Tasks and Duties
Overview
This task focuses on defining a clear agribusiness challenge that can be addressed using data science techniques. You will develop a comprehensive strategy document to outline your understanding of the challenge, its implications, and a strategic action plan based on publicly available information. The final deliverable must be submitted as a DOC file.
Task Objectives
- Identify a relevant and pressing agribusiness challenge suitable for a data science approach.
- Develop a detailed strategic plan for addressing the identified challenge.
- Provide a thorough rationale for your chosen challenge with evidence from public references.
Expected Deliverables
- A DOC file containing a complete strategy document.
- The document must include sections for introduction, problem identification, business impact analysis, proposed data science interventions, and expected outcomes.
Key Steps
- Research: Spend time researching current challenges in agriculture and agribusiness, utilizing publicly available articles, publications, and databases.
- Define the Challenge: Clearly articulate the chosen challenge, including its context and importance within the agribusiness sector.
- Strategic Planning: Draft your strategy document, ensuring it includes a problem statement, objectives, methodology, resources needed, and risks involved.
- Documentation: Format your findings into a structured DOC file with appropriate headings and subheadings.
Evaluation Criteria
- Clarity in defining the problem and relevance to agribusiness.
- Logical and evidence-based strategic plan.
- Structure, readability, and thoroughness of the DOC file.
- Proper citation of publicly available resources.
This task is designed to take approximately 30 to 35 hours. Ensure that your final submission is well-organized and thoroughly analyzes both the challenges and the strategic data science solutions proposed.
Overview
This week's task emphasizes planning and outlining a robust framework for exploratory data analysis and identifying key features that could drive agribusiness insights. Your goal is to produce a DOC file that describes in detail the process you would undertake if provided with a dataset. You will be expected to define a clear EDA plan along with a prospective feature engineering strategy using publicly available data as a reference.
Task Objectives
- Establish a plan for performing exploratory data analysis in an agribusiness context.
- Identify potential features that can be derived from available public datasets.
- Outline methodologies for data cleaning, transformation, and visualization.
Expected Deliverables
- A DOC file that includes an EDA blueprint and feature engineering strategy document.
- The document should contain detailed sections such as introduction, EDA methodology, feature identification rationale, prospective analytical techniques, and anticipated challenges.
Key Steps
- Conceptual Research: Investigate public datasets relevant to agribusiness; understand common variables and attributes.
- EDA Framework: Create an outline of steps for data validation, cleaning, visualization, and initial descriptive analytics.
- Feature Engineering: List potential features and describe the rationale behind their selection including any transformation steps.
- Documentation: Compile the plan in a DOC file with clear sectioning, tables, and bullet points to enhance readability.
Evaluation Criteria
- Completeness and clarity of the EDA and feature engineering blueprint.
- Innovative approach and diligence in planning.
- Quality of insights proposed based on publicly available information.
- Adherence to the document structure and detail.
You are expected to invest about 30 to 35 hours into formulating and refining your document. This exercise will prepare you for real-world data handling and pre-processing challenges.
Overview
This week, you will focus on developing a detailed proposal for data modeling and simulation techniques tailored for the agribusiness domain. The DOC file you produce should detail the process of selecting and validating appropriate models, taking into account both traditional statistical methods and modern machine learning approaches. Your submission must include a thorough discussion of preprocessing steps, modeling techniques, and validation strategies that are applicable to solving an agribusiness problem using a data science perspective.
Task Objectives
- Design a conceptual framework for data modeling in an agribusiness context.
- Identify algorithms and simulation techniques suitable for relevant challenges.
- Explain data preprocessing, feature selection, and model validation strategies.
Expected Deliverables
- A DOC file containing a comprehensive proposal outlining the modeling process.
- The document must include sections on introduction, data preprocessing plan, modeling strategy (covering both statistical and machine learning models), simulation details, and validation criteria.
Key Steps
- Research Methodologies: Investigate various data modeling and simulation techniques relevant to agriculture using publicly available literature.
- Define the Approach: Draft a clear outline on how you would implement the model, including preprocessing, selection of algorithms, and evaluation metrics.
- Simulation Planning: Propose a simulation setup that can help in stress testing and validating your model under various scenarios.
- Documentation: Ensure the document is detailed, logically structured, and formatted as a DOC file with proper HTML headings and lists converted to a DOC equivalent.
Evaluation Criteria
- Depth and clarity in the proposed modeling and simulation process.
- Realism and feasibility of the plan in an agribusiness context.
- Practicality of the data preprocessing and validation techniques proposed.
- Document structure, detail, and adherence to guidelines.
This detailed task is designed to engage you for 30 to 35 hours of focused work, challenging you to integrate both conceptual understanding and practical planning for data modeling in the agribusiness field.
Overview
The final week revolves around the post-modeling phase, where you will develop a comprehensive plan for evaluating the impact of your data science interventions and creating a reporting dashboard. Your DOC file submission should outline how to measure the effectiveness of the implemented models, propose key performance indicators (KPIs), and design a mock dashboard for presenting insights to stakeholders in the agribusiness sector. The goal is to provide a holistic evaluation and reporting strategy that encapsulates the entire data analysis workflow.
Task Objectives
- Design an impact evaluation framework that connects data science outcomes to agribusiness success metrics.
- Identify and define KPIs relevant to measuring model performance and business impact.
- Propose a detailed dashboard layout including visualization types and reporting frequency.
Expected Deliverables
- A DOC file that serves as an impact evaluation and dashboard reporting blueprint.
- The document should include sections on an introduction, evaluation methodologies, KPI definitions, dashboard design mock-ups, and a discussion on the expected business impact.
Key Steps
- Evaluation Strategy: Begin by summarizing methods for evaluating model performance and translating it into business insights using publicly available resources.
- KPI Identification: Define relevant KPIs and explain the rationale behind their selection for an agribusiness environment.
- Dashboard Planning: Outline a visual dashboard concept that includes layout design, types of visualizations (e.g., bar charts, line graphs, heat maps), and interactivity features.
- Documentation: Write your findings in a well-structured DOC file ensuring clarity through headings, bullet points, and diagrams where applicable (diagrams can be described in text form).
Evaluation Criteria
- Comprehensiveness and clarity of the impact evaluation framework.
- Innovative and realistic dashboard design tailored to agribusiness needs.
- Alignment between KPIs proposed and the overall business and model performance.
- Overall structure, readability, and thoroughness of the DOC submission.
This task will require approximately 30 to 35 hours of work, allowing you to synthesize your skills in evaluation, reporting, and dashboard design while addressing key challenges in the agribusiness sector through a data science perspective.