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121 - 130 of 134 results for: all courses

STATS 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 an more »
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: not given this year | Units: 3 | UG Reqs: WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

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 | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

STATS 101: Data Science 101

http://web.stanford.edu/class/stats101/ . This course will provide a hands-on introduction to statistics and data science. Students will engage with the fundamental ideas in inferential and computational thinking. Each week, we will explore a core topic comprising three lectures and two labs (a module), in which students will manipulate real-world data and learn about statistical and computational tools. Students will engage in statistical computing and visualization with current data analytic software (Jupyter, R). The objectives of this course are to have students (1) be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis, and (2) be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures. No programming or statistical background is assumed. Freshmen and sophomores interested in data science, computing and statistics are encouraged to attend. Open to graduates as well.
Terms: Aut, Spr | Units: 5 | UG Reqs: GER: DB-NatSci, WAY-AQR | Grading: Letter or Credit/No Credit

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 | Units: 4-5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

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 | Units: 3-5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

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: Win | Units: 3-5 | UG Reqs: GER:DB-Math, WAY-AQR | Grading: Letter or Credit/No Credit
Instructors: Zhu, X. (PI)

STATS 167: Probability: Ten Great Ideas About Chance (PHIL 166, PHIL 266, STATS 267)

Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of STATS 60 or 116.
Terms: not given this year | Units: 4 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

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: Win | Units: 3-4 | UG Reqs: GER:DB-Math, WAY-AQR | Grading: Letter or Credit/No Credit
Instructors: Walther, G. (PI)

THINK 1: The Science of MythBusters

How do scientists actually go about answering practical questions? How does science function as a way of understanding our world, and importantly how does it differ from other approaches? As its point of departure, this course will examine and critique selected episodes of the television series, MythBusters (Discovery Channel), which tests the validity of many popular beliefs in a variety of imaginative ways, including myths, rumors, traditions, and stories. We will take the opportunity to delve more deeply into the applicability of the scientific method in understanding a vast range of real-world problems, and into the practical acquisition of fact-based knowledge, which together form the cornerstone of all science. The intellectual framework of this course will be based, first and foremost, on skeptical inquiry, combined with the other key ingredients of good science, which include: framing the question well, careful experimental design, meticulous observation and measurement, quanti more »
How do scientists actually go about answering practical questions? How does science function as a way of understanding our world, and importantly how does it differ from other approaches? As its point of departure, this course will examine and critique selected episodes of the television series, MythBusters (Discovery Channel), which tests the validity of many popular beliefs in a variety of imaginative ways, including myths, rumors, traditions, and stories. We will take the opportunity to delve more deeply into the applicability of the scientific method in understanding a vast range of real-world problems, and into the practical acquisition of fact-based knowledge, which together form the cornerstone of all science. The intellectual framework of this course will be based, first and foremost, on skeptical inquiry, combined with the other key ingredients of good science, which include: framing the question well, careful experimental design, meticulous observation and measurement, quantitative analysis and modeling, the evaluation of statistical significance, recovery from failure, disseminating findings, and the continuous cycle of hypothesis and testing. Note: This course is taught at an introductory level, but it pays serious attention to the quantitative treatment of experimental data and associated tests of statistical significance. All students taking the course will be expected to learn, and to work a series of problems in, basic probability and statistics. There is also a hands-on, "dorm lab" component that involves some fabrication and a significant amount of individual testing and measurement. The final course project will involve developing and writing a scientific grant proposal to test a myth. We hope to inculcate in our students "a taste for questioning, a sense of observation, intellectual rigor, practice with reasoning, modesty in the face of facts, the ability to distinguish between true and false, and an attachment to logical and precise language. " (Yves Quéré, 2010 Science 330:605).
Terms: not given this year | Units: 4 | UG Reqs: THINK, WAY-AQR, WAY-SMA | Grading: Letter (ABCD/NP)

THINK 3: Breaking Codes, Finding Patterns

Why are humans drawn to making and breaking codes? To what extent is finding patterns both an art and a science? Cryptography has been used for millennia for secure communications, and its counterpart, cryptanalysis, or code breaking, has been around for just slightly less time. In this course we will explore the history of cryptography and cryptanalysis including the Enigma code, Navajo windtalkers, early computer science and the invention of modern Bayesian inference. We will try our own hand at breaking codes using some basic statistical tools for which no prior experience is necessary. Finally, we will consider the topic of patterns more generally, raising such questions as why we impute meaning to patterns, such as Biblical codes, and why we assume a complexity within a pattern when it's not there, such as the coincidence of birthdays in a group.
Terms: not given this year | Units: 4 | UG Reqs: THINK, WAY-AQR, WAY-FR | Grading: Letter (ABCD/NP)
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