fix: fix frontmmater

Author: DiogoRibeiro7

_posts/-_ideas/2030-01-01-climate_change.md

@@ -5,7 +5,8 @@ categories:
 - Data Science
 classes: wide
 date: '2030-01-01'
-excerpt: Explore how data science, machine learning, and big data are critical tools in addressing climate change and promoting sustainability.
+excerpt: Explore how data science, machine learning, and big data are critical tools
+  in addressing climate change and promoting sustainability.
 header:
   image: /assets/images/data_science_9.jpg
   og_image: /assets/images/data_science_9.jpg
@@ -14,20 +15,22 @@ header:
   teaser: /assets/images/data_science_9.jpg
   twitter_image: /assets/images/data_science_9.jpg
 keywords:
-- Climate Change
-- Data Science
-- Machine Learning
+- Climate change
+- Data science
+- Machine learning
 - Sustainability
-- Big Data
-seo_description: An in-depth look at how data science, big data, and machine learning can help solve climate change and sustainability challenges.
+- Big data
+seo_description: An in-depth look at how data science, big data, and machine learning
+  can help solve climate change and sustainability challenges.
 seo_title: 'Climate Change and Data Science: Solving Global Problems'
 seo_type: article
-summary: This article provides a comprehensive list of potential topics at the intersection of climate change, sustainability, and data science.
+summary: This article provides a comprehensive list of potential topics at the intersection
+  of climate change, sustainability, and data science.
 tags:
-- Climate Change
+- Climate change
 - Sustainability
-- Machine Learning
-- Big Data
+- Machine learning
+- Big data
 title: Exploring Climate Change, Sustainability, and Data Science
 ---
 

_posts/-_ideas/2030-01-01-ideas_statistical_tests.md

@@ -6,7 +6,8 @@ categories:
 - Hypothesis Testing
 classes: wide
 date: '2030-01-01'
-excerpt: A list of 15 article ideas covering statistical tests, ranging from ANOVA and Kruskal-Wallis to non-parametric tests and power analysis.
+excerpt: A list of 15 article ideas covering statistical tests, ranging from ANOVA
+  and Kruskal-Wallis to non-parametric tests and power analysis.
 header:
   image: /assets/images/data_science_3.jpg
   og_image: /assets/images/data_science_2.jpg
@@ -15,20 +16,22 @@ header:
   teaser: /assets/images/data_science_3.jpg
   twitter_image: /assets/images/data_science_2.jpg
 keywords:
-- Statistical Tests
-- ANOVA
-- Kruskal-Wallis
-- Data Analysis
-- Hypothesis Testing
-seo_description: Explore 15 ideas for writing articles on various statistical tests, including their differences, assumptions, and applications in data analysis.
+- Statistical tests
+- Anova
+- Kruskal-wallis
+- Data analysis
+- Hypothesis testing
+seo_description: Explore 15 ideas for writing articles on various statistical tests,
+  including their differences, assumptions, and applications in data analysis.
 seo_title: '15 Article Ideas: Writing about Statistical Tests'
 seo_type: article
-summary: This article provides 15 ideas for articles on statistical tests, including their use cases, assumptions, and applications in real-world data analysis.
+summary: This article provides 15 ideas for articles on statistical tests, including
+  their use cases, assumptions, and applications in real-world data analysis.
 tags:
-- ANOVA
-- Hypothesis Testing
-- Statistical Tests
-- Data Science
+- Anova
+- Hypothesis testing
+- Statistical tests
+- Data science
 title: 15 Article Ideas on Statistical Tests
 ---
 

