# Post-Baccalaureate Certificate curriculum

## Academic requirements

To satisfy all program requirements for the Post-Baccalaureate Certificate admitted students must:

- Complete a minimum of four courses (at least 12 credits) to include one Tier 1 course from the Statistics track, one Tier 1 course from the Algorithmics track, and one Tier 1 course from the Infrastructure track, plus one additional elective course chosen from the complete list.

The elective course should be determined with consultation from the program advisor or Director of Graduate Studies. The elective course can be an additional Tier I course, or a course from the Tier II lists or other electives list. Please note that transfer courses are not allowed.

- Maintain a minimum GPA set by the program (currently 3.0), including any courses taken to fulfill a missing admissions prerequisite (though these latter courses do not count toward the certificate).

### Statistics

#### Tier I Courses

- STAT 5101 - Theory of Statistics I / MATH 5651 - Basic Theory of Probability and Statistics (STAT 5101 and MATH 5651 are equivalencies)
- STAT 5102 - Theory of Statistics II
- STAT 5302 - Applied Regression Analysis
- STAT 5511 - Time Series Analysis
- STAT 5401 - Applied Multivariate Methods
- STAT 8051 - Applied Statistical Methods 1: Computing and Generalized Linear Models
- STAT 8101 - Theory of Statistics I
- STAT 8102 - Theory of Statistics II
- PUBH 7401 - Fundamentals of Biostatistical Inference
- PUBH 7402 - Biostatistics Modeling and Methods
- PUBH 7440 - Introduction to Bayesian Analysis

#### Tier II Courses

- AST/STAT 5731 - Bayesian Astrostatistics
- PUBH 8401 - Linear Models
- PUBH 8432 - Probability Models for Biostatistics
- PUBH 7405 - Biostatistics Regression
- PUBH 7406 - Advanced Regression and Design
- PUBH 7407 - Analysis of Categorical Data
- PUBH 7430 - Statistical Methods for Correlated Data
- PUBH 7460 - Advanced Statistical Computing
- PUBH 7485 - Methods for Causal Inference
- PUBH 8442 - Bayesian Decision Theory
- STAT 5052 - Statistical and Machine Learning
- STAT 5201 - Sampling Methodology in Finite Populations
- STAT 5303 - Designing Experiments
- STAT 5421 - Analysis of Categorical Data
- STAT 5601 - Nonparametric Methods
- STAT 5701 - Statistical Computing
- STAT 8112 - Mathematical Statistics II
- EE 5531 Probability and Stochastic Processes
- EE 8581 - Detection and Estimation Theory
- Any course from a list of STAT/Biostat 5xxx/8xxx classes (but not STAT 5021) with advisor and DGS approval

### Algorithmics

#### Tier I Courses

- CSCI 5521 - Introduction to Machine Learning (formerly Pattern Recognition)
- CSCI 5523 - Introduction to Data Mining
- CSCI 5525 - Machine Learning
- EE 8591 - Predictive Learning from Data
- PUBH 7475 - Statistical Learning and Data Mining
- PUBH 8475 - Statistical Learning and Data Mining

#### Tier II Courses

- CSCI 5302 - Analysis of Numerical Algorithms
- CSCI 5304 - Computational Aspects of Matrix Theory
- CSCI 5511 - Artificial Intelligence I
- CSCI 5512 - Artificial Intelligence II
- CSCI 5609 - Visualization (renumbered from CSCI 5109)
- CSCI 8314 - Sparse Matrix Computations
- CSCI 8581 - Big Data in Astrophysics
- EE 5239 - Introduction to Nonlinear Optimization
- EE 5251 - Optimal Filtering and Estimation
- EE 5389 - Introduction to Predictive Learning
- EE 5391 - Computing With Neural Networks
- EE 5542 - Adaptive Digital Signal Processing
- EE 5551 - Multiscale and Multirate Signal Processing
- EE 5561 - Image Processing and Applications
- EE 5581 - Information Theory and Coding
- EE 5585 - Data Compression
- EE 8231 - Optimization Theory
- IE 5531 - Engineering Optimization I
- IE 8521 - Optimization
- IE 8531 - Discrete Optimization
- Any advanced class in optimization, game theory, or topic related to the listed Algorithmics courses (with advisor and DGS approval)

### Infrastructure and Large Scale Computing

#### Tier I Courses

- CSCI 5105 - Introduction to Distributed Systems
- CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
- CSCI 5707 - Principles of Database Systems
- CSCI 5708 - Architecture and Implementation of Database Management Systems
- EE 5351 - Applied Parallel Programming
- EE 8367/CSCI 8205 - Parallel Computer Organization

#### Tier II Courses

- CSCI 5103 - Operating Systems
- CSCI 5211 - Data Communications and Computer Networks
- CSCI 5231 - Wireless and Sensor Networks
- CSCI 5271 - Introduction to Computer Security
- CSCI 5715 - From GPS and Virtual Globes to Spatial Computing
- CSCI 5751 - Big Data Engineering and Architecture
- CSCI 5801 - Software Engineering I
- CSCI 5802 - Software Engineering II
- CSCI 8102 - Foundations of Distributed Computing
- CSCI 8701 - Overview of Database Research
- CSCI 8715 - Spatial Databases and Applications
- CSCI 8725 - Databases for Bioinformatics
- CSCI 8735 - Advanced Database Systems
- CSCI 8801 - Advanced Software Engineering
- EE 5355 - Algorithmic Techniques for Scalable Many-core Computing
- EE 5371 - Computer Systems Performance Measurement and Evaluation
- EE 5381 - Telecommunications Networks
- EE 5501 - Digital Communication
- Any advanced class in large-scale data management or analysis, or topic related to the listed Infrastructure courses (with advisor and DGS approval)

### Other electives

- CSCI 5106 - Programming Languages
- CSCI 5123 - Recommender Systems
- CSCI 5421 - Advanced Algorithms and Data Structures
- CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics
- CSCI 5561 - Computer Vision
- CSCI 8271 - Security and Privacy in Computing
- CSCI 8363 - Numerical Linear Algebra in Data Exploration
- EE 5393 - Circuits, Computation and Biology
- GEOG 8920 - Urban Mobility & Accessibility
- IE 8535 - Introduction to Network Science
- MATH 5467 - Introduction to the Mathematics of Image and Data Analysis
- PUBH 7445 - Statistics for Human Genetics and Molecular Biology
- PUBH 7461 - Exploring and Visualizing Data in R
- PUBH 8445 - Statistics for Human Genetics and Molecular Biology
- PUBH 8446 - Advanced Statistical Genetics and Genomics
- PUBH 8472 - Spatial Biostatistics
- 5xxx 8xxx topics classes in CSCI, EE, STAT (but not STAT 5021), PUBH, etc. (See advisor for approval)
- Any advanced class in advanced data driven applications or emerging applications (with advisor and DGS approval)
**Note: No PUBH 6xxx, 5xxx, or 4xxx level courses can be used as electives in Data Science**

## Transfer to the M.S. degree

Students enrolled in the certificate program are encouraged to apply for the master’s program, and will receive priority consideration, though admission is not guaranteed.

Any courses taken as part of this certificate program can be used if the student is later admitted to the Data Science M.S. degree. However, the M.S. degree requires a minimum GPA of 3.25, higher than that required for the certificate.

A student maintaining a minimum GPA of 3.25 in their courses for the certificate program may apply for the M.S. degree using the usual procedure, and they may use all the same application material originally submitted for the application to this certificate, plus a letter from a U of MN faculty member.