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Viewing: PP 232 : Big Data in Your Pocket: Call it a Smartphone

Last approved: Thu, 07 Sep 2017 08:02:19 GMT

Last edit: Wed, 06 Sep 2017 17:34:02 GMT

Change Type
Major
PP (Plant Pathology)
232
032496
Dual-Level Course
Cross-listed Course
No
Big Data in Your Pocket: Call it a Smartphone
Big Data in Your Pocket
College of Agriculture and Life Sciences
Entomology & Plant Pathology (11PP)
Term Offering
Fall Only
Offered Every Year
Fall 2017
Previously taught as Special Topics?
Yes
1
 
Course Prefix/NumberSemester/Term OfferedEnrollment
IPGE 295Fall 201623
Course Delivery
Face-to-Face (On Campus)

Grading Method
Letter Grade Only
3
16
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture3
Course Attribute(s)
GEP (Gen Ed)

If your course includes any of the following competencies, check all that apply.
University Competencies

Course Is Repeatable for Credit
No
 
 
Asimina Mila
Associate Professor

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture4040NoNone
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote


Is the course required or an elective for a Curriculum?
No
Data have been, are, and will be collected in every scientific discipline. Data provide a foundation to evaluate hypotheses and advance knowledge. For centuries scientists have collected data and built models separately with methods and principles defined in their disciplines. Modern technological advances have resulted in a data revolution. Data now come fast in all forms and in high volumes, presenting both new challenges and opportunities in many disciplines. In this course we will discuss how data is collected and visually summarized and how modern technology has allowed for the collection of big data, resulting in a revolution in the way we live, work, and think.

Nowadays plenty of information is available. We live in the era of the information revolution. Data come in all forms, volumes, and velocity. Also the methods and technologies used to collect data evolve fast. For instance, 3D cell growth or organs-on-a-chip gain fast momentum in biological sciences. Most professions are or will soon require some analytical skills, beyond any other technical training. This is an introductory course open to students from all backgrounds. The basic objective is to generate interest on the new data/information revolution and introduce students into some of the new technological innovations that may change the way data are collected and used. Several lectures are devoted to data visualization, an area of data exploratory analysis that is been currently under fast evolution. 


No

Is this a GEP Course?
Yes
GEP Categories
Interdisciplinary Perspectives
Humanities Open when gep_category = HUM
Each course in the Humanities category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Mathematical Sciences Open when gep_category = MATH
Each course in the Mathematial Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Natural Sciences Open when gep_category = NATSCI
Each course in the Natural Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Social Sciences Open when gep_category = SOCSCI
Each course in the Social Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Interdisciplinary Perspectives Open when gep_category = INTERDISC
Each course in the Interdisciplinary Perspectives category of the General Education Program will provide instruction and guidance that help students to:
 
 
• Upon successful completion of this course, students will be able to describe the types of data available and the methods used to collect them in social sciences, such as sociology and anthropology, biological and agricultural sciences such as public health and precision agriculture, and engineering such as machine system engineering. In particular, students will be able to contrast the differences between data from these disciplines.

• Upon successful completion of this course, students will be able to compare and contrast how the mathematical definition of a model is implemented in each of these disciplines and what role data play in model development.

• Upon successful completion of this course, students will be able to evaluate how and whether data massively collected nowadays in social sciences, such as sociology, economics and biological sciences such as public health and precision agriculture are useful in addressing a question in that particular discipline.
 
 
Exam questions:
1. What are the two approaches to collecting data in biological sciences? Can or cannot be used in social sciences? Why?
2. The theory of relativity is a: (i) conceptual (ii) physical (iii) mathematical model. (circle the correct answer(s))
3. Critique the following statement in Page 3: “a doctor will be able to assess the likely result of whichever treatment he or she is considering prescribing, backed up by the data from other patients with the same condition, genetic factors and lifestyle.”
 
