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  • #数据科学

UW新开的data science 项目,有人申请了吗?

owl0426
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UW今年新开的项目,八门课外加一个capstone,课程都是新设计的,授课老师分别来自statistics,biostatistics,CS和information school,课程介绍似乎一半看起来都比较基础,只有三门课看上去有些意思,具体信息如下:

Introduction to Statistics & Probability
Credits: 5Covers the fundamentals of probability and mathematical statistics; axioms of probability, conditional and joint probability, random variables, univariate and multivariate distributions and densities, and moments; binomial, negative binomial, geometric, Poisson, normal, exponential distributions, and central limit theorem; and basic estimation and hypothesis testing theory.
Data Visualization & Exploratory AnalyticsCredits: 5Learn how to create visual narratives from data science results and how to analyze the factors contributing to effective data visualization. This course covers the design and presentation of digital information using modern visualization software; the role of vision and perception; methods of presenting complex information to enhance comprehension and analysis; and the incorporation of visualization techniques into human-computer interfaces.
Applied Statistics & Experimental DesignCredits: 5Learn to design and carry out statistical experiments. Covers data analyses using comparisons between batches, analysis of variance and linear and logistic regression. Evaluation of assumptions; data transformation; reliability of statistical measures; resampling methods; validation of assumptions; interpretation; causation versus correlation.
Data Management for Data ScienceCredits: 5Databases have been at the heart of commercial applications for decades, but today both commercial and scientific efforts depend on the management and manipulation of massive datasets, requiring the adaptation of database technology to new contexts. Learn the core concepts powering databases, and explore how these concepts are being used more broadly outside of traditional systems. Learn how to extract information using SQL and how to inspect query plans and use indexes to improve performance at both large and small scales. Learn how to work with unstructured and semi-structured data and how to apply emerging techniques in data cleaning, knowledge extraction and integration.
Statistical Machine Learning for Data ScientistsCredits: 5Introduces the theory and application of statistical machine learning. Topics include supervised versus unsupervised learning; cross-validation; the bias-variance trade-off; classification; k-means and hierarchical clustering; regularization and shrinkage approaches; non-linear approaches; local regression, spline models and generalized additive models; tree-based methods; and support vector machines.
Scalable Data Systems & AlgorithmsCredits: 5Learn the specialized systems and algorithms that have been developed to work with data at scale, including MapReduce and its contemporaries; core techniques in distributed systems; characteristics of HPC and cloud platforms; and important scalable algorithms for graphs, streams and text.
Software Design for Data ScienceCredits: 5Software is the currency of data science. Learn how to design and engineer effective sharable and reusable research projects that incorporate advanced computation and advanced data analysis, including best practices for version control, testing and automatic build management in addition to principles of style and structure.
Human-Centered Data ScienceCredits: 5Introduction to the human aspects of data science: data ethics and data privacy, legal frameworks and intellectual property, provenance and reproducibility, data curation and preservation, user experience design and usability testing, data communication and societal impacts of data science.
Data Science CapstoneCredits: 5This quarter-long capstone focuses on addressing a real-world problem sourced from local partners in science, government and industry. Students will identify a data science problem in a real-world setting and develop the means to address it. Capstone projects can be research-oriented or design-oriented. Solutions are typically interactive, meaning the end product is something that can be implemented and used.

Faculty名单,只有一个professor,两个associate,其他都是assistant以下的了

Faculty/InstructorsMarco CaroneAssistant Professor, BiostatisticsProfile | mcarone@uw.eduMegan FinnAssistant Professor, Information SchoolProfile | megfinn@uw.eduEmily FoxAmazon Professor of Machine LearningAssistant Professor, StatisticsAdjunct Assistant Professor, Computer Science & Engineering and Electrical EngineeringData Science Fellow, eScience InstituteCo-director, MODE LabProfile | ebfox@stat.washington.eduFang HanAssistant Professor, StatisticsProfile | fhan@jhu.eduJessica HullmanAssistant Professor, Information SchoolAdjunct Assistant Professor, Computer Science & EngineeringProfile | jhullman@uw.eduSham KakadeWashington Research Foundation Data Science ChairAssociate Professor, Statistics and Computer Science & EngineeringSenior Data Science Fellow, eScience InstituteProfile | sham@cs.washington.eduArvind KrishnamurthyAssociate Professor, Computer Science & EngineeringProfile | arvind@cs.washington.eduEd LazowskaBill & Melinda Gates Chair in Computer Science & EngineeringFounding Director and Senior Data Science Fellow, eScience InstituteProfile | sabez@uw.eduBrian LerouxProfessor, Biostatistics and Oral Health SciencesProfile | leroux@u.washington.eduChris MeekAffiliate Professor, StatisticsProfile | meek@microsoft.comMarina MeilaSenior Data Science Fellow, StatisticsData Science Fellow, eScience InstituteProfile | mmp@stat.washington.eduHal PerkinsSr. Lecturer, Computer Science & EngineeringProfile | perkins@cs.washington.eduAli ShojaieAssistant Professor, BiostatisticsAdjunct Assistant Professor, StatisticsProfile | ashojaie@uw.eduNoah SimonAssistant Professor, BiostatisticsProfile | nrsimon@uw.eduEmma SpiroAssistant Professor, Information SchoolAdjunct Assistant Professor, SociologyAffiliate, Center for Statistics & Social Science[backcolor=transparent]Profile[/backcolor] | [backcolor=transparent]espiro@uw.edu[/backcolor]

个人觉得这个项目的缺点蛮多的,第一年,全都是晚上上课,老师来自五湖四海(……),不知会不会变成没人管的优点就是这是UW,西雅图地区老大,和amazon什么的近水楼台,不知有没有优势,而且UW的统计和CS都蛮强的,不知如果这个毕业想进统计或者CS读博士会不会有更多机会

今年还没截止,如果手上已经有别的offer,这个有补申的价值吗?有没有UW的在读学长学姐的来透露一些内部消息?

谢谢!
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