Tasks and Duties
Objective
This task aims to immerse you in the realm of Beauty & Wellness from a data science perspective. You will define a specific business problem related to the beauty and wellness industry that can be addressed with data science techniques. The focus is on understanding the business context, formulating relevant questions, and outlining an approach that combines both marketing insights and analytic rigor.
Expected Deliverables
- A comprehensive DOC file narrating your chosen business problem
- An explanation of its relevance in the beauty & wellness sector
- A clear articulation of research questions and hypotheses
- A step-by-step outline of your anticipated approach
Key Steps
- Research trends in the beauty & wellness industry using publicly available information.
- Select a business problem (e.g., predicting customer trends, product recommendation issues, or brand sentiment analysis).
- Define the variables and possible data that could help investigate the problem.
- Develop an analysis plan that includes initial exploratory ideas and potential data science tools.
- Document the problem statement, objectives, and a rationale for your approach.
Evaluation Criteria
- Clarity and relevance of the problem statement.
- Depth of industry and academic research supporting the chosen problem.
- Coherence in the proposed approach and justification for selected methods.
- Overall structure and articulation of the DOC file deliverable.
This task is designed for approximately 30-35 hours of work. Ensure that your final DOC file is detailed and professional, presenting a strong conceptual foundation for your upcoming data analysis work in the beauty & wellness sector.
Objective
This week you are required to design a strategy to identify relevant data sources and plan for data collection, using publicly available data as references. The task emphasizes understanding how to identify, assess, and collect data that suits your chosen problem from Week 1. The focus is on creating a structured plan for data collection while ensuring alignment with the beauty & wellness theme.
Expected Deliverables
- A DOC file outlining your data sourcing strategy.
- A list of potential data sources with links to publicly available datasets or APIs.
- An explicit plan for initial exploratory analysis including expected statistical methods and visualization techniques.
- An assessment of data quality and relevance for solving the defined problem.
Key Steps
- Review diverse public resources such as governmental databases, social media datasets, and public APIs from the beauty & wellness industry.
- Create a comprehensive list of data sources and discuss their pros and cons including data accessibility, frequency, and reliability.
- Plan a preliminary exploratory analysis that includes descriptive statistics, data visualization ideas, and identification of key variables.
- Explain how your data can help in testing the hypotheses defined in Week 1.
- Detail data cleaning or preprocessing that might be required initially.
Evaluation Criteria
- Thoroughness in identifying and evaluating multiple data sources.
- The clarity of the data collection strategy aligned with the business problem.
- Depth of planning for initial analysis and techniques outlined.
- Logical structure and presentation in your DOC file.
This task should take approximately 30-35 hours to complete, allowing you adequate time to plan rigorously and document your strategy in detail.
Objective
The focus of this task is to develop a robust data cleaning and preprocessing pipeline for data that you have conceptualized in previous weeks. You will simulate the cleaning of raw data, address data quality issues, handle missing values, and perform normalization. The aim is to prepare the dataset for accurate analysis and modeling in a typical beauty & wellness context.
Expected Deliverables
- A detailed DOC file documenting your data cleaning and preprocessing pipeline.
- A step-by-step description of how you would handle issues such as missing values, outliers, and data inconsistencies.
- A discussion on the tools (Python libraries like pandas, NumPy, etc.) you would use and why they are suitable.
- Insight on documentation of your cleaning process for reproducibility.
Key Steps
- Outline potential issues that might be present in publicly available data related to beauty & wellness.
- Describe methods for cleaning data including handling missing entries, detecting outliers, and standardizing data formats.
- Explain the use of Python libraries for the cleaning process and include pseudo-code examples if possible.
- Discuss how the cleaned data will enhance further analysis and modeling efforts.
- Create a flowchart or schematic of your overall data preprocessing steps and rationale behind each.
Evaluation Criteria
- Depth and clarity in describing the data cleaning process.
- Technical accuracy in the proposed usage of Python libraries.
- Innovative solutions to common data quality issues in the beauty & wellness domain.
- Overall quality and organization of the DOC file.
