, students will be provided with either reading or video material that they are expected to view/read prior to class. The first few minutes of class will review this material and answer any questions from students. After this, the instructor will lead students through interactive exercises related to the theme of the class period.

Course Policies

Attendance Policy

Attendance will not be taken or factor into the grades for this class. However, students who regularly miss class will likely perform poorly.

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, which is crucial to learning.

Course Technology

Students are required to provide their laptops and to install free and open source software on those laptops. Support will be provided by the instructor in the installation of required software.

Grading Policies

Grading for this course will revolve around a combination of assignments (72%), practical quizzes (24%), and a cumulative final exam (4%)

Grading scale

• A 90-100
• B 80-89.99
• C 70-79.99
• D 60-69.99
• F <60

Assignments

There will be 12 equally-weighted assignments. Problems will be graded as follows:

• Produces the correct answer using the requested approach: 100%
• Generally uses the right approach, but a minor mistake results in an incorrect answer: 90%
• Attempts to solve the problem and makes some progress using the core concept: 50%
• Answer demonstrates a lack of understanding of the core concept: 0%

In the event you believe the grading of an assignment to be unfair or incorrect, you may submit an appeal by email stating why you believe the answer you provided was correct. Your appeal should include a clear, well-reasoned argument including references to materials that support your claim. Appeals must be submitted within 48 hours of the time that the assignment is returned to you.

Assignments are due by 11:59 pm Central Time on the due date. Assignments should be submitted via the appropriate D2L Dropbox. Generally we will spend two-three class periods on each lesson and the associated assignment will be due the day after the last class period for that lesson.

Practical Quizzes

Pratical quizzes will be given at the conclusion of each of the 12 lessons. Students will be required to perform a set of pertinent tasks within a limited amount of time without the aid of notes. Points assigned to a task will be in accordance with the complexity of the task.

Cumulative Final Exam

The cumulative final exam will follow the same format as the practical quizzes except that the set of required tasks may include content from any part of the course.

Course Schedule

A detailed course schedule is available on the course website at: http://palderman.github.io/DataSciAg/schedule.

Disclaimer: This syllabus represents the general plans and objectives for the course. Throughout the semester, the syllabus may change to enhance the learning experience in response to student needs. Such changes will be communicated clearly and promptly to all students.

Oklahoma State University Policies

Please review the Fall 2017 Syllabus Attachement for an overview of university-wide policies and academic resources.