How to Start a Data Science Project With No or Little Data
Little data? No problem! Best practices for starting an ML project with little or no data. Also how to handle sensitive data and recommendations on what models to use.
Hajar Khizou
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11 minArticles in this topic
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Hajar Khizou
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11 min
Keep it Clean: Why Bad Data Ruins Projects and How to Fix it
This presentation will discuss in what circumstances bad data can affect your project along with some high-profile case studies. We will then spend as much time as we have going through some of the techniques you will need to fix that bad data. This is aimed toward those with intermediate-level Data Science experience.
Dr. Phil Winder
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1 min
Google Releases AI Platform with help from Winder.AI
Google has announced a new range of AI products to help Engineers rapidly develop and deploy models. Winder.AI helped develop content for the AI-Hub.
Dr. Phil Winder
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3 minDevOps and Data Science: DataDevOps?
DataDevOps or often just DataOps, is the combination of DevOps and Data Science. The goal is to empower Data Scientists to build reliable software. The downside, you need to be an expert at everything! Read more.
Dr. Phil Winder
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8 min
Using Data Science to block hackers
Winder.AI developed novel attack detection algorithms for leading web application firewall company, Bitsensor. Using a combination of Data Science, Machine Learning and Cloud-Native technologies, we helped Bitsensor deliver the product their customers needed.
Dr. Phil Winder
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3 min
Cloud Native Data Science: Best Practices
Cloud Native Data Science best practices that you need to know. Find out more.
Dr. Phil Winder
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5 min
Cloud Native Data Science: Technology
Three key technology choices that must be answered before embarking on a Cloud Native Data Science project.
Dr. Phil Winder
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5 min
Cloud Native Data Science: Strategy
These three strategic decisions will help you succeed in any Cloud Native Data Science project.
Dr. Phil Winder
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5 minLife and Death Decisions: Testing Data Science
Abstract We live in a world where decisions are being made by software. From mortgage applications to driverless vehicles, the results can be life-changing. But …
201: Basics and Terminology
Now we have a firm understanding of how business problems map to solutions we need to learn the techniques to deliver the solutions. This section introduces the basic terminology and concepts used in data science.
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