feat: new articles

Author: DiogoRibeiro7

_posts/2020-11-05-probability_theory_basics.md

@@ -4,7 +4,8 @@ categories:
 - Statistics
 classes: wide
 date: '2020-11-05'
-excerpt: An introduction to probability theory concepts every data scientist should know.
+excerpt: An introduction to probability theory concepts every data scientist should
+  know.
 header:
   image: /assets/images/data_science_10.jpg
   og_image: /assets/images/data_science_10.jpg
@@ -17,15 +18,17 @@ keywords:
 - Random variables
 - Distributions
 - Data science
-seo_description: Learn the core principles of probability theory, from random variables to common distributions, with practical examples for data science.
-seo_title: 'Probability Theory Basics for Data Science'
+seo_description: Learn the core principles of probability theory, from random variables
+  to common distributions, with practical examples for data science.
+seo_title: Probability Theory Basics for Data Science
 seo_type: article
-summary: This post reviews essential probability concepts like random variables, expectation, and common distributions, illustrating how they underpin data science workflows.
+summary: This post reviews essential probability concepts like random variables, expectation,
+  and common distributions, illustrating how they underpin data science workflows.
 tags:
 - Probability
 - Statistics
 - Data science
-title: 'Probability Theory Basics for Data Science'
+title: Probability Theory Basics for Data Science
 ---
 
 Probability theory provides the mathematical foundation for modeling uncertainty. By understanding random variables and probability distributions, data scientists can quantify risks and make informed decisions.

_posts/2020-11-10-simple_linear_regression_intro.md

@@ -4,7 +4,8 @@ categories:
 - Data Science
 classes: wide
 date: '2020-11-10'
-excerpt: Understand how simple linear regression models the relationship between two variables using a single predictor.
+excerpt: Understand how simple linear regression models the relationship between two
+  variables using a single predictor.
 header:
   image: /assets/images/data_science_11.jpg
   og_image: /assets/images/data_science_11.jpg
@@ -16,15 +17,17 @@ keywords:
 - Linear regression
 - Least squares
 - Data analysis
-seo_description: Discover the mechanics of simple linear regression and how to interpret slope and intercept when fitting a straight line to data.
-seo_title: 'A Primer on Simple Linear Regression'
+seo_description: Discover the mechanics of simple linear regression and how to interpret
+  slope and intercept when fitting a straight line to data.
+seo_title: A Primer on Simple Linear Regression
 seo_type: article
-summary: This article introduces simple linear regression and the least squares method, showing how a single predictor explains variation in a response variable.
+summary: This article introduces simple linear regression and the least squares method,
+  showing how a single predictor explains variation in a response variable.
 tags:
 - Regression
 - Statistics
 - Data science
-title: 'A Primer on Simple Linear Regression'
+title: A Primer on Simple Linear Regression
 ---
 
 Simple linear regression is a foundational technique for modeling the relationship between a predictor variable and a response variable. By fitting a straight line, we can quantify how changes in one variable are associated with changes in another.

_posts/2020-11-20-bayesian_inference_basics.md

@@ -4,7 +4,8 @@ categories:
 - Statistics
 classes: wide
 date: '2020-11-20'
-excerpt: Explore the fundamentals of Bayesian inference and how prior beliefs combine with data to form posterior conclusions.
+excerpt: Explore the fundamentals of Bayesian inference and how prior beliefs combine
+  with data to form posterior conclusions.
 header:
   image: /assets/images/data_science_12.jpg
   og_image: /assets/images/data_science_12.jpg
@@ -17,15 +18,17 @@ keywords:
 - Priors
 - Posterior distributions
 - Data science
-seo_description: An overview of Bayesian inference, demonstrating how to update prior beliefs with new evidence to make data-driven decisions.
-seo_title: 'Bayesian Inference Explained'
+seo_description: An overview of Bayesian inference, demonstrating how to update prior
+  beliefs with new evidence to make data-driven decisions.
+seo_title: Bayesian Inference Explained
 seo_type: article
-summary: Learn how Bayesian inference updates prior beliefs into posterior distributions, providing a flexible framework for reasoning under uncertainty.
+summary: Learn how Bayesian inference updates prior beliefs into posterior distributions,
+  providing a flexible framework for reasoning under uncertainty.
 tags:
 - Bayesian
 - Inference
 - Statistics
-title: 'Bayesian Inference Explained'
+title: Bayesian Inference Explained
 ---
 
 Bayesian inference offers a powerful perspective on probability, treating unknown quantities as distributions that update when new evidence appears.

