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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 predictiveanalytics (using models to forecast future outcomes).
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: ExploratoryDataAnalysis (EDA) MEV Over 25,000 MEV-related transactions have been executed through Curve.
AI / ML offers tools to give a competitive edge in predictiveanalytics, 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.
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.
ExploratoryDataAnalysis (EDA) ExploratoryDataAnalysis (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.
Summary: AI in Time Series Forecasting revolutionizes predictiveanalytics by leveraging advanced algorithms to identify patterns and trends in temporal data. This is due to the growing adoption of AI technologies for predictiveanalytics. Making Data Stationary: Many forecasting models assume stationarity.
ExploratoryDataAnalysis (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.
It involves deeper analysis and investigation to identify the root causes of problems or successes. Root cause analysis is a typical diagnostic analytics task. 3. PredictiveAnalytics Projects: Predictiveanalytics involves using historical data to predict future events or outcomes.
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