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From Data to Decisions: Deep Dive into Workshop Learnings

Women in Big Data

Learning Objectives Recap: Paradigms in Data Science: We explored the two main paradigms in data science: descriptive analytics (understanding what happened in the past) and predictive analytics (using models to forecast future outcomes).

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Curve Finance Data Challenge Review & Insights Research

Ocean Protocol

Abstract This research report encapsulates the findings from the Curve Finance Data Challenge , a competition that engaged 34 participants in a comprehensive analysis of the decentralized finance protocol. Part 1: Exploratory Data Analysis (EDA) MEV Over 25,000 MEV-related transactions have been executed through Curve.

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Announcing the Winners of ‘The NFL Fantasy Football’ Data Challenge

Ocean Protocol

AI / ML offers tools to give a competitive edge in predictive analytics, business intelligence, and performance metrics. Fantasy Football is a popular pastime for a large amount of the world, we gathered data around the past 6 seasons of player performance data to see what our community of data scientists could create.

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How to tackle lack of data: an overview on transfer learning

Data Science Blog

And importantly, starting naively annotating data might become a quick solution rather than thinking about how to make uses of limited labels if extracting data itself is easy and does not cost so much. In that case, you tasks have your own problem, and you would have to be careful about your EDA, data cleaning, and labeling.

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Exploring Different Types of Data Analysis: Methods and Applications

Pickl AI

Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is an approach to analyse datasets to uncover patterns, anomalies, or relationships. The primary purpose of EDA is to explore the data without any preconceived notions or hypotheses.

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AI in Time Series Forecasting

Pickl AI

Summary: AI in Time Series Forecasting revolutionizes predictive analytics by leveraging advanced algorithms to identify patterns and trends in temporal data. This is due to the growing adoption of AI technologies for predictive analytics. Making Data Stationary: Many forecasting models assume stationarity.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Exploratory Data Analysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset. Techniques such as statistical summaries, data visualisation, and correlation analysis help uncover patterns, anomalies, and relationships within the data.