_posts/-_ideas/NLP and Data Science Article Topic Ideas.md

@@ -4,20 +4,23 @@ categories:
 - NLP
 - Data Science
 classes: wide
-excerpt: Explore in-depth article topics combining NLP and Data Science, from text preprocessing to deep learning models, sentiment analysis, and chatbots.
+excerpt: Explore in-depth article topics combining NLP and Data Science, from text
+  preprocessing to deep learning models, sentiment analysis, and chatbots.
 keywords:
-- NLP
-- Data Science
-- Machine Learning
-- Topic Modeling
-- Sentiment Analysis
-seo_description: Explore in-depth article topics combining Natural Language Processing and Data Science, covering a range of tasks, models, and techniques.
+- Nlp
+- Data science
+- Machine learning
+- Topic modeling
+- Sentiment analysis
+seo_description: Explore in-depth article topics combining Natural Language Processing
+  and Data Science, covering a range of tasks, models, and techniques.
 seo_title: 'NLP and Data Science: Article Topics'
-summary: This article provides a list of topic ideas for writing detailed articles about NLP and Data Science, suitable for technical and practical discussions.
+summary: This article provides a list of topic ideas for writing detailed articles
+  about NLP and Data Science, suitable for technical and practical discussions.
 tags:
-- NLP
-- Data Science
-- Machine Learning
+- Nlp
+- Data science
+- Machine learning
 title: 'NLP and Data Science: Article Topic Ideas'
 ---
 

_posts/-_ideas/numerical_methods_fortran.md

@@ -1,7 +1,7 @@
 ---
 tags:
-- plaintext
-- fortran
+- Plaintext
+- Fortran
 ---
 
 # Numerical Methods Using Fortran Repository

_posts/2020-01-01-causality_and_correlation.md

@@ -4,7 +4,8 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-01'
-excerpt: Understand how causal reasoning helps us move beyond correlation, resolving paradoxes and leading to more accurate insights from data analysis.
+excerpt: Understand how causal reasoning helps us move beyond correlation, resolving
+  paradoxes and leading to more accurate insights from data analysis.
 header:
   image: /assets/images/data_science_4.jpg
   og_image: /assets/images/data_science_1.jpg
@@ -13,21 +14,25 @@ header:
   teaser: /assets/images/data_science_4.jpg
   twitter_image: /assets/images/data_science_1.jpg
 keywords:
-- Simpson's Paradox
+- Simpson's paradox
 - Causality
-- Berkson's Paradox
+- Berkson's paradox
 - Correlation
-- Data Science
-seo_description: Explore how causal reasoning, through paradoxes like Simpson's and Berkson's, can help us avoid the common pitfalls of interpreting data solely based on correlation.
+- Data science
+seo_description: Explore how causal reasoning, through paradoxes like Simpson's and
+  Berkson's, can help us avoid the common pitfalls of interpreting data solely based
+  on correlation.
 seo_title: 'Causality Beyond Correlation: Understanding Paradoxes and Causal Graphs'
 seo_type: article
-summary: An in-depth exploration of the limits of correlation in data interpretation, highlighting Simpson's and Berkson's paradoxes and introducing causal graphs as a tool for uncovering true causal relationships.
+summary: An in-depth exploration of the limits of correlation in data interpretation,
+  highlighting Simpson's and Berkson's paradoxes and introducing causal graphs as
+  a tool for uncovering true causal relationships.
 tags:
-- Simpson's Paradox
-- Berkson's Paradox
+- Simpson's paradox
+- Berkson's paradox
 - Correlation
-- Data Science
-- Causal Inference
+- Data science
+- Causal inference
 title: 'Causality Beyond Correlation: Simpson''s and Berkson''s Paradoxes'
 ---
 

_posts/2020-01-02-maximum_likelihood_estimation_statistical_modeling.md

@@ -4,7 +4,9 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-02'
-excerpt: Discover the fundamentals of Maximum Likelihood Estimation (MLE), its role in data science, and how it impacts businesses through predictive analytics and risk modeling.
+excerpt: Discover the fundamentals of Maximum Likelihood Estimation (MLE), its role
+  in data science, and how it impacts businesses through predictive analytics and
+  risk modeling.
 header:
   image: /assets/images/data_science_3.jpg
   og_image: /assets/images/data_science_3.jpg
@@ -13,24 +15,27 @@ header:
   teaser: /assets/images/data_science_3.jpg
   twitter_image: /assets/images/data_science_3.jpg
 keywords:
-- Machine Learning
-- Predictive Analytics
-- Statistical Modeling
-- Maximum Likelihood Estimation
-- MLE
-- bash
-- python
-seo_description: Explore Maximum Likelihood Estimation (MLE), its importance in data science, machine learning, and real-world applications.
+- Machine learning
+- Predictive analytics
+- Statistical modeling
+- Maximum likelihood estimation
+- Mle
+- Bash
+- Python
+seo_description: Explore Maximum Likelihood Estimation (MLE), its importance in data
+  science, machine learning, and real-world applications.
 seo_title: 'MLE: A Key Tool in Data Science'
 seo_type: article
-summary: This article covers the essentials of Maximum Likelihood Estimation (MLE), breaking down its mathematical foundation, importance in data science, practical applications, and limitations.
+summary: This article covers the essentials of Maximum Likelihood Estimation (MLE),
+  breaking down its mathematical foundation, importance in data science, practical
+  applications, and limitations.
 tags:
-- Statistical Modeling
-- bash
-- Maximum Likelihood Estimation
-- Data Science
-- MLE
-- python
+- Statistical modeling
+- Bash
+- Maximum likelihood estimation
+- Data science
+- Mle
+- Python
 title: 'Maximum Likelihood Estimation (MLE): Statistical Modeling in Data Science'
 ---
 

