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Introduction to applied data science 101: Key concepts and methodologies 

Data Science Dojo

Statistical analysis and hypothesis testing Statistical methods provide powerful tools for understanding data. Hypothesis testing, correlation, and regression analysis, and distribution analysis are some of the essential statistical tools that data scientists use.

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Your Roadmap to Learn AI from Scratch 2024

Pickl AI

Specialise in domains like machine learning or natural language processing to deepen expertise. Natural Language Processing (NLP): NLP enables computers to understand, interpret, and generate human language, facilitating communication between humans and machines.

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The 2021 Executive Guide To Data Science and AI

Applied Data Science

They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data.

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Introduction to R Programming For Data Science

Pickl AI

It provides functions for descriptive statistics, hypothesis testing, regression analysis, time series analysis, survival analysis, and more. It offers a comprehensive set of built-in statistical functions and packages for hypothesis testing, regression analysis, time series analysis, survival analysis, and more.

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Rethinking Large Language Models for NLP: Alternatives and Efficiency

Mlearning.ai

Photo by Google DeepMind on Unsplash Introduction Large language models, or LLMs, are powerful deep learning algorithms that are capable of a range of tasks, including recognizing, summarizing, translating, predicting, and generating text and other content. They made a hypothesis testing with the Chinchilla model.

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What We Learned Auditing Sophisticated AI for Bias

O'Reilly Media

For the LLM and NER task, BNH.AI ’s audit plan required demographics associated with entities in raw text, and possibly text in multiple languages. While there are many interesting and useful benchmark datasets for testing bias in natural language processing, none provided these types of exhaustive demographic labels.

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What’s the Difference: Quantitative vs Qualitative Data

Alation

Academic Quantitative Analysis represents the next chapter in zip code analysis; this form of analysis focuses on the interplay between variables after they have been operationalized, allowing the analyst to study and measure outcomes ( Quantitative and statistical research methods: from hypothesis to results , Bridgmon & Martin, 2006.).