4 Python Libraries for Basic Data Science

For levels 1 and 2 data science, mastery of pandas, numpy, matplotlib, and scikit-learn is essential

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Image by Benjamin O. Tayo

Introduction

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Image by Benjamin O. Tayo

1. Basic Level

1.1 Data Basics

1.2. Data Visualization

1.3 Supervised Learning (Predicting Continuous Target Variables)

2. Intermediate Level

2.1 Supervised Learning (Predicting Discrete Target Variables)

2.2 Model Evaluation and Hyperparameter Tuning

2.3 Combining Different Models for Ensemble Learning

How to Learn the Essential Python Libraries

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)

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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