In my previous blog post I discussed several key takeaways from the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), including time series analysis, survival analysis, and massive online analytics. In this post, I will take a deeper dive into survival analysis. I’ll go into detail on what survival analysis is, how it originated, the components involved, different methods that can be utilized, real-world examples, and open source libraries available.
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Two weeks ago I was in Halifax, Nova Scotia attending the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017). KDD is one of the largest and most respectable conferences in the data science community. It offers a wealth of knowledge on the latest research in data science, data mining, big data, and predictive analytics. Researchers and professionals come together to learn and discuss novel ideas and technologies to solve challenging problems. My areas of interest lied in time series analysis, IoT streaming, and big data. So here are a few of the interesting things I learned in those areas.