[M1U1] Introducción a las metodologías para proyectos en ciencias de datos

Exploring Data Science Project Methodologies

General Methodologies in Data Science

• The video introduces various methodologies used in data science projects, highlighting CRISP-DM as one of the most utilized frameworks.

• The primary goal of these methodologies is to structure work, avoid common errors, and maximize the value of results.

• Other methodologies mentioned include KDD and SEMMA, with SEMMA focusing on technical aspects while CRISP-DM offers a broader integration with business objectives.

Understanding CRISP-DM

• CRISP-DM stands for Cross Industry Standard Process for Data Mining and consists of six iterative phases guiding the entire data mining project lifecycle.

1. Business Understanding

• The first phase emphasizes defining the business problem or opportunity clearly; understanding what the company or client aims to achieve is crucial.

• An example provided involves a telecommunications company aiming to reduce customer churn by understanding why customers are leaving.

2. Data Understanding

• After defining the problem, the next step is collecting and exploring relevant data to assess its quality and availability for addressing the issue at hand.

3. Data Preparation

• This phase involves cleaning data by handling incorrect or missing values, transforming variables, and selecting important features for modeling.

4. Modeling

• In this phase, appropriate analytical techniques are selected to build predictive models based on defined objectives; examples include logistic regression and decision trees.

5. Evaluation

• Once models are built, they must be evaluated against business objectives to ensure their utility and accuracy; adjustments may be necessary if models do not meet precision standards.

6. Deployment

• The final phase involves implementing the model into production which could mean generating reports or integrating it into software systems for decision-making support.

Conclusion: Importance of Business Understanding

• The video concludes by stressing that effective business understanding can significantly influence model effectiveness; viewers are encouraged to reflect on past projects where these concepts could apply.