Tasks and Duties
Task Objective
The goal of this task is to familiarize yourself with the process of data collection and initial analysis in the hospitality domain using Python. You will identify publicly available datasets, outline a data collection strategy, and conduct a preliminary analysis to assess data quality and relevance.
Expected Deliverables
- A DOC file containing a project plan detailing your data collection approach.
- A comprehensive initial analysis report including data sources, data cleaning strategies, and exploratory findings.
- Python code snippets (embedded as text) used for data scraping or downloading.
Key Steps to Complete the Task
- Research: Identify at least two publicly available data sources related to hospitality, such as tourism trends, hotel occupancy, or customer reviews. Document your sources with URLs and describe why they are relevant.
- Data Collection Strategy: Outline a clear plan for gathering the data. Include data format, frequency of updates, and any issues you expect to encounter.
- Initial Analysis: Use Python to perform a preliminary analysis. This may include descriptive statistics, missing value assessment, and basic visualizations using libraries such as pandas, matplotlib, or seaborn.
- Documentation: Compile all your findings, code snippets, and reflections in a well-organized DOC file.
Evaluation Criteria
The submission will be evaluated based on the clarity and structure of your plan, the thoroughness of your research and initial analysis, the correct usage of Python for data tasks, and the depth of your documentation. Your analysis should be logical, detailed, and provide insight into the challenges and opportunities of working with hospitality data.
Task Objective
This task requires you to design and document a complete data pipeline tailored for hospitality analytics. You will be expected to map out the end-to-end flow from data collection to data processing, culminating in the storage of cleaned data, ready for analysis. The task involves planning, coding outline, and a discussion of best practices in pipeline automation using Python.
Expected Deliverables
- A DOC file containing a detailed design document for the Python data pipeline.
- An explanation of each component of the pipeline (data ingestion, processing, cleaning, storage).
- Pseudocode or code outlines illustrating key parts of the pipeline.
Key Steps to Complete the Task
- Pipeline Mapping: Create a flowchart or diagram (can be described textually) detailing the stages of your data pipeline. Clearly indicate the transitions between data ingestion, processing, and storage.
- Component Description: Write a detailed explanation for each stage of your pipeline. Describe the Python libraries and methods that would be used, such as pandas for data processing, requests for data ingestion, and SQL or CSV formats for storage.
- Automation Strategies: Discuss how automation can be implemented. Highlight scheduling techniques, error handling, and logging to ensure the pipeline runs smoothly over time.
- Documentation: Compile your design, diagrams, pseudocode, and automation strategies in a DOC file, making sure it is well-organized and thorough.
Evaluation Criteria
Your project will be assessed on the clarity of your pipeline design and the depth of your component-wise breakdown. Emphasis will be on how realistically your design can be implemented using Python, attention to detail, and the overall coherence of your documentation. The DOC file should reflect a comprehensive understanding of both data pipeline design and related Python methodologies.
Task Objective
This task focuses on conducting a robust exploratory data analysis (EDA) and creating visualizations relevant to the hospitality industry using Python. You will analyze trends, patterns, and relationships in sample hospitality data (publicly available or simulated). You are required to prepare a detailed report summarizing your EDA process and insights.
Expected Deliverables
- A DOC file that includes a comprehensive EDA report.
- Visualizations (embedded as images or described with corresponding code snippets) demonstrating key trends and insights.
- Descriptions of Python methods and libraries used (e.g., pandas, matplotlib, seaborn).
Key Steps to Complete the Task
- Data Setup: Use either publicly available hospitality data or generate a simulated dataset. Provide a rationale for your choice.
- Exploratory Techniques: Perform detailed statistics and correlation analysis. Use Python to summarize central tendencies, disperse data and identify potential outliers.
- Visualization Creation: Generate various visualizations such as histograms, scatter plots, and time-series plots to illustrate relationships between different variables.
- Report Compilation: Document each step of your analysis process. Describe the code used, explain the significance of each visualization, and conclude with insights or recommendations based on your findings.
Evaluation Criteria
The evaluation will be based on the clarity and depth of your exploratory analysis, the creativity and relevance of your visualizations, and the integration of Python code and its explanations. The final report should demonstrate critical thinking, comprehensive analysis, and a detailed understanding of how to use Python for data analysis in the hospitality sector.
Task Objective
This week focuses on building and documenting a predictive model that forecasts demand or occupancy trends in the hospitality industry. You are expected to select relevant features, choose an appropriate modeling technique, and justify your choices using Python. Your task will demonstrate your capacity to translate data insights into predictive frameworks.
Expected Deliverables
- A detailed DOC file with a complete modeling plan.
- A description and justification for the selected features and algorithms.
- Pseudocode or Python code outlines describing the model training process.
Key Steps to Complete the Task
- Problem Framing: Define the forecasting problem clearly—identify which aspect of hospitality demand or occupancy you wish to predict and why it is important.
- Feature Selection: Identify a set of features (from public data or synthetically generated data) that could influence the forecast. Provide a rationale for each feature.
- Model Choice and Justification: Explain the type of model you would deploy (e.g., linear regression, time-series analysis, decision trees, etc.), including why it is well-suited for the task. Discuss the validation strategy.
- Implementation Plan: Outline the steps for data splitting, training, validating, and testing the model using Python. Include key code snippets, libraries, and parameters that will be used.
- Documentation: Assemble all sections into a DOC file, making sure to explain any assumptions and potential limitations of your approach.
Evaluation Criteria
Your submission will be reviewed based on the logical structure of your modeling approach, the relevance of feature selection, the appropriateness of the chosen predictive model, and the clarity in which the process is communicated in your DOC file. The documentation should reflect a deep understanding of predictive modeling within the hospitality context and articulate both the strengths and challenges of the approach taken.
Task Objective
The final weekly task is to evaluate and optimize the predictive model you created in Week 4, and then prepare a strategic report for stakeholders. In this task, you will detail the evaluation metrics, perform model tuning where applicable, and document your findings. The goal is to translate technical model outcomes into actionable strategic insights for decision makers in the hospitality industry.
Expected Deliverables
- A DOC file containing a detailed evaluation and optimization report.
- An explanation of evaluation metrics such as RMSE, MAE, or R-squared, and why they are relevant.
- An account of any optimization or tuning strategies employed, including code snippets or pseudocode.
Key Steps to Complete the Task
- Model Evaluation: Deploy the predictive model previously developed and evaluate its performance using relevant statistical metrics. Explain each metric and interpret what it indicates about your model's performance.
- Optimization Process: Identify areas where the model could be improved. Discuss potential hyperparameter tuning, feature engineering, or re-sampling techniques. Include any Python snippets or pseudocode that describe the optimization strategy.
- Strategic Reporting: Prepare a report section that translates your findings into business recommendations. Consider aspects like potential revenue impact, operational changes, or strategic decisions based on forecasted trends.
- Documentation: Create a well-organized DOC file that includes your evaluation process, tuning steps, results, and strategic recommendations. Ensure that your report is detailed and clearly inherited the story of your analysis and subsequent improvements.
Evaluation Criteria
Submissions will be evaluated based on the technical depth of your model evaluation and optimization steps, the clarity in which metrics and improvements are explained, and the effectiveness of your strategic insights. Your final report should reflect a seamless integration of technical analysis with business strategy, underpinned by a clear narrative that could guide real-world decision making in hospitality management.