Optional Curricula for the MS in Statistics

Students may complete one of the three optional curricula as part of their MS program. May also choose to do none of these options. The notes on the MS degree also apply.

Option in Quality and Productivity Management

Eight required core courses in Statistics:
1.  Regression Analysis (960:563)
2.  Basic Probability (960:580) or Introduction to Methods and Theory of Probability (960:582)
3.  Methods of Statistical Inference (960:583)
4.  Interpretation of Data I (960:586)
5.  Design of Experiments (960:590)
6.  Advanced Design of Experiments (960:591)
7.  Statistical Quality Control I (960:540)
8.  Life Data Analysis (960:542)

Two required courses in Industrial Engineering:
9.  Systems Reliability Engineering I (540:585)
10.  Quality Management (540:580)

Option in Biostatistics

Eight required courses:
1.  Regression Analysis (960:563)
2.  Basic Probability (960:580) or Introduction to Methods and Theory of Probability (960:582)
3.  Methods of Statistical Inference (960:583)
4.  Interpretation of Data I (960:586)
5.  Design of Experiments (960:590)
6.  Biostatistics I -- Methods for Observational Studies (960:584)
7.  Biostatistics II -- Methods for Controlled Experiments (960:585)
8.  Categorical Data Analysis (960:553) or Life Data Analysis (960:542)

Two optional courses:
1.  Life Data Analysis (960:542)
2.  Statistical Practice (960:545)
3.  Categorical Data Analysis (960:553)
4.  Methods in Nonparametric Statistics (960:555)
5.  Applied Time Series Analysis (960:565)
6.  Applied Multivariate Analysis (960:567)
7.  Survey Sampling (960:576)
8.  Interpretation of Data II (960:587)
9.  Data Mining (960:588)
10.  Adv Design of Experiments (960:591)
11.  Other related advanced courses (approval of graduate adviser)

Option in Data Mining

Seven required courses in Statistics:
1.  Regression Analysis (960:563)
2.  Applied Multivariate Analysis (960:567)
3.  Basic Probability (960:580) or Introduction to Methods and Theory of Probability (960:582)
4.  Methods of Statistical Inference (960:583)
5.  Interpretation of Data I (960:586)
6.  Interpretation of Data II (960:587)
7.  Data Mining (960:588)

Two required courses in Computer Science:
8.  Design/Analysis of Data Structure and Algorithms (198:513)
9.  Machine Learning (198:536)

One optional course from the list given under the biostatistics option.

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