Section 1.1: Core Agile Principles

Before we discuss TDSP, it’s essential to explore the foundational principles of Agile as articulated in the Agile Manifesto:

1. Prioritizing individuals and interactions over processes and tools.
2. Valuing working software over extensive documentation.
3. Emphasizing customer collaboration over contract negotiation.
4. Adapting to change rather than rigidly adhering to a plan.

These principles shape various Agile practices, such as daily stand-ups and incremental development, which are tailored to facilitate quick adjustments and deliver functional solutions.

Section 1.2: What is TDSP?

TDSP is a methodology that applies Agile principles to streamline the delivery of data science solutions. It is built around the data science lifecycle, akin to the software development lifecycle in Agile. The data science lifecycle encompasses five iterative steps:

1. Business Understanding
2. Data Acquisition and Understanding
3. Modeling
4. Deployment
5. Stakeholder/Customer Acceptance

These phases are visually represented in the workflow diagram below.

Each data science project begins with a clear definition of the business problem and requirements. This foundational understanding leads to data acquisition, which is crucial for subsequent modeling efforts. If any results are unsatisfactory or requirements change, the process allows for revisiting prior stages due to its iterative design.

Section 2.1: Business Understanding

The initial stage focuses on clarifying project requirements and identifying necessary data. This involves two key tasks:

1. Defining objectives: Collaborate with stakeholders to pinpoint the business problem.
2. Identifying data sources: Once the problem is defined, determine the data required for resolution.

These foundational tasks are critical for any successful data science initiative.

Section 2.2: Data Acquisition and Understanding

After identifying the business problem, the next logical step is to collect and analyze the data. This phase consists of three primary tasks:

1. Data ingestion: Bring data into your analytical environment, which may involve uploading files in Jupyter when working locally.
2. Data exploration: Conduct exploratory data analysis (EDA) to preprocess data and recognize patterns.
3. Establishing a data pipeline: Create a process for continuous data ingestion, which can be batch-based, real-time, or a hybrid.

While data scientists typically handle EDA, data engineers often play a crucial role in setting up the data pipeline, underscoring the importance of diverse skill sets in data science teams.

Section 2.3: Modeling

The modeling phase is often the most thrilling for data scientists and relies heavily on the successful execution of the previous stages. The quality of models directly correlates with the quality and understanding of the data. This stage involves three main activities:

1. Feature engineering: Develop data features from raw data for model training.
2. Model training: Train models using defined features and target variables, splitting the data into training, validation, and testing sets.
3. Model evaluation: After training, assess each model based on specific metrics to determine its effectiveness in solving the business problem.

This phase is inherently iterative; it’s common to revisit feature engineering and model training based on evaluation outcomes.

Section 2.4: Deployment

This stage transforms models into actionable tools that yield business insights. The primary task is to operationalize the model by integrating it with the data pipeline in a production-like environment. Potential deployment options include:

• Exposing the model via an API for other applications.
• Creating a microservice or containerized solution.
• Integrating the model within a web application featuring a results dashboard.
• Setting up a batch process that produces predictions for consumption.

Once deployment is complete, stakeholders should be able to access and utilize the model's outputs effectively.

Section 2.5: Customer Acceptance

The final step aims to validate that the data pipeline, model, and deployment meet customer expectations and effectively address the initial business challenge. This includes:

1. System validation: Ensure that the entire setup meets business needs.
2. Project hand-off: Transition the project to the team responsible for its ongoing management.

This iterative process allows for adjustments if any issues arise during validation.