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Corrected a typo #84

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Corrected a typo #84

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2 changes: 1 addition & 1 deletion intro.Rmd
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Expand Up @@ -287,7 +287,7 @@ Sampling
: A first approach to try is reduce the amount of data being handled, through sampling. However, data must be sampled properly by applying sound statistical principles. For instance, selecting the top results is not sufficient in sorted datasets; with simple random sampling, there might be underrepresented groups, which we could overcome with stratified sampling, which in turn adds complexity to properly select categories. It is out of the scope of this book to teach how to properly perform statistical sampling, but many resources are available on this topic.

Profiling
: One can try to understand why a computation is slow and make the necessary improvements. A profiler, is a tool capable of inspecting code execution to help identify bottlenecks. In R, the R profiler, the `profvis` R package [@intro-profvis] and RStudio profiler feature [@intro-rstudio-profiler], allow you to easily to retrieve and visualize a profile; however, it’s not always trivial to optimize.
: One can try to understand why a computation is slow and make the necessary improvements. A profiler, is a tool capable of inspecting code execution to help identify bottlenecks. In R, the R profiler, the `profvis` R package [@intro-profvis] and RStudio profiler feature [@intro-rstudio-profiler], allow you to easily retrieve and visualize a profile; however, it’s not always trivial to optimize.

Scaling Up
: Speeding up computation is usually possible by buying faster or more capable hardware, say, increasing your machine memory, hard drive or procuring a machine with many more CPUs, this approach is known as "scaling up". However, there are usually hard limits as to how much a single computer can scale up and even with significant CPUs, one needs to find frameworks that parallelize computation efficiently.
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