13P-Data-Visualization-Projects-with-Python
| Aspect | Project | EDA (Exploratory Data Analysis) | Capstone Project | Research Project |
|---|---|---|---|---|
| Definition | A task or problem requiring planning, execution, and completion, often involving data analysis or development. | The initial step in data analysis where data sets are summarized, visualized, and understood. | A comprehensive, culminating project that integrates and applies what has been learned in a course or program to solve a real-world problem. | An investigation conducted to discover new knowledge, validate existing theories, or test hypotheses using structured methods. |
| Purpose | To achieve specific goals such as developing a model, analyzing data, or building an application. | To understand underlying patterns, spot anomalies, and check assumptions in data to inform further analysis. | To demonstrate practical application of learned skills and knowledge in solving a real-world problem. | To contribute new knowledge, insights, or methods to a specific field or domain. |
| Scope | Variable; can range from focused tasks to comprehensive projects encompassing multiple stages. | Focused on data understanding, preparation, and initial insights before deeper analysis or modeling. | Broad, integrating multiple aspects of the learned curriculum and often interdisciplinary. | Focuses on a specific research question or hypothesis, structured around academic or scientific inquiry. |
| Components | Can include planning, data collection, EDA, modeling, evaluation, and reporting or deploying a solution. | Data cleaning, summary statistics, visualization, initial insights, and preparing data for modeling. | Typically includes problem statement, literature review, methodology, data collection, analysis, conclusions, and often a presentation or prototype. | Involves literature review, research design, data collection, analysis, interpretation, and dissemination of findings through academic publications or presentations. |
| Duration | Variable; can be short-term (a few days) to long-term (several months), depending on project complexity. | Short-term, typically part of a larger project, lasting from a few days to a few weeks. | Long-term, often spanning a semester or a significant portion of a course or program. | Long-term, often conducted over months or years, depending on the depth of research and resources available. |
| Outcome | Deliverables such as a report, model, dashboard, application, or other solutions tailored to project goals. | Insights into data quality, patterns, and relationships, guiding subsequent steps in analysis or decision-making. | A comprehensive report, presentation, or prototype showcasing the solution to the problem, often with practical implications. | New knowledge, insights, or theories published or presented in academic forums, contributing to the field’s understanding. |
| Evaluation Criteria | Assessed on how well the project meets its objectives, quality of work, effectiveness of the solution, and sometimes deployment considerations. | Assessed on thoroughness of data exploration, quality of insights, and clarity of visualization to inform decision-making. | Assessed on problem-solving skills, application of knowledge, depth of analysis, and clarity of presentation or prototype. | Assessed on research rigor, contribution to knowledge, methodology, interpretation of findings, and impact on the field. |
| Collaboration | Can be individual or team-based, depending on project requirements and complexity. | Typically an individual task but can involve team input for brainstorming and validation. | Often done individually or in teams; collaboration may vary based on program requirements. | Collaboration can involve research teams, advisors, or collaborators in academia or industry, depending on the project’s scope and funding. |
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