How to Build Highly Effective Data Science Workflows

Data science workflows typically look like this

Image of a complex data science workflow
To implement this workflow, many data scientists write code that chains together several functions and execute it linearly. While quick, it likely has many problems:  
  • it doesn't scale well as you add complexity
  • you have to manually track which functions were run with which parameters
  • you have to manually track where data is saved
  • it's difficult for others to read

Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. is a free open-source library which makes it easy for you to build highly effective data science workflows. See github example to learn more.

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Does you write data science code looks like this? Don't do it! There's a better way.

import pandas as pd
import sklearn.svm, sklearn.metrics

def get_data():
    data = download_data()
    data = clean_data(data)

def preprocess(data):
    data = apply_function(data)
    return data

# flow parameters
reload_source = True
do_preprocess = True

# run workflow
if reload_source:

df_train = pd.read_pickle('data.pkl')
if do_preprocess:
    df_train = preprocess(df_train)
model = sklearn.svm.SVC()[:,:-1], df_train['y'])


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