Tasks and Duties
Task Objective
The objective of this task is to simulate a real-world data acquisition and cleaning process in the agribusiness sector. As a junior data analyst, you will be responsible for gathering publicly available data related to agribusiness trends, cleaning the collected data, and performing a preliminary analysis to identify key patterns and anomalies.
Expected Deliverables
- A DOC file containing a detailed report of your process and findings.
- A clear documentation of the data cleaning steps including handling missing values, correcting inconsistencies, and normalizing the datasets.
- A preliminary analysis summary that outlines key patterns, trends, and potential areas of further investigation.
Key Steps
- Identify and select two publicly available datasets that provide relevant information on agribusiness (for example, crop production, sales data, or weather patterns).
- Conduct an initial review of the datasets and document any issues such as missing values or inconsistencies.
- Perform the data cleaning process, documenting each step in detail.
- Generate preliminary summaries such as mean, median, mode, and visualizations such as histograms or box plots.
- Compile all of the above in a DOC file that also includes reflections on the data quality and potential next steps for a more in-depth analysis.
Evaluation Criteria
Your submission will be evaluated based on the clarity of your documentation, the thoroughness of your cleaning process, the insights drawn from the preliminary analysis, and the overall presentation and organization of the DOC file. The report should be thorough and clearly convey your rationale, methods, and results. Ensure that your DOC file submission is well organized, self-contained, and reflects at least 30-35 hours of work.
Task Objective
This task is aimed at developing your skills in exploratory data analysis (EDA) and visualization within the context of agribusiness. You will further analyze the cleaned data from Week 1 or a new set of publicly available data. The goal is to uncover trends, distributions, and relationships among key variables that influence agricultural production and sales.
Expected Deliverables
- A comprehensive DOC file that includes your EDA process, visualizations, and interpretation of the results.
- A series of charts and graphs (e.g., scatter plots, bar charts, trend lines) created using publicly available tools or software.
- Insights and recommendations based on the analysis that identifies potential areas for further investigation.
Key Steps
- Select an appropriate dataset related to agribusiness, ensuring it includes multiple variables suitable for analysis.
- Perform an in-depth exploratory analysis to reveal statistical properties and significant relationships between the variables.
- Create visual representations of your analysis using charts and graphs. Each visualization should be accompanied by descriptive captions and insights.
- Document your analysis process, detailing the methods used and any challenges encountered.
- Compile your findings, visualizations, and further recommendations into a DOC file.
Evaluation Criteria
Submissions will be reviewed based on the clarity of the exploratory analysis, the accuracy and clarity of visualizations, the depth of insights drawn, and the overall quality and organization of your report. Ensure the DOC file is comprehensive, well-structured, and evidences at least 30-35 hours of analytical and documentation effort.
Task Objective
This week, you are tasked with building a basic predictive model to forecast an important metric in the agribusiness sector (e.g., crop yield, commodity price, or sales volume). You will use publicly available data to construct, validate, and interpret a simple forecast model, demonstrating how data-driven insights can inform agribusiness decision-making.
Expected Deliverables
- A DOC file containing a detailed report of your modeling approach.
- A summary of data preparation steps, the methodology for model selection, and model performance assessments.
- Graphs or tables that visualize predictions versus actual data, along with a discussion of potential improvements.
Key Steps
- Identify a publicly available dataset that features time series or relevant predictors like weather or market trends in agribusiness.
- Prepare data for modeling by performing necessary cleaning and feature engineering.
- Select a simple predictive modeling technique (such as linear regression) and justify your choice.
- Split the data into training and testing sets, build the model, and validate its performance.
- Discuss your model's strengths and weaknesses, and propose recommendations or improvements based on your findings.
- Document every step meticulously in your DOC file submission.
Evaluation Criteria
You will be evaluated on the transparency and completeness of your modeling process, the rationale behind your analytical decisions, model accuracy and performance, and the overall clarity in your report. Your submission should clearly reflect an intermediate understanding of predictive analytics and must document at least 30-35 hours of focused work.
Task Objective
The final task requires you to compile all previous work, including data cleaning, exploratory analysis, and predictive modeling, into a comprehensive strategic report. This report should be aimed at decision-makers in the agribusiness sector and provide actionable insights and recommendations. The focus is on effective communication of your data findings and strategic planning based on the analyses conducted in the previous weeks.
Expected Deliverables
- A well-organized DOC file that serves as a strategic report.
- Sections covering an executive summary, methodology, key findings, visualizations, and actionable recommendations.
- A reflective section on challenges encountered and potential next steps in further data analysis.
Key Steps
- Synthesize your work from previous tasks and organize it into a cohesive report.
- Write an executive summary that provides a high-level overview of your findings and their implications.
- Include detailed sections that describe your data collection, cleaning, analysis, and modeling processes.
- Create clear visualizations to support your findings, properly labeled and explained.
- Develop a final section that provides strategic recommendations for improving operational or strategic decisions in agribusiness based on your analyses.
- Ensure that your report is comprehensive, self-contained, and reflective of at least 30-35 hours of work.
Evaluation Criteria
The report will be evaluated on the organization, clarity, comprehensiveness, and strategic depth of your submission. Special emphasis will be placed on the quality of your recommendations and the overall presentation in your DOC file. Your document should be self-contained, provide coherent arguments linking analyses to recommended actions, and be suitable for review by a non-technical decision-maker.