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Data Science Cheatsheet

A comprehensive cheat sheet covering essential concepts, tools, and techniques in Data Science. It provides a quick reference for machine learning algorithms, data manipulation, statistical methods, and more.

Fundamentals

Key Concepts

Supervised Learning

Learning from labeled data to predict outcomes.
Examples: Regression, Classification.

Unsupervised Learning

Discovering patterns in unlabeled data.
Examples: Clustering, Dimensionality Reduction.

Reinforcement Learning

Training an agent to make decisions in an environment to maximize a reward.

Bias-Variance Tradeoff

Balancing model complexity to minimize both bias (underfitting) and variance (overfitting).

Cross-Validation

Evaluating model performance on multiple subsets of the data to ensure generalization.

Feature Engineering

Creating new features or transforming existing ones to improve model accuracy.

Common Algorithms

Linear Regression: Predicts a continuous outcome using a linear equation.

Logistic Regression: Predicts a binary outcome using a logistic function.

Decision Trees: Partitions data into subsets based on feature values to make predictions.

Random Forest: An ensemble of decision trees that averages predictions to improve accuracy.

Support Vector Machines (SVM): Finds the optimal hyperplane to separate data into classes.

K-Nearest Neighbors (KNN): Classifies data based on the majority class among its k nearest neighbors.

K-Means Clustering: Partitions data into k clusters based on distance to cluster centroids.

Python for Data Science

Data Manipulation with Pandas

Creating a DataFrame

 
import pandas as pd
data = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data)

Selecting Columns

 
df['col1']
df[['col1', 'col2']]

Filtering Rows

 
df[df['col1'] > 1]

Grouping and Aggregation

 
df.groupby('col1').mean()

Handling Missing Data

 
df.dropna()
df.fillna(0)

Data Visualization with Matplotlib and Seaborn

Basic Plotting with Matplotlib

 
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.show()

Scatter Plot with Seaborn

 
import seaborn as sns
sns.scatterplot(x='col1', y='col2', data=df)
plt.show()

Histogram with Seaborn

 
sns.histplot(df['col1'])
plt.show()

Box Plot with Seaborn

 
sns.boxplot(x='col1', y='col2', data=df)
plt.show()

Scikit-learn for Machine Learning

Training a Model

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)

Making Predictions

y_pred = model.predict(X_test)

Model Evaluation

from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_test, y_pred)
print(mse)

Data Preprocessing

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

Train-Test Split

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Statistical Methods

Descriptive Statistics

Mean

Average value of a dataset.
Formula: $ \frac{\sum_{i=1}^{n} x_i}{n} $

Median

Middle value of a sorted dataset.

Mode

Most frequent value in a dataset.

Standard Deviation

Measure of the spread of data around the mean.
Formula: $ \sqrt{\frac{\sum_{i=1}^{n} (x_i - \mu)^2}{n}} $

Variance

Square of the standard deviation.
Formula: $ \frac{\sum_{i=1}^{n} (x_i - \mu)^2}{n} $

Inferential Statistics

Hypothesis Testing

A method for testing a claim or hypothesis about a population parameter.

P-value

Probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true.

Confidence Interval

Range of values likely to contain the true population parameter with a certain level of confidence.

T-test

Used to compare the means of two groups.

ANOVA

Used to compare the means of more than two groups.

Model Evaluation and Tuning

Evaluation Metrics

Accuracy

Fraction of correctly classified instances.
Formula: $ \frac{\text{Number of correct predictions}}{\text{Total number of predictions}} $

Precision

Fraction of true positives among predicted positives.
Formula: $ \frac{\text{True Positives}}{\text{True Positives + False Positives}} $

Recall

Fraction of true positives among actual positives.
Formula: $ \frac{\text{True Positives}}{\text{True Positives + False Negatives}} $

F1-Score

Harmonic mean of precision and recall.
Formula: $ 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision + Recall}} $

AUC-ROC

Area under the Receiver Operating Characteristic curve, measures the ability of a classifier to distinguish between classes.

Mean Squared Error (MSE)

Average squared difference between predicted and actual values.
Formula: $ \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 $

R-squared

Proportion of variance in the dependent variable that can be predicted from the independent variables.
Formula: $ 1 - \frac{\sum_{i=1}{n} (y_i - \hat{y}i)^2}{\sum{i=1}{n} (y_i - \bar{y})^2} $

Hyperparameter Tuning

Grid Search: Exhaustively search a specified subset of the hyperparameters of a learning algorithm.

Randomized Search: Sample a given number of candidates from a hyperparameter search space.

Bayesian Optimization: Uses Bayesian inference to find the hyperparameters that optimize a given metric.

Cross-Validation: Evaluate model performance on multiple subsets of the data to ensure generalization.
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