Tasks and Duties
Objective
Your task for Week 1 is to design a comprehensive data exploration and cleaning plan tailored to the agribusiness context. As a Junior Data Scientist, you will need to identify potential data challenges, sources of error, and propose appropriate cleaning techniques that could be applied to raw agribusiness data available in public domains. The goal is to conceptualize a framework that could be used to process complex datasets such as crop yields, weather conditions, and market prices.
Expected Deliverables
- A DOC file containing a detailed plan addressing data exploration objectives.
- A conceptual framework outlining data cleaning methods along with potential challenges and solutions.
- An overview of the public datasets you might reference.
Key Steps
- Research available public agribusiness datasets to understand the variety and sources of data.
- Create a step-by-step plan on how data quality issues will be identified and resolved (e.g., missing values, outliers, inconsistent data formats).
- Outline the methods and tools you plan to use (e.g., Python libraries, Excel, etc.).
- Discuss methods for preliminary data exploration such as descriptive statistics, visualizations, and correlation analysis.
- Conclude with a summary of anticipated findings and benefits of a clean dataset.
Evaluation Criteria
- Clarity and depth of the exploration and cleaning plan.
- Relevance of proposed data cleaning methods to agribusiness scenarios.
- Justification for tool and method selection.
- Quality and organization of the DOC file.
Objective
This week, you are expected to develop an initial data analysis model using basic exploratory data analysis (EDA) techniques. In this task, you will simulate a realistic scenario where you analyze publicly available agribusiness data to extract insights. The focus is on using clear statistical methods and basic visualization techniques to explain trends and patterns inherent to agribusiness performance.
Expected Deliverables
- A DOC file detailing your EDA process and findings.
- Explanation of the statistical tools used and rationale for selecting them.
- Illustrative examples of visualizations such as bar plots, histograms, or scatter plots created conceptually.
Key Steps
- Outline how you would approach the public dataset you select, identifying key variables and their potential impacts on agribusiness outputs.
- Describe the techniques for detecting trends, such as central tendency measures and dispersion statistics.
- Detail the steps to develop your visualizations and what insights each visualization is expected to reveal.
- Explain potential business decisions that can be driven by your analysis findings.
- Discuss limitations of your approach and suggest possible improvements.
Evaluation Criteria
- Depth and logical flow of the analysis plan.
- Soundness of statistical methods and chosen techniques.
- Creativity in visual explanation and result interpretation.
- Comprehensiveness and clarity in the DOC file submission.
Objective
This week’s task revolves around developing a predictive model for key agribusiness metrics such as crop yield, market price, or demand forecasting. As a Junior Data Scientist, you will need to demonstrate your ability to design and justify the selection of statistical or machine learning models using conceptual examples drawn from public data insights. The exercise focuses on the planning, selection of features, and the description of the modeling approach rather than coding.
Expected Deliverables
- A DOC file that includes an in-depth description of the predictive model including its rationale, methodology, and expected outcomes.
- A discussion on possible algorithms and why they would be suitable for the agribusiness context.
- An outline of the key variables and how they are expected to influence the model’s accuracy.
Key Steps
- Identify a prediction goal within agribusiness (e.g., forecasting crop yield based on weather patterns).
- Propose potential features (predictors) and explain how each might impact your target variable.
- Justify your choice of the modeling technique, discussing benefits and limitations.
- Outline the process for model validation and performance evaluation.
- Include a discussion on potential challenges and limitations of your proposed model and suggest mitigation strategies.
Evaluation Criteria
- Depth of conceptual understanding demonstrated in model selection.
- Clarity in explaining feature relevance and data relationships.
- Logical flow in the presentation of the predictive framework.
- Completeness and professional quality of the DOC document.
Objective
The final task combines model evaluation and actionable insights generation. In this week’s assignment, you will simulate a scenario where you report on the outcomes of a predictive model and provide recommendations based on agribusiness trends. This task is designed to assess your ability to interpret data analysis results and communicate them effectively to a non-technical audience. Your focus should be on extracting key insights, evaluating the performance of a hypothetical model, and proposing strategic recommendations for industry growth or operational efficiency.
Expected Deliverables
- A DOC file that includes an executive summary and a detailed report on the model’s outcome analysis.
- A section dedicated to performance metrics and interpretations.
- A set of actionable recommendations tailored to common agribusiness challenges.
Key Steps
- Define key performance indicators (KPIs) for model evaluation such as accuracy, recall, precision, or RMSE.
- Explain how each KPI is relevant to the agribusiness domain.
- Articulate the implications of the model performance on business strategies.
- Provide a thoughtful analysis on potential business risks and areas for improvement.
- Conclude the task with well-supported recommendations that drive strategic decision-making in agribusiness.
Evaluation Criteria
- Clarity in communication and analytical insights.
- Effectiveness of the recommendations provided.
- Depth of performance evaluation and metric explanation.
- Quality of the structured DOC submission with logical segmentation.