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eCommerce Brands Use Data Analytics for Conversion Rate Optimization

Smart Data Collective

One benefit is that they can help with conversion rate optimization. Collecting Relevant Data for Conversion Rate Optimization Here is some vital data that e-commerce businesses need to collect to improve their conversion rates. Experimentation is the key to finding the highest-yielding version of your website elements.

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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

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Your New Cloud for AI May Be Inside a Colo

CIO Business Intelligence

The cloud is great for experimentation when data sets are smaller and model complexity is light. However, this repatriation can mean more headaches for data science and IT teams to design, deploy and manage infrastructure optimized for AI as the workloads return on premises.

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The CIO’s 2024 AI playbook

CIO Business Intelligence

After a year of frenzied experimentation and investment, executives will have to identify truly valid use cases (and ROI) for AI in 2024. Here are three strategies designed to help CIOs and others maximize their return not just on AI, but all essential tech.

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10 Technical Blogs for Data Scientists to Advance AI/ML Skills

DataRobot Blog

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. After DataRobot has determined an optimal model, Continuous AI helps ensure that the currently deployed model will always be the best one, even as the world changes around it. Read the blog.

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12 data science certifications that will pay off

CIO Business Intelligence

The certification consists of several exams that cover topics such as machine learning, natural language processing, computer vision, and model forecasting and optimization. You should also have experience with pattern detection, experimentation in business optimization techniques, and time-series forecasting.

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How To “Ultralearn” Data Science: summary, for those in a hurry

KDnuggets

For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science.