_posts/2020-11-25-hypothesis_testing_real_world_applications.md

@@ -4,7 +4,8 @@ categories:
 - Statistics
 classes: wide
 date: '2020-11-25'
-excerpt: See how hypothesis testing helps draw meaningful conclusions from data in practical scenarios.
+excerpt: See how hypothesis testing helps draw meaningful conclusions from data in
+  practical scenarios.
 header:
   image: /assets/images/data_science_13.jpg
   og_image: /assets/images/data_science_13.jpg
@@ -17,15 +18,18 @@ keywords:
 - P-values
 - Significance
 - Data science
-seo_description: Learn how to apply hypothesis tests in real-world analyses and avoid common pitfalls when interpreting p-values and confidence levels.
-seo_title: 'Applying Hypothesis Testing in the Real World'
+seo_description: Learn how to apply hypothesis tests in real-world analyses and avoid
+  common pitfalls when interpreting p-values and confidence levels.
+seo_title: Applying Hypothesis Testing in the Real World
 seo_type: article
-summary: This post walks through frequentist hypothesis testing, showing how to formulate null and alternative hypotheses and interpret the results in practical data science tasks.
+summary: This post walks through frequentist hypothesis testing, showing how to formulate
+  null and alternative hypotheses and interpret the results in practical data science
+  tasks.
 tags:
 - Hypothesis testing
 - Statistics
 - Experiments
-title: 'Applying Hypothesis Testing in the Real World'
+title: Applying Hypothesis Testing in the Real World
 ---
 
 Hypothesis testing allows data scientists to objectively assess whether an observed pattern is likely due to chance or reflects a genuine effect.

_posts/2020-11-30-data_visualization_best_practices.md

@@ -4,7 +4,8 @@ categories:
 - Data Science
 classes: wide
 date: '2020-11-30'
-excerpt: Discover best practices for creating clear and compelling data visualizations that communicate insights effectively.
+excerpt: Discover best practices for creating clear and compelling data visualizations
+  that communicate insights effectively.
 header:
   image: /assets/images/data_science_14.jpg
   og_image: /assets/images/data_science_14.jpg
@@ -17,15 +18,17 @@ keywords:
 - Charts
 - Communication
 - Best practices
-seo_description: Guidelines for selecting chart types, choosing colors, and avoiding clutter when visualizing data for stakeholders.
-seo_title: 'Data Visualization Best Practices'
+seo_description: Guidelines for selecting chart types, choosing colors, and avoiding
+  clutter when visualizing data for stakeholders.
+seo_title: Data Visualization Best Practices
 seo_type: article
-summary: Learn how to design effective visualizations by focusing on clarity, appropriate chart selection, and thoughtful use of color and labels.
+summary: Learn how to design effective visualizations by focusing on clarity, appropriate
+  chart selection, and thoughtful use of color and labels.
 tags:
 - Visualization
 - Data science
 - Communication
-title: 'Data Visualization Best Practices'
+title: Data Visualization Best Practices
 ---
 
 Effective data visualization bridges the gap between complex datasets and human understanding. Following proven design principles ensures that your charts highlight the important messages without distractions.

_posts/2021-10-05-data_preprocessing_pipelines.md

@@ -4,7 +4,8 @@ categories:
 - Data Science
 classes: wide
 date: '2021-10-05'
-excerpt: Learn how to design robust data preprocessing pipelines that prepare raw data for modeling.
+excerpt: Learn how to design robust data preprocessing pipelines that prepare raw
+  data for modeling.
 header:
   image: /assets/images/data_science_6.jpg
   og_image: /assets/images/data_science_6.jpg
@@ -17,10 +18,12 @@ keywords:
 - Pipelines
 - Data cleaning
 - Feature engineering
-seo_description: Discover best practices for building reusable data preprocessing pipelines that handle missing values, encoding, and feature scaling.
+seo_description: Discover best practices for building reusable data preprocessing
+  pipelines that handle missing values, encoding, and feature scaling.
 seo_title: Building Data Preprocessing Pipelines for Reliable Models
 seo_type: article
-summary: This post outlines the key steps in constructing data preprocessing pipelines using tools like scikit-learn to ensure consistent model inputs.
+summary: This post outlines the key steps in constructing data preprocessing pipelines
+  using tools like scikit-learn to ensure consistent model inputs.
 tags:
 - Data preprocessing
 - Machine learning

