printer friendly pageSTATS 48N: Riding the Data Wave
Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
Terms: Aut
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Units: 3
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Grading: Letter or Credit/No Credit
Instructors:
Sabatti, C. (PI)
STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
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Units: 5
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UG Reqs: GER:DBMath
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Grading: Letter or Credit/No Credit
Instructors:
Walther, G. (PI)
;
Basu, K. (TA)
;
Fukuyama, J. (TA)
;
Sun, D. (TA)
...
more instructors for STATS 60 »
Instructors:
Walther, G. (PI)
;
Basu, K. (TA)
;
Fukuyama, J. (TA)
;
Sun, D. (TA)
STATS 110: Statistical Methods in Engineering and the Physical Sciences
Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus.
Terms: Aut, Sum
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Units: 4-5
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UG Reqs: GER:DBMath
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Grading: Letter or Credit/No Credit
Instructors:
Reeves, G. (PI)
STATS 116: Theory of Probability
Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites:
MATH 52 and familiarity with infinite series, or equivalent.
Terms: Aut, Spr, Sum
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Units: 3-5
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UG Reqs: GER:DBMath
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Grading: Letter or Credit/No Credit
Instructors:
Bacallado de Lara, S. (PI)
;
Wang, C. (TA)
STATS 141: Biostatistics (BIO 141)
Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Aut
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Units: 3-5
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UG Reqs: GER:DBMath
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Grading: Letter or Credit/No Credit
Instructors:
Li, L. (PI)
;
Su, W. (TA)
STATS 160: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
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Units: 5
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Grading: Letter or Credit/No Credit
Instructors:
Walther, G. (PI)
STATS 191: Introduction to Applied Statistics
Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Recommended: 60, 110, or 141.
Terms: Aut
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Units: 3-4
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UG Reqs: GER:DBMath
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Grading: Letter or Credit/No Credit
Instructors:
Taylor, J. (PI)
STATS 198: Practical Training
For students majoring in Mathematical and Computational Science only. Students obtain employment in a relevant industrial or research activity to enhance their professional experience.
Terms: Aut, Win, Spr, Sum
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Units: 1-3
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Repeatable for credit
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Grading: Letter or Credit/No Credit
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum
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Units: 1-15
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Repeatable for credit
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Grading: Satisfactory/No Credit
Instructors:
Candes, E. (PI)
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Cover, T. (PI)
;
Dembo, A. (PI)
;
Diaconis, P. (PI)
...
more instructors for STATS 199 »
Instructors:
Candes, E. (PI)
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Cover, T. (PI)
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Dembo, A. (PI)
;
Diaconis, P. (PI)
;
Donoho, D. (PI)
;
Efron, B. (PI)
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Friedman, J. (PI)
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Hastie, T. (PI)
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Holmes, S. (PI)
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Johnstone, I. (PI)
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Lai, T. (PI)
;
Montanari, A. (PI)
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Olkin, I. (PI)
;
Olshen, R. (PI)
;
Owen, A. (PI)
;
Rajaratnam, B. (PI)
;
Rogosa, D. (PI)
;
Romano, J. (PI)
;
Siegmund, D. (PI)
;
Switzer, P. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Walther, G. (PI)
;
Wong, W. (PI)
;
Zhang, N. (PI)
STATS 202: Data Mining and Analysis
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization.
Terms: Aut, Sum
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Units: 3
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Grading: Letter or Credit/No Credit
Instructors:
Taylor, J. (PI)
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