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

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.

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.

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.

*Note that no more than 6 s.h. of course work for a major will be counted towards the Certificate.*

The certificate requires 21 credits from the courses listed below depending on the area of interest.

**Prerequisites: (not counted as certificate courses)**

◦ CS:1210 or equivalent Computer Science I: Fundamentals (or ENGR:2730)

◦ MATH:1850 or equivalent Calculus I

◦ MATH:1860 or equivalent Calculus II

◦ MATH:2700 Introduction to Linear Algebra

**Level I (6 s.h.)**

◦ MATH:3800/CS:3700 Elementary Numerical Analysis

◦ STAT:2010 Statistical Methods and Computing

(this class is open to non-Statistics majors the day after Early Registration ends.)

**Level II (9 s.h.)**

**This course:** STAT:3200 Applied Linear Regression

** Two of the following:**

◦ MSCI:3200 Database Management

◦ CS:4700/MATH:4860 High Performance and Parallel Computing

◦ MATH:4820/CS:4720 Optimization Techniques

◦ CS:4980 Topics in Computer Science II (consult advisor for section approval)

◦ STAT:5400 Computing in Statistics

◦ CS:5430 Machine Learning

◦ CS:5630 Cloud Computing Technology

➥ STAT:5810/BIOS:5310 Research Data Management may be substituted for STAT:5400 with advisor approval.

➥ **Related courses such as **Topological Data Analysis, Big Data Analytics, and selected Topics in CS (e.g. Information Visualization or Internet Measurements and Analytics) may be accepted under Level II.

**Level III (6 s.h.)**- CS/MATH/STAT:4740 Large Data Analysis (capstone course)
- MSCI:4480/CS:4480 Knowledge Discovery (Known as MSCI:6421/CS:6421 prior to Fall 2016)

New requirements will apply to students declaring the Certificate in Fall 2017 or later.

The undergraduate Certificate in Large Data Analysis requires a minimum of 18 s.h. 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.

Prerequisites (or their equivalents) for the certificate include the following.

- CS:1210 Computer Science I: Fundamentals
- MATH:1850 Calculus I
- MATH:1860 Calculus II
- MATH:2700 Introduction to Linear Algebra
- STAT:2010 Statistical Methods and Computing 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.)**

◦ MATH:3800/CS:3700 Elementary Numerical Analysis

◦ STAT:3200/IE:3760/IGPI:3200 Applied Linear Regression

**Level II (6 s.h.)**

**Two of these:**

◦ CS:4700/MATH:4860 High Performance and Parallel Computing

◦ MATH:4820/CS:4720 Optimization Techniques

◦ MSCI:3200 Database Management

◦ STAT:4580/IGPI:4580 Data Visualization and Data Technologies

**Level III (3 s.h.)**

**One of these:**

◦ CS:5430 Machine Learning

◦ CS:5630 Cloud Computing Technology

◦ IE:4172 Big Data Analytics

◦ MSCI:3500 Data Mining

◦ MSCI:4480/CS:4480/ECE:4480 Knowledge Discovery

◦ STAT:4540/IGPI:4540 Statistical Learning

**Capstone Course (3 s.h.)**

**This course:**

◦ CS:4740/IGPI:4740/MATH:4740/STAT:4740 Large Data Analysis (must be taken within 30 s.h. of graduation)

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.

- 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
- Padmini Srinivasan, Computer Science
- Nick Street, Management Sciences
- David Stewart, Mathematics
- Aixin Tan, Statistics and Actuarial Science
- Lihe Wang, Mathematics
- Tianbao Yang, Computer Science

- 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

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.