| 01_Welcome.mp4 | 7.4 MB | ||
| 02_Benchmarking.mp4 | 12.3 MB | ||
| 03_How goood is your machine.mp4 | 7.6 MB | ||
| 04_Memory allocation.mp4 | 8.4 MB | ||
| 05_Importance of vectorizing your code.mp4 | 5.9 MB | ||
| 06_Data frames and matrices.mp4 | 7.1 MB | ||
| 07_What is code profiling.mp4 | 11.4 MB | ||
| 08_Profvis larger example.mp4 | 7.6 MB | ||
| 09_Monopoly overview.mp4 | 4.3 MB | ||
| 10_CPUs why do we have more than one.mp4 | 3.3 MB | ||
| 11_What sort of programmings benefit from parallel computing.mp4 | 7 MB | ||
| 12_The parallel package parApply.mp4 | 7.4 MB | ||
| 13_The parallel package parSapply.mp4 | 11.8 MB | ||
| 14_You can write efficient R code.mp4 | 1.1 MB |
The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.
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