_posts/2020-01-03-assessing_goodness-of-fit_non-parametric_data.md

@@ -4,7 +4,9 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-03'
-excerpt: The Kolmogorov-Smirnov test is a powerful tool for assessing goodness-of-fit in non-parametric data. Learn how it works, how it compares to the Shapiro-Wilk test, and explore real-world applications.
+excerpt: The Kolmogorov-Smirnov test is a powerful tool for assessing goodness-of-fit
+  in non-parametric data. Learn how it works, how it compares to the Shapiro-Wilk
+  test, and explore real-world applications.
 header:
   image: /assets/images/data_science_3.jpg
   og_image: /assets/images/data_science_3.jpg
@@ -13,21 +15,26 @@ header:
   teaser: /assets/images/data_science_3.jpg
   twitter_image: /assets/images/data_science_3.jpg
 keywords:
-- Kolmogorov-Smirnov test
-- goodness-of-fit tests
-- non-parametric statistics
-- distribution fitting
-- Shapiro-Wilk test
-seo_description: This article introduces the Kolmogorov-Smirnov test for assessing goodness-of-fit in non-parametric data, comparing it with other tests like Shapiro-Wilk, and exploring real-world use cases.
+- Kolmogorov-smirnov test
+- Goodness-of-fit tests
+- Non-parametric statistics
+- Distribution fitting
+- Shapiro-wilk test
+seo_description: This article introduces the Kolmogorov-Smirnov test for assessing
+  goodness-of-fit in non-parametric data, comparing it with other tests like Shapiro-Wilk,
+  and exploring real-world use cases.
 seo_title: 'Kolmogorov-Smirnov Test: A Guide to Non-Parametric Goodness-of-Fit Testing'
 seo_type: article
-summary: This article explains the Kolmogorov-Smirnov (K-S) test for assessing the goodness-of-fit of non-parametric data. We compare the K-S test to other goodness-of-fit tests, such as Shapiro-Wilk, and provide real-world use cases, including testing whether a dataset follows a specific distribution.
+summary: This article explains the Kolmogorov-Smirnov (K-S) test for assessing the
+  goodness-of-fit of non-parametric data. We compare the K-S test to other goodness-of-fit
+  tests, such as Shapiro-Wilk, and provide real-world use cases, including testing
+  whether a dataset follows a specific distribution.
 tags:
-- Kolmogorov-Smirnov Test
-- Goodness-of-Fit Tests
-- Non-Parametric Data
-- Shapiro-Wilk Test
-- Distribution Fitting
+- Kolmogorov-smirnov test
+- Goodness-of-fit tests
+- Non-parametric data
+- Shapiro-wilk test
+- Distribution fitting
 title: 'Kolmogorov-Smirnov Test: Assessing Goodness-of-Fit in Non-Parametric Data'
 ---
 

_posts/2020-01-04-multiple_comparisons_problem:_bonferroni_correction_other_solutions.md

