Feb 23 · 1 min read · This article provides a deep analysis of Judea Pearl's Ladder of Causality, challenging contemporary approaches to data analysis. The text explains why traditional statistics and machine learning, based primarily on correlation, are insufficient for ...
Join discussionDec 14, 2025 · 3 min read · This week at the DataraFlow program covered web scraping. This involves using Python to search webpages for tables directly, without needing to download an Excel or CSV file. As part of this week’s assessment, I was tasked with proving my web scrapin...
Join discussionNov 26, 2025 · 4 min read · Raphael Ajiboye Olabanji • Data Science Student @ Dataraflow, Lagos 20 min read • #pandas #DataScience #WorldBank #ClimateChange #Energy #100DaysOfCode #NigeriaTech #LearnInPublic This wasn’t just another week. This was the week. The week I stopped c...
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Nov 12, 2025 · 3 min read · Last week already focused on how to clean data, so this week took a deeper dive to explore how to transform and combine datasets and dataframes to suit the needs of a data scientist. \ JOINING DATAFRAMES TOGETHER To kick things off, we learnt about t...
Join discussionNov 11, 2025 · 2 min read · From Joining Datasets to Discovering CorrelationsExploring how data integration, transformation, and visualization reveal deeper insights. This week’s focus was on how to transform and combine datasets—a vital step in preparing data for analysis. Bui...
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Jun 10, 2025 · 6 min read · Introduction In this project, I performed web scraping via the Central Bank of Brazil's (BCB) public API and automated the collection of historical data for a set of indicators grouped into four analytical categories: Sectoral Costs: IPCA Health, Me...
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Oct 13, 2024 · 5 min read · In the world of statistics and data analysis, understanding how different variables interact is crucial for building effective models. Three important concepts that help us analyze these relationships are covariance, correlation, and collinearity. In...
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Aug 26, 2024 · 4 min read · Introduction: Understanding Correlation and Causation in Data Science In the world of data science, understanding the difference between correlation and causation is fundamental. Correlation refers to a statistical relationship between two variables,...
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Oct 30, 2023 · 2 min read · Introduction In JMeter, correlation is very important because with the help of correlation, we can capture and store the dynamic value inside a variable and use it wherever it's required in the subsequent request. We perform correlation because dynam...
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Aug 14, 2023 · 4 min read · Correlations lie at the heart of data science, serving as crucial tools to uncover relationships between variables and unveil hidden insights within complex datasets. As a data scientist, understanding different types of correlations is essential for...
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