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UW新开的data science 项目,有人申请了吗?

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本帖最后由 owl0426 于 2016-4-19 03:54 编辑

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: 5
Covers 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 Analytics
Credits: 5
Learn 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 Design
Credits: 5
Learn 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 Science
Credits: 5
Databases 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 Scientists
Credits: 5
Introduces 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 & Algorithms
Credits: 5
Learn 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 Science
Credits: 5
Software 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 Science
Credits: 5
Introduction 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 Capstone
Credits: 5
This 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.
. 1point3acres
Faculty名单,只有一个professor,两个associate,其他都是assistant以下的了
Faculty/InstructorsMarco Carone
Assistant Professor, Biostatistics
Megan Finn
Assistant Professor, Information School
Emily Fox
Amazon Professor of Machine Learning
Assistant Professor, Statistics
Adjunct Assistant Professor, Computer Science & Engineering and Electrical Engineering
Data Science Fellow, eScience Institute
Co-director, MODE Lab
Fang Han
Assistant Professor, Statistics
Jessica Hullman
Assistant Professor, Information School
Adjunct Assistant Professor, Computer Science & Engineering
Sham Kakade
Washington Research Foundation Data Science Chair
Associate Professor, Statistics and Computer Science & Engineering
Senior Data Science Fellow, eScience Institute
Arvind Krishnamurthy
Associate Professor, Computer Science & Engineering
Ed Lazowska
Bill & Melinda Gates Chair in Computer Science & Engineering
Founding Director and Senior Data Science Fellow, eScience Institute
Brian Leroux
Professor, Biostatistics and Oral Health Sciences
Chris Meek
Affiliate Professor, Statistics
Marina Meila
Senior Data Science Fellow, Statistics
Data Science Fellow, eScience Institute
Hal Perkins
Sr. Lecturer, Computer Science & Engineering
Ali Shojaie
Assistant Professor, Biostatistics
Adjunct Assistant Professor, Statistics
Noah Simon
Assistant Professor, Biostatistics
Emma Spiro
Assistant Professor, Information School
Adjunct Assistant Professor, Sociology

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!
. From 1point 3acres bbs

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catherineycycy 2016-5-4 07:17:48 | 显示全部楼层
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同关注,之前也看到了这个项目,好像之前是continuing education下面的?最近刚刚收到他们的邮件说申请ddl延到5.13了,应该是申的人比较少吧,看介绍情况有parttime和fulltime,但课都是晚上上,就是不知道选其他专业的课的情况行不行,而且刚开始的话应该career service和了解的学长学姐是几乎没有的,不知道背靠UW这个大树能有多少好处。。
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uuisafresh 2016-5-4 18:57:07 | 显示全部楼层
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好像是晚上上课?
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michaelsuper 2016-5-4 23:59:19 | 显示全部楼层
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這是我之前二月寄信去問enrollment advisor的結果,自己看下吧:-baidu 1point3acres

The program is in partnership with six top-ranked UW STEM related departments and schools. However, the program isn't an officially certified STEM program.

Unfortunately, this program only allows students to take courses within the program.

There isn't an option to take more than two courses per quarter but its possible this may change in the next couple of years as the program grows and is more established.

We do not require students to have full-time work experience. However, this is the first year the program is being offered and we expect those who have some work experience will be the most competitive going through the admissions process.

總之這項目目前非STEM (但full-time應該還是能申F1身份),一個quarter不能選超過兩門課,只能選項目中列的那些課程,沒有任何flexibility。
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yingy4 2016-5-5 00:04:10 | 显示全部楼层
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非STEM劣势太大了。。。
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lha_1313 2016-5-5 01:43:57 | 显示全部楼层
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我在微软的前同事申请了 lol
如果同学很多是这样的 内推的机会应该很多啊
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dianaz91 2016-5-25 07:13:35 | 显示全部楼层
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有人最后申这个program么? 今天结果出了....
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tonylovedoris 2016-5-25 23:08:32 | 显示全部楼层
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昨天收到录取了也打算去读
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tonylovedoris 2016-5-26 03:30:54 | 显示全部楼层
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dianaz91 发表于 2016-5-25 07:13
有人最后申这个program么? 今天结果出了....

新人到论坛权限不够回复私信,你的微信我怎么搜不到呢
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jfiowenoic 2016-5-28 11:44:54 | 显示全部楼层
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都是晚上上课,而且每学期的课很少,感觉不太好
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