Merge pull request #87 from DiogoRibeiro7/feat/reserve_branche

Feat/reserve branche

Author: github-actions[bot]

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

@@ -5,8 +5,7 @@ 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
@@ -20,12 +19,10 @@ keywords:
 - 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.
+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
 - Sustainability

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

@@ -6,8 +6,7 @@ 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
@@ -21,12 +20,10 @@ keywords:
 - 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_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

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

@@ -19,13 +19,7 @@ There are several interesting article topics you can explore under the umbrella
    - **Overview**: Explain what predictive maintenance (PdM) is and how it differs from preventive and reactive maintenance. 
    - **Focus**: Basic techniques and traditional approaches to predictive maintenance, including time-based and condition-based maintenance strategies.
 
-### 2. The Role of Data Science in Predictive Maintenance
-   - **Overview**: Explore how data science methods, such as statistical analysis and predictive modeling, enable organizations to forecast failures and optimize maintenance schedules.
-   - **Focus**: Key data science techniques used in PdM, such as regression, anomaly detection, and clustering.
 
-### 3. How Big Data is Transforming Predictive Maintenance
-   - **Overview**: Discuss how the vast amounts of data generated by IoT sensors, machinery, and operational systems contribute to more accurate predictions and better decision-making.
-   - **Focus**: Challenges and opportunities in managing and analyzing big data in PdM, such as data storage, cleaning, and integration.
 
 ### 4. Machine Learning Models for Predictive Maintenance
    - **Overview**: An in-depth guide on how machine learning models are applied in PdM, covering supervised, unsupervised, and reinforcement learning techniques.

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

@@ -4,19 +4,16 @@ 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.
+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

_posts/-_ideas/numerical_methods_fortran.md

@@ -2,6 +2,8 @@
 tags:
 - Plaintext
 - Fortran
+- plaintext
+- fortran
 ---
 
 # Numerical Methods Using Fortran Repository

_posts/2020-01-01-causality_and_correlation.md

@@ -4,8 +4,7 @@ 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
@@ -19,14 +18,10 @@ keywords:
 - 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.
+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

_posts/2020-01-02-maximum_likelihood_estimation_statistical_modeling.md

@@ -4,9 +4,7 @@ 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
@@ -22,20 +20,21 @@ keywords:
 - Mle
 - Bash
 - Python
-seo_description: Explore Maximum Likelihood Estimation (MLE), its importance in data
-  science, machine learning, and real-world applications.
+- python
+- bash
+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
+- python
+- bash
 title: 'Maximum Likelihood Estimation (MLE): Statistical Modeling in Data Science'
 ---
 

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

@@ -4,9 +4,7 @@ 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
@@ -20,15 +18,10 @@ keywords:
 - 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_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

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

@@ -4,9 +4,7 @@ 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
@@ -21,22 +19,19 @@ keywords:
 - 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.
+- 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
+- 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

@@ -4,9 +4,7 @@ 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
@@ -20,14 +18,10 @@ keywords:
 - 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_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