College of Liberal Arts & Sciences
Large Data Analysis Certificate
The Large Data Analysis Certificate is an opportunity for you to stand out in your field. This is a credential that you can get along with your BA or BS that shows that you have expertise in this important and growing area. Flyer
Frequently Asked Questions
To declare your intention to complete this certificate visit the Office of Academic Programs & Student Development in Room 120 SH. You can also declare the certificate using MyUI. Upon graduation, certificate students must declare the Large Data Analysis Certificate when submitting the application for degree.
Classes you take before can be counted. If you are interested in this certificate, contact the affiliated faculty. For financial support, contact the academic coordinator.
Download and fill out a plan of study and email to the academic coordinator.
Courses requirements are shown below.
Your major advisor may refer to the "LDA Certificate Advising/Fact Sheet" for guidance.
What is Large Data Analysis?
Large Data Analysis is a new area for handling, processing, and extracting information from large data sets. As computers have become faster and smaller, so too have sensors, and the amount of information that we can gather about the world around us has exploded. Large data analysis techniques enable us to use these data for a wide range of applications, such as
- finding out what's under the ground (seismic data),
- identifying groups of people via Facebook,
- understanding the genome,
- searching for the causes of diseases and ways of preventing, controlling, or curing them.
We need computer science to know how to handle the large amounts of data and how to implement the algorithms to process them, statistics to see what can and what cannot be legitimately inferred from the data, and mathematics for the algorithms and methods for connecting these things.
What is a Certificate?
A Certificate is an official University credential saying that you have learned a specific area (usually related to major in some way). The advantage of a Certificate, is that it shows that you have additional expertise in this particular area. This provides clear evidence to any potential employer or graduate school that you have interest and abilities in this speciality.
The Large Data Analysis is an undergraduate Certificate offered through the College of Liberal Arts and Sciences (CLAS).
The University of Iowa offers a number of Certificates in a wide range of areas.
Note: You can get a CLAS certificate without having earned a baccalaureate degree.
The Certificate in Large Data Analysis addresses the need for people with the quantitative and computational skills to make sense of massive data. Expertise in this field involves computational and algorithmic skills to efficiently process large data sets, statistical analysis to understand if correlations seen in large data sets are significant, and mathematical skills to develop and understand the underlying algorithms for the data analysis.
Requirements for the Large Data Analysis Certificate (Click here for requirements prior to Fall 2017 matriculation)
The undergraduate Certificate in Large Data Analysis requires a minimum of 18 s.h. Students must maintain a g.p.a. of at least 2.00 in work for the certificate. The certificate may be earned by any student admitted to the University of Iowa who is not concurrently enrolled in a UI graduate or professional degree program.
Students majoring in computer science, mathematics, or statistics may count a maximum of 6 s.h. of course work for their major toward the certificate. Students pursuing other majors should consult with their major advisors to ascertain whether they may count certificate course work toward their majors.
Most of the certificate courses have prerequisites not included in the certificate requirements. Students need to select courses for which they have met the prerequisites.
Some of the prerequisites (or their equivalents) for the certificate include the following.
CODE | TITLE | HOURS |
---|---|---|
CS:1210 | Computer Science I: Fundamentals | 4 |
MATH:1850 | Calculus I | 4 |
or MATH:1550 | Engineering Mathematics I: Single Variable Calculus | |
MATH:1860 | Calculus II | 4 |
or MATH:1560 | Engineering Mathematics II: Multivariable Calculus | |
MATH:2700 | Introduction to Linear Algebra | 4 |
STAT:2010 | Statistical Methods and Computing | 3 |
or STAT:2020 | Probability and Statistics for the Engineering and Physical Sciences |
The Certificate in Large Data Analysis requires the following course work.
- Level I (6 s.h.)
Both of these:
Code | Title | Hours | Tentative Schedule |
MATH:3800/CS:3700 | Elementary Numerical Analysis | 3 | Fall, Summer, Spring |
STAT:3200/IE:3760/IGPI:3200 | Applied Linear Regression | 3 | Spring, Fall * |
* Stat majors have priority in the Fall.
- Level II (6 s.h.)
Two of these:
CS:4700/MATH:4860 | High Performance and Parallel Computing | 3 | Fall (Odd years only) |
MATH:4820/CS:4720 | Optimization Techniques | 3 | Spring |
MSCI:3200 | Database Management | 3 | Fall, Spring |
or CS:2420 | Databases for Informatics | Fall (CHECK) | |
or CS:4400 | Database Systems | Fall, Spring | |
STAT:4580/IGPI:4580 | Data Visualization and Data Technologies | 3 | Spring |
- Level III (3 s.h.)
One of these:
CS:5430 | Machine Learning | 3 | Fall |
CS:5630 | Cloud Computing Technology | 3 | Every other year |
IE:4172 | Big Data Analytics | 3 | Fall |
MSCI:3500 | Data Mining | 3 | Fall, Spring |
MSCI:4480/CS:4480/ECE:4480/IGPI:4480 | Knowledge Discovery | 3 | Fall |
STAT:4540/IGPI:4540 | Statistical Learning | 3 | Fall |
- Capstone Course (3 s.h.)
This course:
CS:4740/IGPI:4740/MATH:4740/STAT:4740 | Large Data Analysis (should be taken within 30 s.h. of graduation) | 3 | Spring |
Sample plans of study for the following majors shown below. These are meant simply to be a guide for how you might complete your major and the Large Data Analysis Certificate.
Students majoring in other Departments and Programs can also join the Certificate program.
Affiliated faculty
- Raman Aravamudhan, Computer Science
- Bruce Ayati, Mathematics
- Kate Cowles, Statistics and Actuarial Science
- Rodica Curtu, Mathematics
- Isabel Darcy, Mathematics
- Rhonda DeCook, Statistics and Actuarial Science
- Oguz Durumeric, Mathematics
- Ted Herman, Computer Science
- Palle Jorgensen, Mathematics
- Joseph Kearney, Computer Science
- Amaury Lendasse, Mechanical and Industrial Engineering
- Tong Li, Mathematics
- Suely Oliveira, Computer Science
- Walter Seaman, Mathematics, and College of Education
- Zubair Shafiq, Computer Science
- Sanvesh Srivastava, Statistics and Actuarial Science
- Padmini Srinivasan, Computer Science
- Nick Street, Management Sciences
- David Stewart, Mathematics
- Aixin Tan, Statistics and Actuarial Science
- Lihe Wang, Mathematics
- Tong Wang, Management Sciences
- Tianbao Yang, Computer Science
Advisory Board
- Suely Oliveira, Computer Science (Coordinator)
- Dan Anderson, Mathematics (DEO)
- Bruce Ayati, Mathematics
- Kate Cowles, Statistics and Actuarial Science
- Isabel Darcy, Mathematics
- Joseph Lang, Statistics and Actuarial Science (DEO)
- Alberto Segre, Computer Science (DEO)
- David Stewart, Mathematics
Related Activity: Big Data Summer School
Support from the National Science Foundation
Some faculty have NSF funding to support undergraduate students doing research on large data analytics during the academic year. More information about the grant is here.