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Data Science Central Profile
Data Science Central

@analyticbridge

Followers
229K
Following
8K
Media
6K
Statuses
143K

Part of the DSC community, our focus is on the evolving future of data and the technology that is driven by it. Community Editor is Kurt Cagle.

Seattle, WA
Joined March 2008
Don't wanna be here? Send us removal request.
@analyticbridge
Data Science Central
5 years
Free Machine Learning, AI and Data Science Books
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356
@analyticbridge
Data Science Central
3 years
The Riemann Hypothesis in One Picture: https://t.co/v4If0WZH9C - I wrote this article for machine learning and analytic professionals in general. Actually, I describe a new visual, simple, intuitive method for supervised classification. It involves synthetic data and explainable
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@analyticbridge
Data Science Central
3 years
Reinventing or Reusing? Home-made vs Third-party Solutions. https://t.co/AaU8D2fsV5 The decision does not need to be a binary one. I discuss the pluses and minuses of both options. Combining them offers the best of both worlds. I explain with examples how to do it.
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@analyticbridge
Data Science Central
3 years
In each episode of Data Point of View, members of the @Mobilewalla team join data science leaders and experts to discuss topics like #predictivemodeling, #featureengineering, and #dataenrichment. #sponsored Check out all the episodes here:
mobilewalla.com
Unleash the power of data! Mobilewalla podcasts bring you insights on AI, machine learning, data science & more
2
4
19
@analyticbridge
Data Science Central
3 years
In the third-party #dataenrichment market, data quality is not equal across the board. Read to find out from @Mobilewalla why researching third-party data partners and ensuring they are providing high-quality, privacy-compliant data is a must. #sponsored
Tweet card summary image
mobilewalla.com
High-quality data is a must-have for brands looking to gather insights from third-party data and those improving their predictive modeling capabilities.
1
8
21
@analyticbridge
Data Science Central
3 years
#AI has different data requirements, #datacentricai changes the thinking and leads to better #precitivemodels, read why in this @Mobilewalla white paper, #sponsored
Tweet card summary image
mobilewalla.com
This guide will explain how data quality, breadth, and depth are crucial to building accurate predictive models with AI.
2
7
18
@analyticbridge
Data Science Central
3 years
#Predictivemodeling combines features and #machinelearning to predict outcomes. In this @Mobilewalla Technology Brief, they explore how to streamline #featureengineering for better predictive modeling results. Read the brief. #sponsored
mobilewalla.com
Feature selection is time-consuming and error-prone. Discover data science tactics data for streamlined feature engineering and more accurate predictive models.
0
2
12
@analyticbridge
Data Science Central
3 years
In each episode of Data Point of View, members of the @Mobilewalla team join data science leaders and experts to discuss topics like #predictivemodeling, #featureengineering, and #dataenrichment. #sponsored Check out all the episodes here:
mobilewalla.com
Unleash the power of data! Mobilewalla podcasts bring you insights on AI, machine learning, data science & more
1
0
6
@analyticbridge
Data Science Central
3 years
In the third-party #dataenrichment market, data quality is not equal across the board. Read to find out from @Mobilewalla why researching third-party data partners and ensuring they are providing high-quality, privacy-compliant data is a must. #sponsored
Tweet card summary image
mobilewalla.com
High-quality data is a must-have for brands looking to gather insights from third-party data and those improving their predictive modeling capabilities.
0
0
9
@analyticbridge
Data Science Central
4 years
#AI has different data requirements, #datacentricai changes the thinking and leads to better #precitivemodels, read why in this @Mobilewalla white paper, #sponsored
Tweet card summary image
mobilewalla.com
This guide will explain how data quality, breadth, and depth are crucial to building accurate predictive models with AI.
0
0
5
@analyticbridge
Data Science Central
4 years
#Predictivemodeling combines features and #machinelearning to predict outcomes. In this @Mobilewalla Technology Brief, they explore how to streamline #featureengineering for better predictive modeling results. Read the brief. #sponsored
mobilewalla.com
Feature selection is time-consuming and error-prone. Discover data science tactics data for streamlined feature engineering and more accurate predictive models.
0
0
8
@analyticbridge
Data Science Central
4 years
In each episode of Data Point of View, members of the @Mobilewalla team join data science leaders and experts to discuss topics like #predictivemodeling, #featureengineering, and #dataenrichment. #sponsored Check out all the episodes here:
mobilewalla.com
Unleash the power of data! Mobilewalla podcasts bring you insights on AI, machine learning, data science & more
0
0
3
@analyticbridge
Data Science Central
4 years
In the third-party #dataenrichment market, data quality is not equal across the board. Read to find out from @Mobilewalla why researching third-party data partners and ensuring they are providing high-quality, privacy-compliant data is a must. #sponsored
Tweet card summary image
mobilewalla.com
High-quality data is a must-have for brands looking to gather insights from third-party data and those improving their predictive modeling capabilities.
0
0
5
@analyticbridge
Data Science Central
4 years
#Predictivemodeling combines features and #machinelearning to predict outcomes. In this @Mobilewalla Technology Brief, they explore how to streamline #featureengineering for better predictive modeling results. Read the brief. #sponsored
mobilewalla.com
Feature selection is time-consuming and error-prone. Discover data science tactics data for streamlined feature engineering and more accurate predictive models.
0
3
10
@analyticbridge
Data Science Central
4 years
#AI has different data requirements, #datacentricai changes the thinking and leads to better #precitivemodels, read why in this @Mobilewalla white paper, #sponsored
Tweet card summary image
mobilewalla.com
This guide will explain how data quality, breadth, and depth are crucial to building accurate predictive models with AI.
1
2
6
@analyticbridge
Data Science Central
4 years
In each episode of Data Point of View, members of the @Mobilewalla team join data science leaders and experts to discuss topics like #predictivemodeling, #featureengineering, and #dataenrichment. #sponsored Check out all the episodes here:
mobilewalla.com
Unleash the power of data! Mobilewalla podcasts bring you insights on AI, machine learning, data science & more
0
0
5
@analyticbridge
Data Science Central
4 years
#Predictivemodeling combines features and #machinelearning to predict outcomes. In this @Mobilewalla Technology Brief, they explore how to streamline #featureengineering for better predictive modeling results. Read the brief. #sponsored
mobilewalla.com
Feature selection is time-consuming and error-prone. Discover data science tactics data for streamlined feature engineering and more accurate predictive models.
0
1
4
@analyticbridge
Data Science Central
4 years
In the third-party #dataenrichment market, data quality is not equal across the board. Read to find out from @Mobilewalla why researching third-party data partners and ensuring they are providing high-quality, privacy-compliant data is a must. #sponsored
Tweet card summary image
mobilewalla.com
High-quality data is a must-have for brands looking to gather insights from third-party data and those improving their predictive modeling capabilities.
0
2
2