Tasks and Duties
Task Objective
The objective of this task is to acquaint you with the overall landscape of agribusiness data and develop a foundational strategic approach to data analysis. You will identify publicly available data sources related to agribusiness, explore different types of agronomic datasets, and plan your approach to ingesting and analyzing data for future tasks.
Expected Deliverables
- A comprehensive DOC file that outlines your exploratory research findings.
- A detailed written plan on potential analysis functions (including data types, metrics, and business impact).
- A clear explanation of how your approaches could address common agribusiness challenges.
Key Steps
- Research and list at least five publicly available sources of agribusiness data.
- Review available data types such as crop yields, weather patterns, market prices, and supply chain metrics.
- Discuss how data from these sources can be utilized for decision-making processes in agribusiness.
- Write a strategic plan outlining your planned approach to data exploration and analysis in future tasks, discussing key performance indicators and metrics.
- Compile all your findings and planning steps into a DOC file.
Evaluation Criteria
- Depth of research and relevance of identified data sources.
- Clarity and practicality of the strategic planning document.
- Ability to align the approach with identifying agribusiness sector challenges.
- Overall organization, clarity, and completeness of the DOC file.
This task is designed to take around 30 to 35 hours. Ensure that your DOC file is thoroughly detailed and clearly outlines each section as mentioned above.
Task Objective
The objective for this week is to develop your skills in data cleaning and preparation. You will simulate a scenario where you have derived raw agribusiness data from various public sources, and you need to prepare this data for analysis. Focus on identifying common data quality issues and applying appropriate cleaning techniques.
Expected Deliverables
- A DOC file containing a detailed report of your data cleaning process.
- Screenshots or snippets of your data cleaning steps (pseudo-code or explanation text) within the DOC file.
- A discussion of the challenges faced during the cleaning process and strategies used to overcome them.
Key Steps
- Assume an aggregated set of raw data with inconsistencies, missing values, and formatting issues.
- Outline common issues such as duplicate entries, missing values, and outlier detection.
- Describe methods to address each of these problems including imputation techniques, normalization, and elimination of duplicates.
- Create a step-by-step plan in the DOC file documenting your cleaning process, along with the rationale for each technique chosen.
- Provide potential outcomes and any recommendations for continuous quality improvement in data preparation.
Evaluation Criteria
- Clarity and thoroughness of the data cleaning documentation.
- Depth of explanation regarding techniques and challenge resolution methods.
- The logical steps taken to ensure data integrity and readiness.
- Overall presentation of the findings in the DOC file.
This task should take approximately 30 to 35 hours of work.
Task Objective
The objective for this week is to perform exploratory data analysis (EDA) and create visualizations that highlight key trends and insights in agribusiness data. You will simulate an analysis where you focus on variables like crop yields, weather influences, and market trends. This task will help you develop skills in identifying patterns and anomalies in datasets.
Expected Deliverables
- A DOC file that presents a detailed EDA report with visualizations embedded or described.
- A discussion of trends identified and their business implications.
- Recommendations for further analysis based on your findings.
Key Steps
- Develop a simulated dataset context where you identify variables such as crop performance, weather patterns, and pricing trends.
- Outline the steps of an EDA including data summary statistics, distributions, and correlation analysis.
- Create or describe visual aids (charts, graphs, scatter plots, etc.) that would effectively communicate your insights.
- Discuss any challenges encountered in identifying meaningful trends and how you addressed them.
- Compile your report in a DOC file that details your entire analysis and visualization process.
Evaluation Criteria
- Depth of analysis: thorough use of statistical measures and visualizations.
- Clarity in linking data insights to agribusiness contexts.
- Creativity and thoughtfulness of recommendations for further work.
- Coherence, structure, and presentation of the final DOC report.
It is expected that this task will require approximately 30 to 35 hours to complete.
Task Objective
This week’s task requires you to apply statistical analysis techniques and develop a simple predictive model using hypothetical agribusiness data. The focus is on understanding the relationship between various agricultural variables and predicting outcomes such as crop yield or market demand. You are expected to design and discuss the methodology behind your predictive modeling process and its limitations.
Expected Deliverables
- A DOC file containing your full analysis report.
- An explanation of the statistical methods and predictive modeling techniques used.
- An interpretation of the outcomes and discussions on potential business implications.
Key Steps
- Select a set of key variables (for example, rainfall, temperature, and input costs) that could influence a target variable such as crop yield.
- Detail your methodology: hypothesis formation, selection of statistical tests, and clear explanation of the chosen predictive model (e.g., regression analysis).
- Discuss the underlying assumptions of your model and potential limitations.
- Provide a theoretical walkthrough of how data would be processed and analyzed within this framework.
- Summarize the findings in a DOC file with clear sections for methodology, analysis, discussion, and conclusions.
Evaluation Criteria
- Understanding and correct application of statistical methods.
- Clarity in the methodology and detailed explanation provided.
- The depth of critical discussion on model limitations and reliability.
- Quality and structure of the final DOC file and presentation of insights.
You are allocated approximately 30 to 35 hours to complete this task.
Task Objective
The final week will focus on synthesizing all insights drawn from previous tasks and developing a comprehensive report that communicates data-driven business insights relevant to the agribusiness sector. In this task, your role is to translate analytical outcomes into strategic recommendations for improving operational efficiency, market competitiveness, and overall business performance in agribusiness.
Expected Deliverables
- A complete DOC file that functions as a professional business report.
- A summary of key findings from previous analyses, including data trends, statistical insights, and predictive model outcomes.
- Strategic recommendations tailored to agribusiness improvement.
Key Steps
- Review and consolidate key outcomes from your prior tasks including data exploration, cleaning, and analysis.
- Extract business insights by linking your data analysis to real-world agribusiness challenges.
- Draft a clear and structured report that begins with an executive summary, followed by methodological overviews, key findings, and strategic recommendations.
- Ensure that the final report is detailed, segmented into clear sections with headings, and discusses both the benefits and potential pitfalls of the proposed strategies.
- Conclude by suggesting further areas for research or data-driven insights that could benefit the agribusiness industry.
Evaluation Criteria
- Integration of analysis results from prior tasks into actionable business insights.
- Clarity and logical flow of the business report.
- Feasibility and relevance of the strategic recommendations provided.
- Professional presentation and overall completeness of the DOC file.
Approximately 30 to 35 hours should be dedicated to this final stage of the internship task.