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Data Science versus Machine Learning by Vincent Granville Jan 2, 2017

by Mountain Computers Inc., Publication Date: Thursday, February 28, 2019

View Count: 1020, Keywords: Data Science versus Machine Learning, Hashtags: #DataScienceversusMachineLearning

I find this article below immensely interesting and satisfying in how we do what we do with computers and human capital. For me and my vision, mission and goals; while everyone has an idea about how computing helps us, only a few of us keeps things less complicated in order to help society rather than confuse it more.
more to come.. enjoy!
source: https://bit.ly/2lG3Bri
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Data Science versus Machine Learning

Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters. This encompasses many techniques such as regression, naive Bayes or supervised clustering. But not all techniques fit in this category. For instance, unsupervised clustering - a statistical and data science technique - aims at detecting clusters and cluster structures without any a-priori knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit in this category.

Data science is much more than machine learning though. Data, in data science, may or may not come from a machine or mechanical process (survey data could be manually collected, clinical trials involve a specific type of small data)  and it might have nothing to do with learning as I have just discussed. But the main difference is the fact that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. In particular, data science also covers

Of course, in many organisations, data scientists focus on only one part of this process. To read about some of my original contributions to data science, click here.

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