 
• Upon successful completion of this course, students will be able to describe how, due to the complexity and the large-scale nature of data now being generated in the disciplines mentioned above, similar data analysis methods and technologies are used in all of these disciplines.

• Upon successful completion of this course, students will be able to describe how technology and the internet have changed the relationship between data and models in both biological sciences such as public health and precision agriculture and social sciences such as sociology, economics connecting them as disciplines more than ever.
 
 
Exam questions:
1. In class we watched a video where a company described how they predict the level of poverty around the globe with no survey data, traditionally used to develop such models. Describe (i) what kind of information/data the company uses, (ii) what type of technical skills and disciplines are involved and (iii) what technology is involved.
2. Several local and state governments consider Internet access as an important infrastructure to invest into. Can you think of an application where Internet access allows for simultaneous financial, managerial and environmental benefit by reducing pesticide use in agriculture?
 
 
• Upon successful completion of this course, students will be able to integrate knowledge from biological sciences such as public health and crop science and social sciences such as sociology and anthropology to create visual models and synthesize information by visually inspecting data. Particular emphasis will be placed on perceptions and fallacies created by inappropriate visualizations.
 
 
Project type:
Students were given a very large data set (from data.gov) where data on health, restaurant type and use, and socioeconomic variables per county and state in the USA is available. Students visually inspected the data, made connections between restaurant type and use given socioeconomic factors and derived conclusions on how these factors affect health. Data analysis, connections and conclusions were performed in Tableau.

 
 
PP 232 synthesizes knowledge from Biology (sub-discipline precision agriculture, crop science), Public Health, Sociology, and Economics to understand how biology, social attitudes, and data, together will drive innovation and change the shape of science and society within the next 20 years.
 
 
Material is presented in a context that enables students to understand types of data collected from Biology (sub-discipline precision agriculture and crop science), Public Health, Sociology, and Economics, their use to derive knowledge and innovation. This is a survey course and an in-depth knowledge of methods in data collection and statistical methods is not required. However some basic knowledge is necessary particularly with regards to types of data (quantitative and qualitative) and their relative value in each discipline to extract knowledge. Thus the few first lectures will be used to cover this topic. Colleagues from the departments of Sociology & Anthropology and Biological and Agricultural Engineering will give relevant disciplinary lectures. Subsequently, the concept of a model will be introduced. Students will be asked to bring in class an example of a model and justify their selection. After the initial intuitive introduction of a model, the mathematical definition of a model will be introduced with examples of models (theoretical or research-subject) from each discipline. The relationship between data and models and the explanation of why quantitative and qualitative data have different relative value among the disciplines in study will become clear with selected readings. The second part of the course will focus on data generation with mobile devices and uses of big data analytics. Several examples from applications such as Facebook and mobile apps will be conducted and discussed in class and readings on the role of mobile devices and social media will be provided. Videos and readings from the disciplines of genetics and sociology, economics (ie. Marketing) and public health will give discipline grounded understanding of data collection and the unique role technology potentially will play across disciplines in data collection and information extraction. Readings provided will lead students to re-examine the potential value of quantitative and qualitative data in Biology (sub-discipline precision agriculture), Public Health, Sociology, and Economics, as it compares with the traditional use of quantitative and qualitative data in each of these disciplines. Six lectures have been included: three on hands on practice and three on “case studies” from biology (precision agriculture, public health) and Sports analytics. Students are expected to use materials from those lectures to integrate the similarities and differences among disciplines as it relates to data types, methods of collection and approaches in model development. Finally data visualization is an important nowadays method of extracting information from large datasets. In this course several lectures and 2 in-class practice sessions using Tableau will be conducted to integrate data, and investigate relationships (ie. models) from biology (crop science) and sociology/anthropology. Students will describe similarities in visualization methods used in both disciplines as it is related to extracting information, storytelling, testing hypothesis, and building conceptual models.
Visual & Performing Arts Open when gep_category = VPA
Each course in the Visual and Performing Arts category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Health and Exercise Studies Open when gep_category = HES
Each course in the Health and Exercise Studies category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
&
 

 
 

 
 

 
 

Global Knowledge Open when gep_category = GLOBAL
Each course in the Global Knowledge category of the General Education Program will provide instruction and guidance that help students to achieve objective #1 plus at least one of objectives 2, 3, and 4:
 
 

 
 

 
Please complete at least 1 of the following student objectives.
 

