Please explain the importance of :
1. DOE - Design Of Experiment
2. Statistical Testing Process Document
A.H.M. Fuad Azam
Problem Definition: Clearly define the problem statement and objectives of the project. This involves understanding stakeholder needs, defining success criteria, and framing the business problem in a way that can be addressed using data. Data Collection: Gather relevant data sources necessary for the analysis. This could involve data from internal databases, external sources, APIs, or other data providers. Ensure data quality, completeness, and relevance to the problem at hand. Data Cleaning and Preprocessing: Clean the data to handle missing values, outliers, duplicates, and inconsistencies. Preprocess the data to transform it into a format suitable for analysis, including feature engineering, normalization, and scaling. Exploratory Data Analysis (EDA): Explore the data to gain insights, understand patterns, correlations, and relationships between variables. Visualize the data using charts, graphs, and statistical summaries to identify trends and anomalies. Feature Selection and Engineering: Select relevant features that contribute most to the predictive power of the model. Engineer new features if necessary to enhance model performance. Model Development: Select appropriate machine learning or statistical models based on the problem type (e.g., classification, regression, clustering). Train and evaluate the models using appropriate techniques such as cross-validation, hyperparameter tuning, and model selection. Model Evaluation: Evaluate the performance of the models using appropriate evaluation metrics, considering factors such as accuracy, precision, recall, F1-score, or others depending on the problem domain. Model Deployment: Deploy the trained model into production or implement it into business processes to make predictions or generate insights. This may involve integrating the model into existing systems or developing APIs for real-time predictions. Monitoring and Maintenance: Continuously monitor the performance of the deployed model in production. Update the model periodically with new data and retrain if necessary to ensure it remains accurate and relevant over time. Documentation and Reporting: Document the entire process, including data sources, methodologies, assumptions, and decisions made throughout the project lifecycle. Prepare reports or presentations to communicate findings, insights, and recommendations to stakeholders. Feedback and Iteration: Gather feedback from stakeholders and end-users, iterate on the model or analysis based on feedback, and refine the solution to address any emerging issues or changing requirements.
After formulating a hypothesis based on a problem statement in a Data Science (DS) project, the subsequent steps typically follow a structured project lifecycle. While different organizations or teams may have variations, here are the common steps constructed within a DS project lifecycle:
By following these steps within the DS project lifecycle, teams can effectively tackle data-driven problems, derive actionable insights, and deliver value to stakeholders.
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