Standardize our software development and data science lifecycles

[praeducer]

Once our working model is decided, we'll implement it in our tools like here on Github: https://github.com/trendscenter/coinstac-leadership-architecture-wiki/issues/5

[praeducer]

Here is a good run down of SDLC models we could adopt as standards, as well as common definitions.

Here, are prime reasons why SDLC is important for developing a software system:

• It offers a basis for project planning, scheduling, and estimating
• Provides a framework for a standard set of activities and deliverables
• It is a mechanism for project tracking and control
• Increases visibility of project planning to all involved stakeholders of the development process
• Increased and enhance development speed
• Improved client relations
• Helps you to decrease project risk and project management plan overhead

The entire SDLC process divided into the following SDLC steps:

1. Phase 1: Requirement collection and analysis
2. Phase 2: Feasibility study
3. Phase 3: Design
4. Phase 4: Coding
5. Phase 5: Testing
6. Phase 6: Installation/Deployment
7. Phase 7: Maintenance

[praeducer]

[praeducer]

I highly recommend we adopt something similar to this for our data science life cycle: The Team Data Science Process (TDSP).

The TDSP lifecycle is composed of five major stages that are executed iteratively. These stages include:

1. Business understanding
2. Data acquisition and understanding
3. Modeling
4. Deployment
5. Customer acceptance

The TDSP lifecycle is modeled as a sequence of iterated steps that provide guidance on the tasks needed to use predictive models. You deploy the predictive models in the production environment that you plan to use to build the intelligent applications. The goal of this process lifecycle is to continue to move a data-science project toward a clear engagement end point. Data science is an exercise in research and discovery. The ability to communicate tasks to your team and your customers by using a well-defined set of artifacts that employ standardized templates helps to avoid misunderstandings. Using these templates also increases the chance of the successful completion of a complex data-science project.

[praeducer]

Need to figure out our multi-stage deployment environments!

[praeducer]

Implementing a version of these lifecycles here: Build out a six-week plan to address important user experience improvements