Remove Data Collection Remove Data Quality Remove Deep Learning Remove Statistics
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The quest for high-quality data

O'Reilly on Data

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

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15 best data science bootcamps for boosting your career

CIO Business Intelligence

An education in data science can help you land a job as a data analyst , data engineer , data architect , or data scientist. It’s a fast growing and lucrative career path, with data scientists reporting an average salary of $122,550 per year , according to Glassdoor. Top 15 data science bootcamps.

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Bringing an AI Product to Market

O'Reilly on Data

This tradeoff between impact and development difficulty is particularly relevant for products based on deep learning: breakthroughs often lead to unique, defensible, and highly lucrative products, but investing in products with a high chance of failure is an obvious risk. Data Quality and Standardization.

Marketing 362
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What you need to know about product management for AI

O'Reilly on Data

Pragmatically, machine learning is the part of AI that “works”: algorithms and techniques that you can implement now in real products. We won’t go into the mathematics or engineering of modern machine learning here. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before.

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AI adoption in the enterprise 2020

O'Reilly on Data

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data.

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Data Science, Past & Future

Domino Data Lab

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

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Themes and Conferences per Pacoid, Episode 7

Domino Data Lab

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. One-fifth use reinforcement learning.