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What is Data Science?

What is Data Science?
How is it different from Business Intelligence (BI) and Data Analysis?
The lifecycle of Data Science with the help of a use case.
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Data science

by deborasumopayroll In reply to What is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years?
A Data Analyst usually explains what is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.
So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.
Business Intelligence (BI) vs. Data Science
BI basically analyzes the previous data to find hindsight and insight to describe the business trends. BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer the questions like quarterly revenue analysis or business problems. BI can evaluate the impact of certain events in the near future.
Data Science is a more forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting the future outcomes with the aim of making informed decisions. It answers the open-ended questions as to “what” and “how” events occur.
Lifecycle of Data Science
Phase 1—Discovery
Phase 2—Data preparation
Phase 3—Model planning
Phase 4—Model building
Phase 5—Operationalize
Phase 6—Communicate results

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