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End to End Statistics for Data Science

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction to Statistics Statistics is a type of mathematical analysis that employs quantified models and representations to analyse a set of experimental data or real-world studies. Data processing is […].

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Robust Experimentation and Testing | Reasons for Failure!

Occam's Razor

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice. Wah wah wah waaah.

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Do You Need a DataOps Dojo?

DataKitchen

A centralized team can publish a set of software services that support the rollout of Agile/DataOps. Develop/execute regression testing . Test data management and other functions provided ‘as a service’ . With a standard metric supported by a centralized technical team, the organization maintains consistency in analytics.

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eCommerce Brands Use Data Analytics for Conversion Rate Optimization

Smart Data Collective

Several organizations and research firms publish e-commerce conversion rate benchmarks based on industry data and trends. Whether you’re optimizing headlines, button colors, product descriptions, or layouts, testing different versions can yield decisive data-driven decisions.

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Methods of Study Design – Experiments

Data Science 101

Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Suppose we want to test the effectiveness of a new drug against a particular disease. Bias can cause a huge error in experimentation results so we need to avoid them. We randomly recruit subjects for that.

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Drug Discovery Needs AI To Discover More Treatments

Smart Data Collective

Phase 0 is the first to involve human testing. Phase I involves dialing-in the proper dosage and further testing in a larger patient pool. To bring costs down and encourage further experimentation, artificial intelligence can study hundreds or thousands of patient records in search of the biomarkers the drug intends to target.

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Adopting the 4 Step Data Science Lifecycle for Data Science Projects

Domino Data Lab

Develop: includes accessing and preparing data and algorithms, researching and development of models and experimentation. Deploy: includes validating, publishing and delivering working models into a business environment. The Deploy phase is where the tested model is transferred to a production environment.