[ CourseWikia ] Udemy - Data Science Methodology

[ CourseWikia.com ] Data Science Methodology

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Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz
Language: English | VTT | Size: 2.34 GB | Duration: 4h 20m
Understand steps and tasks needed for designing and building a Data Driven AI engagement
What you'll learn
They will be articulate data science process and methodology – BI vs AI differences
They will understand how to analyze data sources and enhancements needed
They will explore how users and experts will be engaged for model measurement and monitoring
Finally they will understand the controls and governance aspects

Description
Data Science grew through our experiences with Business Intelligence or BI, a field that became popular in 1990s. However, the last 20 years have seen unprecedented improvement in our ability to take actions using Artificial Intelligence. As we adopt the BI methodologies to AI deployments, how will these methodologies morph to add considerations needed for model deployment, and machine learning.

Today’s Data Science work deals with big data. It introduces three major challenges:

How to deal with large volumes of data. Data understanding and data preparation must deal with large scale observations about the population. In the world of BI on small samples, the art of data science was to find averages and trends using a sample and then projecting it using universal population measures such as census to project to the overall population. Most of the big data provides significant samples where such a projection may not be needed. However, bias and outliers become the real issues

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