关键信息
关于内容
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
课程大纲
- Week 1 - Week 1
This week covers the basics of analytic graphics and the base plotting system in R. We've also included some background material to help you install R if you haven't done so already. - Week 2 - Week 2
Welcome to Week 2 of Exploratory Data Analysis. This week covers some of the more advanced graphing systems available in R: the Lattice system and the ggplot2 system. While the base graphics system provides many important tools for visualizing data, it was par... - Week 3 - Week 3
Welcome to Week 3 of Exploratory Data Analysis. This week covers some of the workhorse statistical methods for exploratory analysis. These methods include clustering and dimension reduction techniques that allow you to make graphical displays of very high dime... - Week 4 - Week 4
This week, we'll look at two case studies in exploratory data analysis. The first involves the use of cluster analysis techniques, and the second is a more involved analysis of some air pollution data. How one goes about doing EDA is often personal, but I'm pr...
教师
Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health
内容设计师
平台
Coursera是一家数字公司，提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。
Coursera与顶尖大学和组织合作，在线提供一些课程，并提供许多科目的课程，包括：物理，工程，人文，医学，生物学，社会科学，数学，商业，计算机科学，数字营销，数据科学 和其他科目。
Highly recommended course for budding data scientist. I loved the John Hopkins univ pedagogy and peer review system. The content is great.
This is a very good course, at times it felt like the instruction was to do things mechanically without understanding the motivation. Perhaps this should come after or in conjunction with Statistical Inference
Highly recommended course for budding data scientist. I loved the John Hopkins univ pedagogy and peer review system. The content is great.
This is the worst of the Data Science courses so far (they've all been pretty good up to this point).It's called Exploratory Data Analysis, but is actually all about the graphics systems in R. And it does a botched job on those as well.All quizzes and assignments are about the graphics systems. The only portion of the course that deviates from that is Week 3 (for which there is no quiz or project) where we "learn" about clustering and dimension reduction. However, that material is presented really poorly: not enough depth for someone who is already familiar with the subject matter; and not nearly well enough explained for newbies.On the graphics side, none of the systems is explored in great depth. The lattice system is essentially just mentioned in passing. To cap it all off, the brief for the last assignment is really ambiguous, which often causes perfectly valid work to be graded poorly by peers. (Just look at the forums, if you need proof.)
This was a great course. I learned how to use several graphic systems within R, and to imagine how to make clear answers to questions using plots.
It's a very good course. Week 3 was a little bit more challenging than expected, as well as assignment 2, but you get a good idea of how to use all the different plotting systems
