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Every time someone runs a correlation coefficient on two time series, an angel loses their wings
We all know correlation doesn’t equal causality at this point, but when working with time series data, correlation can lead you to come to the wrong conclusion.
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Advice For Applying To Data Science Jobs
A comprehensive guide to applying for a job in data science, covering the application, interview and offer stage.
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How (dis)similar are my train and test data?
This articles examines a scenario where your machine learning model can fail.
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Audience Segmentation
The process of audience segmentation is not about just statistics, it’s about finding your ideal clients and choosing the right way of interaction with them.
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The Future of Artificial Intelligence: Is Your Job Under Threat?
This article examines the rapid growth of artificial intelligence: how we got to this point, the future AI job market and what can be done.
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Overview of Dash Python Framework from Plotly for building dashboards
Introduction to Dash framework from Plotly, reactive framework for building dashboards in Python. Tech talk covers basics and more advanced topics like custom component and scaling.
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Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM
CRISP-DM methodology is a must teach to explain analytics project steps. This article purpose it to complement it with specific chart flow that explain as simply as possible how it is more likely used in descriptive analytics, classic machine learning or deep learning.
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Event Processing: Three Important Open Problems
This article summarizes the three most important problems to be solved in event processing. The facts in this article are supported by a recent survey and an analysis conducted on the industry trends.
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Data Science: 4 Reasons Why Most Are Failing to Deliver
Data Science: Some see billions in returns, but most are failing to deliver. This article explores some of the reasons why this is the case.
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Scientific debt – what does it mean for Data Science?
This article analyses scientific debt - what it is and what it means for data science.
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