@@ -4,7 +4,9 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-04'
-excerpt: The multiple comparisons problem arises in hypothesis testing when performing multiple tests increases the likelihood of false positives. Learn about the Bonferroni correction and other solutions to control error rates.
+excerpt: The multiple comparisons problem arises in hypothesis testing when performing
+  multiple tests increases the likelihood of false positives. Learn about the Bonferroni
+  correction and other solutions to control error rates.
 header:
   image: /assets/images/data_science_6.jpg
   og_image: /assets/images/data_science_6.jpg
@@ -13,23 +15,28 @@ header:
   teaser: /assets/images/data_science_6.jpg
   twitter_image: /assets/images/data_science_6.jpg
 keywords:
-- multiple comparisons problem
+- Multiple comparisons problem
 - Bonferroni correction
-- Holm-Bonferroni
-- false discovery rate
-- hypothesis testing
-- python
-seo_description: This article explains the multiple comparisons problem in hypothesis testing and discusses solutions such as Bonferroni correction, Holm-Bonferroni, and FDR, with practical applications in fields like medical studies and genetics.
+- Holm-bonferroni
+- False discovery rate
+- Hypothesis testing
+- Python
+seo_description: This article explains the multiple comparisons problem in hypothesis
+  testing and discusses solutions such as Bonferroni correction, Holm-Bonferroni,
+  and FDR, with practical applications in fields like medical studies and genetics.
 seo_title: 'Understanding the Multiple Comparisons Problem: Bonferroni and Other Solutions'
 seo_type: article
-summary: This article explores the multiple comparisons problem in hypothesis testing, discussing solutions like the Bonferroni correction, Holm-Bonferroni method, and False Discovery Rate (FDR). It includes practical examples from experiments involving multiple testing, such as medical studies and genetics.
+summary: This article explores the multiple comparisons problem in hypothesis testing,
+  discussing solutions like the Bonferroni correction, Holm-Bonferroni method, and
+  False Discovery Rate (FDR). It includes practical examples from experiments involving
+  multiple testing, such as medical studies and genetics.
 tags:
-- Multiple Comparisons Problem
-- Bonferroni Correction
-- Holm-Bonferroni
-- False Discovery Rate (FDR)
-- Multiple Testing
-- python
+- Multiple comparisons problem
+- Bonferroni correction
+- Holm-bonferroni
+- False discovery rate (fdr)
+- Multiple testing
+- Python
 title: 'Multiple Comparisons Problem: Bonferroni Correction and Other Solutions'
 ---
 

_posts/2020-01-05-one-way_anova_vs._two-way_anova_when_use_which.md

@@ -4,7 +4,9 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-05'
-excerpt: One-way and two-way ANOVA are essential tools for comparing means across groups, but each test serves different purposes. Learn when to use one-way versus two-way ANOVA and how to interpret their results.
+excerpt: One-way and two-way ANOVA are essential tools for comparing means across
+  groups, but each test serves different purposes. Learn when to use one-way versus
+  two-way ANOVA and how to interpret their results.
 header:
   image: /assets/images/data_science_1.jpg
   og_image: /assets/images/data_science_1.jpg
@@ -13,21 +15,25 @@ header:
   teaser: /assets/images/data_science_1.jpg
   twitter_image: /assets/images/data_science_1.jpg
 keywords:
-- one-way ANOVA
-- two-way ANOVA
-- interaction effects
-- main effects
-- hypothesis testing
-seo_description: This article explores the differences between one-way and two-way ANOVA, when to use each test, and how to interpret main effects and interaction effects in two-way ANOVA.
+- One-way anova
+- Two-way anova
+- Interaction effects
+- Main effects
+- Hypothesis testing
+seo_description: This article explores the differences between one-way and two-way
+  ANOVA, when to use each test, and how to interpret main effects and interaction
+  effects in two-way ANOVA.
 seo_title: 'One-Way ANOVA vs. Two-Way ANOVA: When to Use Which'
 seo_type: article
-summary: This article discusses one-way and two-way ANOVA, focusing on when to use each method. It explains how two-way ANOVA is useful for analyzing interactions between factors and details the interpretation of main effects and interactions.
+summary: This article discusses one-way and two-way ANOVA, focusing on when to use
+  each method. It explains how two-way ANOVA is useful for analyzing interactions
+  between factors and details the interpretation of main effects and interactions.
 tags:
-- One-Way ANOVA
-- Two-Way ANOVA
-- Interaction Effects
-- Main Effects
-- Hypothesis Testing
+- One-way anova
+- Two-way anova
+- Interaction effects
+- Main effects
+- Hypothesis testing
 title: 'One-Way ANOVA vs. Two-Way ANOVA: When to Use Which'
 ---