Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

Business Intelligence vs. Data Science
Data science as you have learned by now is an interdisciplinary approach
that applies mathematical algorithms and statistical tools to extract valuable
insights from raw data. On the other hand, Business Intelligence (BI) refers
to the application of analytical tools and technologies to gain a deeper
understanding of the current state of the company as it relates to the
company's historical performance. Simply put BI provides intelligence to
the company by analyzing their current and historical data whereas data
science is much more powerful and capable of analyzing the humongous
volume of raw data to make future predictions.
An avalanche of qualitative and quantitative data flowing in from a wide
variety of input sources has created a dependency on data science for
businesses to make sense of this data and use it to maintain and expand
their businesses. The advent of data science as the ultimate decision-making
tool goes to show the increasing data dependency for businesses. In some

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Business intelligence tasks could potentially be automated with the use of
data science-driven tools and technologies. The ability to gather insights
using these automated tools from anywhere across the world, with the use
of the Internet, will only propel the use of "centralized data repositories" for
everyday business users.
Business intelligence is traditionally used for "descriptive analysis" and
offers retrospective wisdom to the businesses. On the other hand, Data
science is much more futuristic and used for "predictive and prescriptive
analysis". As data science seeks to answer questions like "Why the event
occurred and can it happen again in the future?", Business intelligence
focuses on questions like "What happened doing the event and what can be
changed to fix it?". It is this fundamental distinction between the "Ws” that
are addressed by each of these two fields, that sets them apart.
The niche of business intelligence was once dominated by technology users
with computer science expertise, however, Data science is revamping the
Business intelligence space by allowing non-technical and core business
users to perform analytics and BI activities. Once the data has been
operationalized by the data scientists, the tools are easy to use for the
mainstream business corridor and can be easily maintained by a support
team, without needing any data science expertise. Business intelligence
experts are increasingly working hand in hand with the data scientist to
develop the best possible Data models and solutions for the businesses.
Unlike Business intelligence that is used to create data reports primarily key
performance indicators and metrics dashboards, and provide supporting
information for data management strategy, Data science is used to create
forecasts and predictions using advanced tools and statistics and provide

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supplemental information for data governance. A key difference between
Data science and business intelligence lies in the range and scale of “built-
in machine learning libraries”, which empower everyday business user to
perform semi-automated or automated data analysis activities. Think of data
science as business intelligence on steroids, that is set to turn the business
analysis world into a democracy!