Modern scientific computing and big data analytics in Python
summary
Shows how to apply Python 3 tools to scientific and engineering problems, especially big-data use cases, using real-world datasets. Covers scikit-learn (machine learning), scikit-image (image processing), Pandas (data analysis) and IPython-parallel for parallel computation.
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chapters (22)
- 0:00Start
- 5:24PART I: BIG DATA
- 9:57SCALING
- 11:53MAPREDUCE "MapReduce: Simplified Data Processing on Large Clusters". Jeffrey Dean and Sanjay Ghemawat. Usenix 2004
- 13:50BEYOND MAPREDUCE? + PySpark bindings
- 14:41PART 2: TOOLS
- 15:01DENOISING removal of noise from the data
- 15:34INTERPOLATION recovery of missing values
- 16:09SIGNAL PROCESSING eg image super-resolution. See
- 17:25PREDICTION extrapolating data
- 17:49CLUSTERING unsupervised learning
- 18:07CLUSTERING + PREDICTION sklearn example
- 19:04SUPERVISED LEARNING PHASE Landsat satellite image data
- 19:47COMPRESSION reducing storage and bandwidth requirements
- 25:56"BIG CPU" PROBLEMS traditional "hard" problems in scientific computing
- 26:11320X CHEAPER The cost of one EC2 CPU core vs your time
- 29:02WHAT GIL? BLAS libraries can use all cores
- 33:45PYTHON:TRAFFIC ANALYSIS 57 github.io/blog/2013/06/19/real-time-traffic.data
- 41:13PART 3: EXAMPLES
- 41:52LANDSAT SATELLITE IMAGERY North Carolina
- 42:48CHOOSE A RANDOM PORT
- 47:26LOG IN WITH HTTPS:// with your randomly generated port
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