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Introduction to Data Science: How to “Big Data” with Python

Dataconomy

Katharine Jarmul and Data Natives are joining forces to give you an amazing chance to delve deeply into Python and how to apply it to data manipulation, and data wrangling. By the end of her workshop, Learn Python for Data Analysis, you will feel comfortable importing and running simple Python analysis on your.

Big Data 196
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Object-centric Process Mining on Data Mesh Architectures

Data Science Blog

In addition to Business Intelligence (BI), Process Mining is no longer a new phenomenon, but almost all larger companies are conducting this data-driven process analysis in their organization. The creation of this data model requires the data connection to the source system (e.g.

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How to Ensure AI Models Reflect the Richness of Human Diversity

Towards AI

Insights from bridging data science and cultural understandingDall-E image:impressionist painting interpretation of a herring boat on the open ocean At my core I am a numbers guy, a computer scientist by trade, fascinated by data and what information can be gleaned from it. Isn’t AI just great for this sort of analysis?

AI 116
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Michael I. Jordan of Berkeley on Learning-Aware Mechanism Design

ODSC - Open Data Science

As newer fields emerge within data science and the research is still hard to grasp, sometimes it’s best to talk to the experts and pioneers of the field. His research interests bridge the computational, statistical, cognitive, biological, and social sciences. Recently, we spoke with Michael I.

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Data Analysis at Warp Speed: Explore the World of Polars

Mlearning.ai

Empowering Data Scientists and Engineers with Lightning-Fast Data Analysis and Transformation Capabilities Photo by Hans-Jurgen Mager on Unsplash ?Goal ⏱️Performance benchmarking Let’s try it on Kaggle competition dataset based on the 2016 NYC Yellow Cab trip record data and see the numbers using different libraries.

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Data Fabric and Address Verification Interface

IBM Data Science in Practice

Data fabric is defined by IBM as “an architecture that facilitates the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems.” The concept was first introduced back in 2016 but has gained more attention in the past few years as the amount of data has grown.

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Beyond the Checkered Flag: F1 Statistics Explored

Towards AI

Analyzing F1 from a fan and data science perspective could help gain useful insights. Image by Author Tools The following tools were used to assist the data analysis process: Tableau: Used to create the visualizations. Remove erroneous values. Create a new CSV file based on the newly cleaned dataset.