Programming Skills are no Longer an Essential Requirement for Data Science Beginners

Focus on using existing libraries and packages. Have some background on the math behind each package or library

Photo by Christopher Robin Ebbinghaus on Unsplash

1. Basic Programming Skills

2. Standard Libraries and Packages

from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScaler

sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegressionfrom sklearn.linear_model import LogisticRegressionfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.svm import SVCfrom sklearn.pipeline import Pipelinepipe_lr = Pipeline([('scl', StandardScaler()),('lr',

3. Learning from the Python Documentation

import pandas as pd?pd.read_csv
from sklearn.linear_model import LogisticRegression?LogisticRegression
LogisticRegression(penalty ='l2',dual=False, tol=0.0001, C=1.0,    
fit_intercept=True, intercept_scaling=1,
class_weight=None, random_state=None,
solver='liblinear', max_iter=100,
multi_class='ovr', verbose=0,
warm_start=False, n_jobs=1)

Real Case Studies with Code Included

Summary and Conclusion

Additional data science/machine learning resources

Physicist, Data Science Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Predictive Analytics, Materials Sciences, Biophysics

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