Data Science Methodolgy

8/19/2019 Data Science Methodolgy

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© 2015 IBM Corporation

Foundational Data Science Methodology

John B. Rollins, Ph.D.IBM Analytics | IBM Corporation


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© 2015 IBM Corporation2

Introduction

! Why we are interested in data science

- Solve problems and answer questions

- Gain useful insights through modeling to predict outcomes or discover

underlying patterns

! Rapidly evolving technologies

- Platform growth

- In-database analytics

- Text analysis

- Automation


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Data science methodology

! Why?

- To provide a guiding strategy

! What?

- General strategy that guides the processes and activities within a given

domain

- Does not depend on particular technologies or tools

- Not a set of techniques or recipes

- Provides the data scientist with a framework for how to proceed to obtain

answers


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Methodology diagram

BusinessUnderstanding

Data

Understanding

DataPreparation

AnalyticApproach

DataRequirements

Data Collection

Modeling

Evaluation

Deployment

Feedback


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Business understanding

! Every project begins with business understanding.

- Clearly define project objectives and requirements from the business

perspective… key to a successful solution

- Business sponsors most critical in this stage

• Define problem and solution requirements

- Business sponsors involved throughout the project

• Provide domain expertise

• Review intermediate findings

• Ensure that the work generates the intended solution

BusinessUnderstanding


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Analytic approach

! With a clear definition of the business problem, we define the analytic

approach to solving the problem.

- Express problem in context of statistical and machine learning techniques

- Identify suitable technique(s)

- Examples

• Classification to predict response to a promotion ("yes" or "no“)

• Clustering and Associations for customer segmentation and market basket

analysis

AnalyticApproach


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Data

Understanding

DataRequirements

Data Collection

Data compilation

! The chosen analytic approach determines the

data requirements.

- Content, formats, representations

! Initial data collection is performed.

- Available data resources (structured, unstructured,semi-structured) relevant to the problem domain

- Decide whether to obtain less-accessible data

elements

- Revise data requirements or collect more data,

if needed

! Then data understanding is gained.

- Descriptive statistics and visualization

- Content, quality, initial insights about data

- Additional data collection to fill gaps, if needed


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Data preparation

! Data preparation encompasses all activities to construct the data set.

- Data cleaning

• Missing or invalid values

• Eliminating duplicate rows

•

Formatting properly

- Combining multiple data sources

- Transforming data

- Feature engineering

- Text analysis

! Accelerate data preparation by

automating common stepsData

Preparation


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Modeling

Modeling

! Modeling focuses on developing models.

- Predictive or descriptive models

- According to the previously-defined analytic approach

- Training set for predictive modeling

! Highly iterative process

- Intermediate insights " refinements in data preparation & model specification

- Multiple algorithms & parameters to find best model for a given technique


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Model evaluation

! Model evaluation is performed during model development and before

model deployment.

- Understand the model’s quality

- Ensure that it properly addresses the business problem

! Diagnostic measures

- Suitable to the modeling technique used

- Testing set

- Refine model as needed

! Statistical significance tests

Evaluation


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Deployment and feedback

! Once finalized, the model is deployed into a production environment.

- May be in a limited / test environment until model is proven

- Involves additional groups, skills, and technologies

• Solution owner

•

Marketing

•

Application developers

• IT administration

!

Feedback to assess model performance- Gathering and analysis of feedback for assessment

of the model’s performance and impact

- Iterative process for model refinement and redeployment

- Accelerate through automated processes

Deployment

Feedback


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Ongoing value through good methodology

! Methodology diagram illustrates the iterative nature of problem-solving in

a data science project.

! Through feedback, refinement, and redeployment, models are continually

improved and adapted to evolving conditions.

! The model continues to provide value to the organization for as long as

the solution is needed.

Documents

Data Science Methodolgy