## about this topic
Data science is the discipline that turns raw data into decisions — combining statistics, programming and domain knowledge. This hub gathers the CodeCrush articles on data and machine learning: statistical fundamentals (regression, hypothesis testing, biases such as Simpson’s paradox), tools from the Python ecosystem (NumPy, pandas), analysis techniques and the machine learning models used in production. Unlike the Artificial Intelligence hub — focused on applications and LLMs — the focus here is the analytical foundation: how models learn, how to evaluate results rigorously and how to build a reliable data pipeline. Ideal for anyone moving into the data field, or wanting to put technical ground under their AI work.
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Articles about Data Science and Machine Learning
- AI and Data
AI for Healthcare: what it is and how it transforms care
AI for healthcare uses machine learning to analyze medical data, support diagnoses, accelerate drugs, and personalize treatments with greater precision.
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Henrico - AI and Data
Computational Immunology: What It Is and How It Works
Computational immunology is the field that applies bioinformatics, mathematical models, and machine learning to study and predict the behavior of the immune system.
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Ana-carolina - AI and Data
Simpson's Paradox: when aggregated data deceives
Simpson's Paradox occurs when a trend seen in subgroups disappears or reverses when the data is aggregated. See why it happens and how to handle it in Python.
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Renata - AI and Data
NumPy: what it is and what the Python library is for
NumPy is the Python library for numerical computing that provides multidimensional arrays and fast vectorized operations for data and science.
Read articleHenrico - AI and Data
Euclidean Distance: What It Is, Formula, and Uses in Python
Euclidean distance is the straight-line measure between two points, obtained by the root of the sum of squared differences; it is the basis of KNN and K-Means.
Read articleHenrico - Programming
The R language: what it is, what it is for, and how to get started
R is an open-source language for statistics and data visualization, with more than 24 thousand packages on CRAN and central use in data science.
Read articleRenata - AI and Data
Machine Learning fundamentals: concepts and algorithms
Machine Learning is the AI field where algorithms learn patterns from data. See the algorithms, learning types, and evaluation metrics.
Read articleRenata - AI and Data
Types of Linear Regression: Simple, Multiple, Ridge, and Lasso
Meet the types of linear regression — simple, multiple, polynomial, Ridge, and Lasso — with equations, use cases, and criteria for choosing the right model.
Read articleHenrico - AI and Data
Artificial Intelligence in Medicine: Benefits and Risks
AI in medicine accelerates diagnoses and personalizes treatments: the FDA has already authorized more than 1,250 AI-enabled devices. See benefits and risks.
Read articleHenrico - AI and Data
Data Mining vs Machine Learning vs Deep Learning
Data mining discovers patterns in existing datasets; machine learning trains models that learn; deep learning uses deep neural networks.
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How to Create Machine Learning Projects: A 5-Step Guide
Machine learning projects require data preparation, suitable algorithms, iterative pipelines, scalability, and metrics like recall and F1-Score.
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Machine Learning vs Artificial Intelligence: the difference
Artificial Intelligence is the broad field that simulates human capabilities; Machine Learning is the subarea where algorithms learn from data.
Read articleRenata - Security
Gatekeeper and UBA: User Behavior Analytics
UBA monitors user behavior to detect anomalies; integrated with Gatekeeper, it exposes internal threats that authentication alone cannot see.
Read articleHenrico - Infrastructure and DevOps
Data Warehouse: what it is, architecture and how it works
A Data Warehouse is a central repository that integrates data from several sources for historical analysis, reporting and decision support via ETL and BI.
Read articleRenata - AI and Data
What is Power BI and what it is for in data analysis
Power BI is Microsoft''s business intelligence suite that turns raw data into interactive dashboards and reports for data-driven decisions.
Read articleRenata - AI and Data
Data Pipeline: what it is and how it works
A data pipeline is a series of automated processes that collect, transform and move data from source to destination for analysis and decision making.
Read articleHenrico - AI and Data
Georeferencing: what it is and how it works
Georeferencing is the process of assigning geographic coordinates to data or objects, allowing them to be located and analyzed precisely on a map.
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Data Science: 65 Application Areas with Real Examples
Data science uses statistics and machine learning to drive decisions: see 65 application areas, from finance and health to smart cities.
Read articleRenata - AI and Data
How to Study Machine Learning: A Guide to Methods and Resources
To study Machine Learning, start with Python, statistics, and linear algebra, move on to algorithms, and practice on real projects on Kaggle.
Read articleHenrico & Renata - AI and Data
Linear Regression: What It Is, How to Model and Evaluate
Understand linear regression: how it models relationships between variables with a line, predicts continuous values, and is evaluated with R², RMSE, and residual analysis.
Read articleHenrico - AI and Data
Machine Learning: what it is, how it works and where it is used
Machine Learning is the field of AI in which algorithms learn patterns from data to predict, classify and decide without explicit programming.
Read articleHenrico