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

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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

from
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',
LinearRegression())])

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

Written by

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

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