Tasks and Duties
Task Objective
The goal of this task is to introduce you to the process of data acquisition and preliminary analysis in the context of hospitality industry data, using Python. You will simulate collecting publicly available data that reflects various elements of the hospitality sector, such as hotel reviews, occupancy rates, or guest feedback from online sources. This task is designed to build a foundation in handling real-world data scenarios and applying basic data science principles.
Expected Deliverables
- A DOC file report detailing your methodology, findings, and initial insights.
- All Python code snippets used in analysis (embedded as text for discussion purposes in the DOC file).
- A clearly structured narrative explaining your approach to data collection, cleaning, and preliminary statistical analysis.
Key Steps to Complete the Task
- Identify publicly available data sources relevant to the hospitality industry.
- Simulate a data extraction process using Python libraries such as requests, BeautifulSoup, or API consumption methods.
- Import the data into a Python environment and perform basic cleaning (handling missing values, date conversions, etc.).
- Conduct descriptive analysis and summarize key metrics using Python tools (e.g., pandas, numpy).
- Document the entire process with code explanations, visualizations, and commentary.
Evaluation Criteria
Your submission will be evaluated based on clarity, thoroughness, proper use of Python for data collection and analysis, and the depth of insights derived from the exploratory analysis. Make sure the DOC file is well-organized, with each section clearly labeled and supported by code extracts where applicable. The report should reflect your ability to apply data science techniques in a hospitality context over the estimated 30 to 35 hours of work.
Task Objective
This task focuses on refining raw hospitality data by applying data cleaning techniques and performing feature engineering to prepare the dataset for advanced analysis. You will simulate using public data sources by cleaning a dataset that represents a mix of hotel occupancy details, guest reviews, and service ratings. The objective is to apply Python programming skills in preprocessing data and deriving new features that may contribute to deeper insights in the hospitality field.
Expected Deliverables
- A comprehensive DOC file report that includes your methodology, detailed steps of data cleaning, and feature engineering.
- Annotated Python code explanations integrated within the report.
- Explanatory notes and justifications for each transformation or engineered feature.
Key Steps to Complete the Task
- Obtain a sample dataset from publicly available sources that simulate hospitality data.
- Identify and handle data quality issues such as missing values, outliers, inconsistencies, and duplicate records.
- Implement Python libraries such as pandas and numpy to perform data transformations.
- Create and document new features that could enhance the predictive power of the data model (e.g., sentiment scores, seasonality factors, or customer loyalty indices).
- Visualize the before-and-after effects of cleaning and feature engineering using charts or graphs.
Evaluation Criteria
Your DOC file should be comprehensive and logically structured. The clarity of your code explanations, the rationalization behind every transformation, and the final quality of the cleaned and enriched dataset are critical for evaluation. Your submission should indicate a thorough understanding of data quality challenges and the methods to solve them, all achieved within the expected 30 to 35 hours workload.
Task Objective
This task aims to deepen your understanding of predictive analytics by developing and implementing predictive models using Python. In this exercise, you will create models that forecast key performance indicators such as occupancy rates, revenue, or customer satisfaction within the hospitality industry. The task will require integrating feature engineering outcomes from Week 2 and applying machine learning techniques to build robust prediction models.
Expected Deliverables
- A detailed DOC file report that outlines your methodology, model selection, training process, and evaluation results.
- Python code segments demonstrating model development, training, validation, and interpretation of model outputs.
- Discussion on the choice of algorithms and metrics used to evaluate model performance.
Key Steps to Complete the Task
- Select appropriate predictive modeling techniques (e.g., regression, decision trees, or ensemble methods) using Python libraries such as scikit-learn.
- Prepare and split your data into training and testing sets, ensuring that the dataset is appropriately preprocessed.
- Train multiple models and perform hyperparameter tuning to identify the best performing model.
- Evaluate model performance using metrics like RMSE, MAE, or R-squared.
- Document every step in detail with clear code explanations, visualizations of predictions compared to actual figures, and a discussion on model limitations.
Evaluation Criteria
The evaluation of your submission will be based upon methodological clarity, the suitability of the chosen modeling techniques, and the depth of your evaluation analysis. Your report should provide a comprehensive narrative, clear visual aids, and code annotations that reflect a strong grasp of predictive modeling for hospitality performance over the course of the estimated 30 to 35 hours.
Task Objective
This task encourages you to focus on the art of data storytelling through interactive visualizations that convey insights from hospitality data. You will use Python libraries, such as matplotlib, seaborn, or Plotly, to create compelling visual content that tells the narrative of key trends and performance metrics in the hospitality industry. The emphasis is on making the data understandable and actionable by stakeholders through effective visuals.
Expected Deliverables
- A DOC file report containing a narrative of your analysis, the visualizations produced, and the insights derived from them.
- Embedded screenshots or code snippets of the Python visualizations.
- A detailed explanation discussing the insights and implications for the hospitality sector.
Key Steps to Complete the Task
- Revisit the dataset from previous weeks and identify key metrics and trends that can be visually highlighted.
- Use Python visualization libraries to create interactive and static plots that explore data narratives, such as seasonal trends, occupancy variations, or revenue drivers.
- Explain the significance of each visualization and how it aids in decision-making processes typical of hospitality management.
- Develop a cohesive story where each visualization contributes to a larger narrative linking operational insights with strategic recommendations.
- Draft your analysis within a structured DOC file that is clear and detailed.
Evaluation Criteria
Your DOC file will be assessed based on the clarity of your visual storytelling, the quality and relevance of your visualizations, and the depth of your analytical commentary. Ensure that each visualization is accompanied by thorough explanations and that the report reflects a coherent narrative built over the estimated 30 to 35 hours of work.
Task Objective
This final task requires you to integrate all the skills and knowledge developed over the previous weeks into a comprehensive strategic analysis report. Your goal is to formulate actionable insights and recommendations that can significantly impact decision-making in the hospitality industry. Here, you will use the outputs from data acquisition, cleaning, predictive modeling, and visualization to create a strategic plan using Python-driven analyses.
Expected Deliverables
- A DOC file report that integrates data findings into a comprehensive strategic plan.
- Sections detailing background research, analytical techniques used, results of the predictive models, and visualization insights.
- A list of actionable recommendations supported by the data analysis.
- Annotated Python code snippets that support key conclusions.
Key Steps to Complete the Task
- Review and synthesize the work accomplished in the previous weeks.
- Develop a strategic analysis that includes background research, detailed interpretation of your data analyses, and integration of all insights.
- Prepare actionable recommendations for improving operational efficiencies, customer satisfaction, and financial performance in a hospitality setting.
- Support your recommendations with evidence sourced from your predictive models and visual dashboards.
- Draft a comprehensive and well-organized DOC file report, ensuring that each section is well-supported by data and analysis, including both textual explanations and Python code excerpts.
Evaluation Criteria
Your final report will be evaluated on its overall clarity, integration of various data science techniques, strategic relevance, and depth of actionable insights. The submission should demonstrate your ability to use Python effectively for real-world hospitality data challenges, supported by professional-level documentation in a coherent DOC file prepared in approximately 30 to 35 hours of work.