Speed Up Your Data Processing Parallel and Asynchronous ...
Speed Up Your Data Processing
Parallel and Asynchronous Programming in Data Science
By: Chin Hwee Ong (@ongchinhwee)
23 July 2020
About me
Ong Chin Hwee Data Engineer @ ST Engineering Background in aerospace
engineering + computational modelling Contributor to pandas 1.0 release Mentor team at BigDataX
@ongchinhwee
A typical data science workflow
1. Extract raw data 2. Process data 3. Train model 4. Evaluate and deploy model
@ongchinhwee
Bottlenecks in a data science project
Lack of data / Poor quality data Data processing
The 80/20 data science dilemma In reality, it's closer to 90/10
@ongchinhwee
Data Processing in Python
For loops in Python Run on the interpreter, not compiled Slow compared with C
a_list = [] for i in range(100):
a_list.append(i*i)
@ongchinhwee
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