 
 

 
 

 
 

 
 

 
 

US Diversity Open when gep_category = USDIV
Each course in the US Diversity category of the General Education Program will provide instruction and guidance that help students to achieve at least 2 of the following objectives:
Please complete at least 2 of the following student objectives.
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Requisites and Scheduling
100
 
a. If seats are restricted, describe the restrictions being applied.
 
NA
 
b. Is this restriction listed in the course catalog description for the course?
 
NA
 
List all course pre-requisites, co-requisites, and restrictive statements (ex: Jr standing; Chemistry majors only). If none, state none.
 
none
 
List any discipline specific background or skills that a student is expected to have prior to taking this course. If none, state none. (ex: ability to analyze historical text; prepare a lesson plan)
 
none
Additional Information
Complete the following 3 questions or attach a syllabus that includes this information. If a 400-level or dual level course, a syllabus is required.
 
Title and author of any required text or publications.
 

 
Major topics to be covered and required readings including laboratory and studio topics.
 

 
List any required field trips, out of class activities, and/or guest speakers.
 

College(s)Contact NameStatement Summary
Poole College of ManagementD. ShowalterMr. Showalter made PP 232 syllabus available to the committee members of the Data Science minor in Pool College of Management. There were no concerns about the materials of this course with regards to overlapping with courses offered through the minor in MGMT.
College of Humanities and Social SciencesM.CobbDr. Cobb expressed that the course is interesting and there may be students in CHASS that will be willing to take it for IDP credit. He also expressed that he hopes to see more towards Data Science within his college.
College of SciencesS. Muse"I don’t see any concerns from Statistics with this course. In fact, I think it is a course that many of our Statistics majors may want to consider taking for IDP credit- it looks fascinating."
There are sufficient resources (space and materials) to deliver the course.
The course is part of the official teaching appointment of the instructor.

- Get an understanding of how different disciplines collect, analyze data but why all have use for models.


- Analyze the role of technology in data collection and visualization


- Explain how vast amounts of data nowadays available have changed scientific and technological innovation.


Student Learning Outcomes

At the conclusion of this course, students will be able to:


Define the types of data generated in different disciplines and explain how data is collected.


Compare and contrast data collection in the past, present, and how data collection may change in the future.


Describe how developments in technology changed the role of data in innovation.


Evaluate how large amounts of data will change the way we will generate knowledge in the future.


Utilize visualization to summarize vast amounts of data.


Evaluation MethodWeighting/Points for EachDetails
Multiple exams60%There will be 3 scheduled exams. Each exam is worth 20% of the final grade for a total of 60%.
Participation10%See attached rubric for participation scoring
Project30%Project is assigned the 3rd week of classes. Teams meet regularly with instructor to discuss progress or questions and other concerns.
TopicTime Devoted to Each TopicActivity
Types of data; models; relationship between data and models7 lectures
Technology such as mobile devices and social media; their impact on data and models3 lectures
Hands-on experience3 lecturesDH Hill Library Makerspace - (i) 3D printing (ii) generate data with Arduino; use data to develop a model.
Successful stories with Big Data; projects in progress using Big data3 lecturesEach lecture is devoted to one case; cases are presented by graduate students working on projects involving big data in campus. Instructor works with guest lecturer to develop the lecture in a level that is understood and provide good information to class.
Data visualization6 lecturesInvited guest speakers from college of Design and Library services. 3 lectures hands on practice on data visualization using Tableau.
Student's projects2 or 3 lecturesStudent teams will present their project results in class.
Course wrap-up1 lectureCurrent status of data science, ideas about future directions, take home message from course.

Key: 13352