Tasks and Duties
Objective
This task focuses on the critical initial steps of any data science project: gathering data, cleaning it, and performing preliminary analysis. As a Hospitality Data Science Analyst, you are expected to lay the groundwork by identifying relevant publicly available datasets related to hospitality metrics (e.g., occupancy rates, customer satisfaction indices, booking trends), cleaning and transforming the data, and then exploring key trends.
Expected Deliverables
- A DOC file report detailing the end-to-end process.
- Sections on data identification, cleaning methodologies, preliminary exploratory data analysis (EDA), and key insights.
- Code snippets or pseudocode explanations, where relevant, demonstrating Python data science techniques.
Key Steps to Complete the Task
- Data Identification: Research and select publicly available hospitality datasets. Justify your selection and describe the characteristics of the dataset(s).
- Data Cleaning: Describe the cleaning process including handling missing values, outliers, and format inconsistencies using Python tools (e.g., Pandas). Provide a detailed explanation of your chosen methods.
- Exploratory Data Analysis (EDA): Perform and explain initial data exploration. Include measures such as summary statistics, correlations, and visual exploratory plots, explaining what patterns or anomalies you identified.
- Documentation: Organize your work into a well-structured DOC file report with clear headings, thorough explanations, and screenshots or code excerpts if applicable.
Evaluation Criteria
- Completeness of data collection and justification of dataset choice.
- Clarity and rigor in describing the cleaning and EDA process.
- Quality of insights derived from the EDA.
- Overall organization, clarity, and professionalism of the DOC report.
This assignment is designed to take approximately 30 to 35 hours of work. Ensure that your submission is self-contained and does not rely on internal resources from any platform.
Task Objective
This assignment will have you build a predictive analytics model using Python that can forecast guest satisfaction levels. You will simulate the process with publicly available data, design a regression or classification model (as appropriate), and interpret the key features influencing the results. The focus here is on constructing a robust and reliable model, while ensuring you document every step and decision process in your final report.
Expected Deliverables
- A DOC file report that details your approach, from data preparation to model evaluation.
- Description of the hypothesis, selected model (including why it was chosen), key feature engineering steps, model training, and testing.
- Insightful discussion on the model’s performance, including key metrics and potential use cases in hospitality analytics.
Key Steps to Complete the Task
- Data Preparation: Choose a publicly available dataset or simulate one based on hospitality metrics. Describe any pre-processing steps including normalization, feature scaling, or encoding.
- Model Selection and Feature Engineering: Explain your approach for model selection suitable for prediction of guest satisfaction. Justify your selected features and provide details on how you prepared them.
- Model Training and Testing: Implement the model using Python libraries (e.g., Scikit-learn). Detail the training process, cross-validation, and performance evaluation using appropriate metrics.
- Interpretation and Documentation: Clearly document the entire process, including code explanations, model limitations, and potential improvements for practical applications.
Evaluation Criteria
- Thoroughness in addressing data preparation and the rationale behind model selection.
- Validity and clarity of the evaluation metrics presented.
- Insightfulness of the analysis in leveraging data science to improve hospitality guest satisfaction.
- Overall quality and organization of the DOC report.
The task is expected to require 30 to 35 hours of your time. Please ensure your submission is fully self-contained and does not rely on proprietary resources.
Task Objective
This exercise is aimed at performing customer segmentation using clustering techniques to better understand the varied customer profiles in the hospitality industry. You will leverage Python’s data science libraries to conduct an unsupervised learning analysis that categorizes guests based on behavioral and demographic factors. This task is particularly relevant for creating tailored marketing strategies and enhancing guest services.
Expected Deliverables
- A DOC file report that comprehensively details your methodology, analysis, and interpretation of segmentation results.
- An explanation of the clustering algorithm chosen (e.g., K-means, hierarchical clustering), including a justification based on dataset characteristics.
- Visualizations (scatter plots, dendrograms, or other cluster visualizations) augmented by clear explanations of what each visualization reveals.
Key Steps to Complete the Task
- Data Selection and Preprocessing: Utilize a publicly available hospitality dataset or create a simulated data set. Prepare and preprocess the data to ensure its suitability for clustering (address scaling and missing values).
- Clustering Technique Implementation: Choose a clustering technique and document your rationale. Provide detailed steps on how to implement the algorithm including parameter selection.
- Visualization and Analysis: Produce visual representations of the clusters. Discuss the properties of each segment and provide insights regarding potential strategies to engage different clusters.
- Report Writing & Documentation: Compile your work into a DOC file report. Include an abstract, methodology, analytical results, and conclusion sections. Embed visuals and code snippets as necessary for clarity.
Evaluation Criteria
- Depth and clarity of the data preprocessing and rationale for chosen techniques.
- Effectiveness of clustering approach and quality of visualizations.
- Quality of the interpretative insights drawn from the segmentation analysis.
- Organization, readability, and thoroughness of the DOC report.
This task is estimated to require between 30 to 35 hours. Your DOC submission should be complete, self-explanatory, and independent of external internal resources.
Task Objective
The final task in this internship series focuses on data visualization and the art of storytelling in the context of hospitality analytics. Your goal is to transform complex datasets into compelling visual narratives that can be understood by both technical and non-technical stakeholders. You will use Python’s visualization libraries (such as Matplotlib, Seaborn, or Plotly) to create interactive and insightful visualizations that communicate key findings related to hospitality trends, customer behavior, or operational efficiency.
Expected Deliverables
- A DOC file that serves as a comprehensive story-telling report with a clear narrative woven through your visualizations.
- Detailed descriptions of the data sources, visualizations chosen, and the rationale behind the design decisions.
- Embedded examples of visualizations, along with interpretations of how each visualization contributes to the overall narrative.
Key Steps to Complete the Task
- Data and Storyboarding: Identify or simulate a dataset that reflects a relevant hospitality scenario. Develop a storyboard outline that defines the narrative you wish to convey.
- Visualization Design: Create a series of visualizations using Python libraries. Explain your choices of visualizations (e.g., bar charts, heat maps, line graphs) with emphasis on how they clarify or enhance understanding of the data.
- Narrative and Interpretation: For each visualization, provide a detailed narrative including context, key insights, and implications for decision-making in the hospitality sector.
- DOC Report Compilation: Organize your work into a well-structured DOC file report that includes an introduction, methodology, visualizations with annotations, discussion, and conclusion. Provide code excerpts or pseudocode wherever necessary to explain your process.
Evaluation Criteria
- Creativity and clarity in designing visualizations that effectively tell a story.
- Sound reasoning behind the visualization choices and narrative structure.
- Depth of insights drawn from the visual analysis.
- Professional presentation and organization of the DOC report.
This task is designed to take approximately 30 to 35 hours. Ensure that your submission is self-contained and does not depend on internal resources from any platform.