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Data scientists often employ various analytic frameworks depending on the specific problem they are trying to solve or the type of analysis they are conducting. Here are three commonly used frameworks in the field of data science:
1.)CRISP-DM (Cross-Industry Standard Process for Data Mining): CRISP-DM is a widely adopted framework that provides a structured approach for data mining and analytics projects. It consists of six phases:
a. Business Understanding: Understand the project objectives, requirements, and constraints from a business perspective.
b. Data Understanding: Explore and familiarize yourself with the available data, identify data quality issues, and determine data relevance.
c. Data Preparation: Select, clean, integrate, and transform the data to make it suitable for analysis.
d. Modeling: Select and apply appropriate modeling techniques, such as machine learning algorithms, to build predictive or descriptive models.
e. Evaluation: Evaluate the models’ performance and assess their effectiveness in meeting the project objectives.
f. Deployment: Deploy the models into production systems and integrate them into business processes. Monitor and maintain the models over time.
2.)OSEMN (Obtain, Scrub, Explore, Model, Interpret): OSEMN is a data science framework popularized by the book “Python for Data Analysis” by Wes McKinney. It provides a sequential approach for data analysis projects:
a. Obtain: Gather the necessary data from various sources, including databases, APIs, or files.
b. Scrub: Clean and preprocess the data, handle missing values, deal with outliers, and ensure data quality.
c. Explore: Perform exploratory data analysis (EDA) to understand the data, identify patterns, correlations, and gain insights.
d. Model: Build predictive or descriptive models using machine learning or statistical techniques, depending on the project goals.
e. Interpret: Interpret the model results, evaluate the model’s performance, and derive meaningful insights and conclusions.
3.) TDSP (Team Data Science Process): TDSP is a framework developed by Microsoft for collaborative data science projects. It emphasizes collaboration, reproducibility, and iterative development. The framework comprises the following stages:
a. Business Understanding: Understand the business problem and define the project objectives.
b. Data Acquisition and Understanding: Obtain and explore the data, identify data quality issues, and gain a deeper understanding of the data.
c. Modeling: Develop and refine models using various algorithms and techniques. Evaluate and tune the models for better performance.
d. Deployment: Deploy the models into production systems, create APIs or interfaces, and integrate them into the business environment.
e. Customer Acceptance: Validate the deployed solution with stakeholders and users. Collect feedback and make necessary improvements.
f. Operations: Monitor and maintain the deployed solution, track performance, and make updates or enhancements as required.
These frameworks provide a structured approach to data science projects, ensuring that data scientists follow a systematic and organized process from problem definition to deployment. However, it’s important to note that the choice of framework may vary depending on the organization, project requirements, and the preferences of the data science team.