A data analysis can fail if it doesn't present a coherent story and "close all the doors". Such a failure is not simply a problem with communication, but often indicates a problem with the details of the analysis itself.

We are three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry) who are fired up about the new era where data are abundant and statisticians are scientists.The views represented here are our own and do not represent the views of Johns Hopkins University, Harvard University or Dana Farber Cancer Institute.

Dec 16, 2016 · I’m teaching a class in data science for our masters and PhD students here at Hopkins. I’ve been teaching a variation on this class since 2011 and over time I’ve introduced a number of new components to the class: high-dimensional data methods (2011), data manipulation and cleaning (2012), real, possibly not doable data analyses (2012,2013), peer reviews (2014), building swirl tutorials ...

Jan 31, 2016 · Update: I realize this may seem like I’m picking on people. I really don’t mean to, I have for sure made all of these mistakes and many more.

The Johns Hopkins Data Science Lab has been teaching massive online open courses for more than 5 years now. During that time we’ve reached more than 5 million learners who want to break into the number one rated job in America.. While we have been incredibly excited about the results of these training programs, we’ve also learned over …

Jan 3, 2023 · I read this really interesting paper over the break, where they had multiple analyst teams analyze the same data set and fit a model to answer the same question.. This is a topic we’ve thought about a lot in the past; mostly from a therotical perspective. We have discussed the researcher degrees of freedom, recipe tradeoff and how p-values are just the tip of the iceberg for analyst variability.

Dec 10, 2018 · In data analysis, we make use of a lot of theory, whether we like to admit it or not. In a traditional statistical training, things like the central limit theorem and the law of large numbers (and their many variations) are deeply baked into our heads.

Feb 28, 2012 · Here are a few ideas that might make for interesting student projects at all levels (from high-school to graduate school). I’d welcome ideas/suggestions/additions to the list as well.

Oct 28, 2011 · It seems like everywhere we look, data is being generated - from politics, to biology, to publishing, to social networks. There are also diverse new computational tools, like GPGPU and cloud computing, that expand the statistical toolbox.

Nov 23, 2020 · In 2019 I wrote a post about The Tentpoles of Data Science that tried to distill the key skills of the data scientist. In the post I wrote: When I ask myself the question “What is data science?” I tend to think of the following five components.