Tasks and Duties
Objective
The aim of this task is to define a clear business problem within the agribusiness domain and establish a detailed project plan using data science techniques. The student will plan an analytical project that addresses a real-world issue in agriculture such as crop yield optimization, soil health analysis, or market trend forecasting. The plan should include problem definition, methodology, expected outcomes, and contingency measures.
Expected Deliverables
- A comprehensive DOC file containing the project plan.
- An executive summary, project objectives, and timeline.
- A detailed description of methodology and anticipated data sources (using publicly available information).
Key Steps to Complete the Task
- Problem Definition: Identify a challenge in agribusiness that can be addressed using data science methods. Provide background and rationale for its importance.
- Methodology Development: Outline the analytical techniques, tools, and frameworks to address the problem. Include data acquisition strategies and potential limitations.
- Timeline and Milestones: Divide the project into phases and assign tentative durations to each phase, ensuring a realistic workload.
- Risk Assessment: Discuss potential project risks and propose mitigation strategies.
- Summary: Conclude with a brief overview of how this plan can yield actionable insights for agribusiness stakeholders.
Evaluation Criteria
Your submission will be evaluated based on clarity, depth of analysis, relevance to real-world agribusiness problems, feasibility of the plan, and overall organization of the document. Ensure that you meet the 30 to 35 hour effort guideline with thorough research and well-structured planning. Additional marks will be awarded for creativity and the inclusion of contingency plans.
Objective
This task focuses on the critical phase of data sourcing and exploratory data analysis (EDA) in the field of agribusiness. The student's objective is to simulate a real-world data exploration process by gathering publicly available data and performing initial analysis to uncover trends, patterns, and anomalies. The task emphasizes understanding data quality, structure, and potential insights that can drive further analysis.
Expected Deliverables
- A DOC file report that includes detailed documentation of your data sourcing strategy, cleaning processes, and initial analysis findings.
- Visuals such as charts or tables (embedded as screenshots or described in text) to support your findings.
Key Steps to Complete the Task
- Data Identification: Research and list publicly available data sources relevant to agribusiness, such as crop production statistics, weather data, or market trends.
- Data Acquisition Strategy: Describe the process you would use to collect data and the criteria for selecting data sources.
- Data Cleaning and Preparation: Detail your approach for handling missing values, outliers, and data normalization.
- Exploratory Analysis: Perform initial exploratory analysis to extract insights, using descriptive statistics and simple visualizations.
- Documentation: Summarize the entire process including challenges encountered and how you addressed them.
Evaluation Criteria
Submissions will be assessed on the thoroughness of data exploration, clarity in explaining the data handling process, the relevance of chosen datasets to agribusiness issues, and the ability to identify initial trends. Structured documentation and insightfulness in the discussion will contribute significantly to the evaluation.
Objective
This task requires the application of predictive modeling techniques to a simulated agribusiness dataset. The student will design and describe a predictive model tailored to address an agribusiness challenge, emphasizing the selection of relevant features and the rationale behind the chosen model. The focus is on forecasting key outcomes such as crop yield, demand, or price movements by utilizing data science methodologies.
Expected Deliverables
- A documented DOC file presenting your predictive modeling strategy and an oral rationale for the chosen methods.
- Details on feature selection, model evaluation metrics, validation techniques, and potential improvements.
Key Steps to Complete the Task
- Problem Statement: Clearly articulate a specific agribusiness problem where predictive modeling can have a significant impact.
- Model Selection: Discuss several modeling approaches (e.g., regression, decision trees, ensemble methods) and select the most appropriate one based on problem specifics.
- Feature Engineering: Describe the process of selecting and engineering features relevant to your model. Justify each selection with potential impact on prediction accuracy.
- Validation Strategy: Explain how you would validate and test your model including cross-validation techniques and performance evaluation metrics.
- Improvement Opportunities: Suggest areas for further model refinement and future research avenues.
Evaluation Criteria
Your submission will be evaluated based on the clarity of your model strategy, depth of justification behind selected techniques, logical flow of the methodology, and the innovative approach to solving an agribusiness problem with predictive analytics. Comprehensive documentation and in-depth analysis will be highly valued.
Objective
This final task integrates project evaluation and the presentation of actionable insights. The student is required to assess the performance and potential business impact of analytical models or strategies developed during the internship. Specifically, the task focuses on summarizing results, outlining strategic recommendations for agribusiness stakeholders, and discussing challenges as well as future improvement pathways.
Expected Deliverables
- A comprehensive DOC file report that captures the evaluation of your data analysis or predictive modeling outcomes.
- An impact assessment narrative, including visual aids (e.g., charts, graphs) that support your conclusions.
Key Steps to Complete the Task
- Performance Summary: Provide a detailed summary of the key findings or model performance metrics. Use appropriate evaluation techniques to measure success.
- Business Impact Analysis: Translate analytical outcomes into business implications, detailing how agribusiness operations could benefit or change.
- Strategic Recommendations: Propose data-driven recommendations for stakeholders. Include potential areas for additional research and challenges that might be encountered in real-world application.
- Future Roadmap: Outline steps for future iterations of the project to enhance impact, efficiency, or scalability.
- Critical Reflection: Reflect on the challenges faced during the internship period and detail the learnings gained.
Evaluation Criteria
The evaluation will consider the depth and practicality of your impact analysis, the clarity of the strategic recommendations, and the overall coherence of your deliverable. Emphasis will be placed on the articulation of actionable insights and the ability to critically evaluate project outcomes. Creativity, thoroughness of the narrative, and alignment with real-world agribusiness challenges are among the key marks for this submission.