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Python is coming to Excel

FlowingData

From Microsoft : Excel users now have access to powerful analytics via Python for visualizations, cleaning data, machine learning, predictive analytics, and more. Using Excel’s built-in connectors and Power Query, users can easily bring external data into Python in Excel workflows.

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Training your AI, not just your team: A marketer’s guide to smarter campaigns

Dataconomy

AI in marketing refers to the use of machine learning (ML), natural language processing (NLP), and predictive analytics to automate, optimize, and personalize campaigns at scale. Pro Tip “Treat AI like a new hiretrain it with clean data, document its decisions, and supervise its work.” Lets dive right in.

AI 113
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AI Revolutionizing IT Support: Transforming Efficiency and Enhancing User Experience

Data Science Connect

From chatbots to predictive analytics, AI-powered solutions are transforming how businesses handle technical support challenges. Predictive Analytics for Issue Prevention Predictive analytics powered by AI can identify patterns and trends in IT infrastructure data.

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How Dataiku and Snowflake Strengthen the Modern Data Stack

phData

From data ingestion and cleaning to model deployment and monitoring, the platform streamlines each phase of the data science workflow. Automated features, such as visual data preparation and pre-built machine learning models, reduce the time and effort required to build and deploy predictive analytics.

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Use Data Enrichment to Supercharge AI

Precisely

It has been at the heart of many innovations over the past two years, powering everything from the chatbots that enhance our customer experiences to the predictive analytics engines that help us make financial decisions. High-integrity data avoids the introduction of noise, resulting in more robust models.

AI 121
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Data Science Career Paths: Analyst, Scientist, Engineer – What’s Right for You?

How to Learn Machine Learning

It’s not simply about the numbers, but how they can communicate the story behind the data to then model complex datasets into insights that stakeholders can act on. So, they very often work with data engineers, analysts, and business partners to achieve that.

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Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

This crucial step involves handling missing values, correcting errors (addressing Veracity issues from Big Data), transforming data into a usable format, and structuring it for analysis. This often takes up a significant chunk of a data scientist’s time. What Industries Benefit Most from Big Data and Data Science?