A methodology for IoT analytics(Data Science for IoT) should cover the unique aspects of each step in Data Science. For example: It is more than the choice of the model family. The choice of the model family (ANN, SVM, Trees, etc) is only one of the many choices to make – Others include:
a) Choice of the model structure - optimisation methodology (CV, Bootstrap, etc)
b) Choice of the model parameter optimisation algorithm (joint gradients vs. conjugate gradients )
c) Preprocessing of the data (centring, reduction, functional reduction, log-transform, etc.)
d) How to deal with missing data (case deletion, imputation, etc.)
e) How to detect and deal with suspect data (distance-based outlier detection, density-based, etc.)
f) How to choose relevant features (filters, wrappers, embedded method ?)
g) How to measure prediction performances (mean square error, mean absolute error, misclassification rate, lift, precision/recall, etc.)
Source: Methodology and standards for data analysis with machine learning tools Damien Francois ∗
source: https://www.kdnuggets.com/2016/01/methodology-data-science-iot-analytics.html
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