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management.
Making business value from machine learning methods
A discussion of data science in business would be incomplete without a description of the popular
machine learning methods being used to generate business value, as described in this list:
Linear regression: You can use linear regression to make predictions for sales forecasts,
pricing optimization, marketing optimization, and financial risk assessment.
Logistic regression: Use logistic regression to predict customer churn, to predict responseversus-ad
spending, to predict the lifetime value of a customer, and to monitor how business
decisions affect predicted churn rates.
Naïve Bayes: If you want to build a spam detector, analyze customer sentiment, or
automatically categorize products, customers, or competitors, you can do that using a Naïve
Bayes classifier.
K-means clustering: K-means clustering is useful for cost modeling and customer
segmentation (for marketing optimization purposes).
Hierarchical clustering: If you want to model business processes, or to segment customers
based on survey responses, hierarchical clustering will probably come in handy.
k-nearest neighbor classification: k-nearest neighbor is a type of instance-based learning.
You can use it for text document classification, financial distress prediction modeling, and
competitor analysis and classification.
Principal component analysis: Principal component analysis is a dimensionality reduction
method that you can use for detecting fraud, for speech recognition, and for spam detection.
If you want to know more about how these machine learning algorithms work, keep
reading! They’re explained in detail in Part 2 of this book.
Differentiating between Business Intelligence
and Business-Centric Data Science
The similarities between BI and business-centric data science are glaringly obvious; it’s the
differences that most people have a hard time discerning. The purpose of both BI and businesscentric
data science is to convert raw data into actionable insights that managers and leaders can
use for support when making business decisions.
BI and business-centric data science differ with respect to approach. Although BI can use
forward-looking methods like forecasting, these methods are generated by making simple
inferences from historical or current data. In this way, BI extrapolates from the past and present to