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As data analytics becomes more accessible and prevalent, data scientists need a core methodology that can
provide a guiding strategy, regardless of the technology, volume of relevant data, or approach. his methodology
emphasises many of the ne approaches in data science. It consists of steps that form an iterative process using
data to discover information. ach step plays an important role in the conte t of the overall methodology.
usiness Analytic
understanding approach
Data
eedback
requirements
Data
Deployment
collection
Data
valuation
understanding
Data
odelling
preparation
S tag e 1 : B usiness U nder standing
very pro ect starts ith a business understanding. usiness sponsors, ho need analytical solutions, play the most
important role at this time in defining the problem, pro ect ob ectives, and solution requirements from a business
perspective. his first step lays the foundation for successful business problem solving and is perhaps the hardest. o
help ensure pro ect success, sponsors should be involved throughout the pro ect to provide e pert kno ledge, revie
interim conclusions and ensure that ork remains on track to produce the intended solution.
S tag e 2 : A nalytic A ppr oach
Once the business problem is clearly stated, the data scientist can define an analytical approach to solving the problem.
his step is to represent a problem in the conte t of statistical techniques and machine learning so that the organisation
can determine the most appropriate for the desired outcome.
or e ample,
• If the goal is to predict an ans er such as yes or no , then the analytical method can be defined as building,
testing, and e ecution of a classification model.
• If the goal is to determine the probability of action, then predictive modelling can be used.
• If the goal is to sho relationships, a descriptive approach may be necessary.
S tag e 3 : Data R eq uir ements
he analytical approach chosen characterises the requirements for the data. In particular, the analytical methods used
require some content, format, and initial data collection.
S tag e 4 : Data C ollection
During the initial data collection phase, data scientists identify available data sources structured, unstructured, and
semi structured) relevant to the problem area. If there is a gap in data collection, the data scientist may need to modify
data requirements accordingly and collect ne and or more data. oday's high performance database analytics enable
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