Statistical Modeling

Review of the fundamentals of the Python language applied to data analysis: data structures, loops, functions, and libraries essential for data science.

Data manipulation and analysis using the Pandas library: loading datasets, filtering, grouping, table joining, and data cleaning.

Creating charts and visualizations with Matplotlib and Seaborn. How to communicate patterns and insights visually and efficiently.

Essential concepts of descriptive statistics: mean, median, mode, standard deviation, variance, and how to interpret them to understand the distribution of data.

Main probability distributions (normal, binomial, Poisson) and their application in modeling real-world phenomena and in statistical inference.

Fundamentals of statistical testing: p-value, confidence interval, t-tests, chi-square and ANOVA. How to validate hypotheses based on data.

Construction and validation of simple and multiple linear regression models. Interpretation of coefficients, performance evaluation, and prediction of continuous values.

Identification and treatment of multicollinearity in regression models. Techniques to ensure the quality and reliability of statistical models.

Models for classification: logistic regression for binary variables and decision trees for classification and regression problems.

Fundamental concepts of machine learning: supervised and unsupervised learning, overfitting, cross-validation, and model evaluation.

Clustering algorithms (K-Means, hierarchical) and dimensionality reduction (PCA). Applications in customer segmentation and exploratory analysis.

Introduction to artificial neural networks: perceptron, backpropagation, and deep learning. First steps with TensorFlow and practical applications with real-world data.