_posts/2021-10-15-decision_tree_algorithms.md

@@ -4,7 +4,8 @@ categories:
 - Machine Learning
 classes: wide
 date: '2021-10-15'
-excerpt: Understand how decision tree algorithms split data and how pruning improves generalization.
+excerpt: Understand how decision tree algorithms split data and how pruning improves
+  generalization.
 header:
   image: /assets/images/data_science_7.jpg
   og_image: /assets/images/data_science_7.jpg
@@ -17,10 +18,12 @@ keywords:
 - Classification
 - Tree pruning
 - Machine learning
-seo_description: Learn the mechanics of decision tree algorithms, including entropy-based splits and pruning techniques that prevent overfitting.
+seo_description: Learn the mechanics of decision tree algorithms, including entropy-based
+  splits and pruning techniques that prevent overfitting.
 seo_title: How Decision Trees Work and Why Pruning Matters
 seo_type: article
-summary: This article walks through the basics of decision tree construction and explains common pruning methods to create better models.
+summary: This article walks through the basics of decision tree construction and explains
+  common pruning methods to create better models.
 tags:
 - Decision trees
 - Classification

_posts/2021-11-10-model_evaluation_metrics.md

@@ -18,14 +18,16 @@ keywords:
 - Precision
 - Recall
 - Regression metrics
-seo_description: A concise overview of essential metrics like precision, recall, F1-score, and RMSE for measuring model performance.
+seo_description: A concise overview of essential metrics like precision, recall, F1-score,
+  and RMSE for measuring model performance.
 seo_title: Essential Metrics for Evaluating Machine Learning Models
 seo_type: article
-summary: Learn how to interpret common classification and regression metrics to choose the best model for your data.
+summary: Learn how to interpret common classification and regression metrics to choose
+  the best model for your data.
 tags:
 - Accuracy
 - F1-score
-- RMSE
+- Rmse
 title: A Guide to Model Evaluation Metrics
 ---
 

_posts/2022-10-15-time_series_decomposition.md

@@ -5,7 +5,8 @@ categories:
 - Time Series
 classes: wide
 date: '2022-10-15'
-excerpt: Learn how time series decomposition reveals trend, seasonality, and residual components for clearer forecasting insights.
+excerpt: Learn how time series decomposition reveals trend, seasonality, and residual
+  components for clearer forecasting insights.
 header:
   image: /assets/images/data_science_12.jpg
   og_image: /assets/images/data_science_12.jpg
@@ -19,10 +20,12 @@ keywords:
 - Seasonality
 - Forecasting
 - Decomposition
-seo_description: Discover how to separate trend and seasonal patterns from a time series using additive or multiplicative decomposition.
-seo_title: 'Time Series Decomposition Made Simple'
+seo_description: Discover how to separate trend and seasonal patterns from a time
+  series using additive or multiplicative decomposition.
+seo_title: Time Series Decomposition Made Simple
 seo_type: article
-summary: This article explains how decomposing a time series helps isolate long-term trends and recurring seasonal effects so you can model data more effectively.
+summary: This article explains how decomposing a time series helps isolate long-term
+  trends and recurring seasonal effects so you can model data more effectively.
 tags:
 - Time series
 - Forecasting

_posts/2025-06-06-exploratory_data_analysis_intro.md

@@ -4,7 +4,8 @@ categories:
 - Data Science
 classes: wide
 date: '2025-06-06'
-excerpt: Discover the essential steps of Exploratory Data Analysis (EDA) and how to gain insights from your data before building models.
+excerpt: Discover the essential steps of Exploratory Data Analysis (EDA) and how to
+  gain insights from your data before building models.
 header:
   image: /assets/images/data_science_5.jpg
   og_image: /assets/images/data_science_5.jpg
@@ -18,16 +19,19 @@ keywords:
 - Python
 - Pandas
 - Data cleaning
-seo_description: Learn the fundamentals of Exploratory Data Analysis using Python, including data cleaning, visualization, and summary statistics.
-seo_title: "Beginner's Guide to Exploratory Data Analysis (EDA)"
+seo_description: Learn the fundamentals of Exploratory Data Analysis using Python,
+  including data cleaning, visualization, and summary statistics.
+seo_title: Beginner's Guide to Exploratory Data Analysis (EDA)
 seo_type: article
-summary: This guide covers the core principles of Exploratory Data Analysis, demonstrating how to inspect, clean, and visualize datasets to uncover patterns and inform subsequent modeling steps.
+summary: This guide covers the core principles of Exploratory Data Analysis, demonstrating
+  how to inspect, clean, and visualize datasets to uncover patterns and inform subsequent
+  modeling steps.
 tags:
-- EDA
+- Eda
 - Data science
 - Python
 - Visualization
-title: "Exploratory Data Analysis: A Beginner's Guide"
+title: 'Exploratory Data Analysis: A Beginner''s Guide'
 ---
 
 Exploratory Data Analysis (EDA) is the process of examining a dataset to understand its main characteristics before applying more formal statistical modeling or machine learning. By exploring your data upfront, you can identify patterns, spot anomalies, and test assumptions that might otherwise go unnoticed.