This assignment is intended to engage you for 30-35 hours, with an emphasis on both conceptual planning and practical execution strategies documented clearly in your DOC file.
Objective
This task focuses on performing an Exploratory Data Analysis (EDA) on the preprocessed data and creating new features that could improve further data modeling. You will simulate the analytical process by drafting a detailed EDA workflow that helps uncover patterns, trends, and potential insights specifically in the beauty & wellness market.
Expected Deliverables
- A comprehensive DOC file detailing your EDA approach.
- An explanation of the statistical and visualization methods you plan to use.
- A plan for feature engineering, including potential new variables and transformations.
- A discussion of how these features can be used to drive better analytical insights and model accuracy.
Key Steps
- Review basic and advanced EDA techniques suitable for multi-dimensional data.
- Create a detailed plan outlining the use of Python libraries (e.g., matplotlib, seaborn, SciPy) for producing meaningful visualizations.
- Discuss potential features that capture hidden aspects of customer behavior and market trends in the beauty & wellness industry.
- Draft examples of feature engineering processes, such as normalization, encoding, or creating interaction variables.
- Map out a hypothetical scenario where these engineered features could improve decision-making.
Evaluation Criteria
- Quality and depth of exploratory analysis plan.
- Creativity and relevance in proposed feature engineering strategies.
- Alignment between EDA findings and business insights for the beauty & wellness market.
- Clarity, specificity, and structure in your DOC file submission.
The intended workload for this assignment is approximately 30-35 hours, providing sufficient time to develop a well-thought-out document that links data science techniques with actionable industry insights.
Objective
This week’s assignment focuses on the predictive modeling phase. You will simulate the process of selecting and justifying appropriate predictive algorithms that could be employed to forecast trends or behaviors in the beauty & wellness domain. The task necessitates an analytical approach in weaving business insights with technical methods, tailoring model selection to the specific characteristics of the data and the problem defined earlier.
Expected Deliverables
- A DOC file that outlines your modeling strategy and rationale behind choosing one or more predictive algorithms.
- A detailed description of at least two potential models (such as linear regression, decision trees, or clustering techniques) that are applicable for the defined business problem.
- A discussion on the evaluation metrics you plan to use (e.g., RMSE, precision, recall) and predictions about model performance.
- An explanation of how model outcomes will be interpreted for business insights in the beauty & wellness industry.
Key Steps
- Review the data problem defined in Week 1 and align it with suitable predictive tasks.
- Conduct research on different predictive models using Python libraries such as scikit-learn.
- Draft a rationale comparing models including their respective strengths and weaknesses in the context.
- Define how you would validate these models and ensure their robustness.
- Explain potential pitfalls and the steps to mitigate overfitting or underfitting in your analysis.
Evaluation Criteria
- Technical depth and justification of the selected predictive models.
- Clarity in connecting the problem statement with the modeling approach.
- Practical insight into model validation and evaluation procedures.
- Overall organization and depth of your DOC file explanation.
This assignment is estimated to be completed within 30-35 hours. It requires a detailed connection of data science techniques with real-world industry scenarios, ensuring that your predictive modeling strategy is sound and well-documented.
Objective
The final task focuses on the evaluation of your predictive model and the communication of your findings in a manner that is accessible to a non-technical audience. You are required to design an evaluation framework that assesses the performance of your model(s) and to draft a comprehensive report that outlines both the technical details and business implications. The key challenge is to merge data science insights with actionable recommendations for stakeholders interested in the beauty & wellness market.
Expected Deliverables
- A DOC file that explains your model evaluation strategy clearly.
- A section summarizing the performance metrics and what they signify in a business context.
- A detailed report that includes visualizations, performance tables, and charts (sketches or sample descriptions are acceptable) outlining the outcomes.
- A concluding section with actionable business recommendations based on your findings.
Key Steps
- Outline key performance metrics pertinent to your predictive model, such as accuracy, F1-score, or AUC-ROC, along with the rationale for choosing them.
- Design a comprehensive validation plan, describing different stages of model testing and performance measurement.
- Integrate visual aids (diagrams, flowcharts, mock-up tables) as part of your report to communicate the results clearly.
- Discuss the business impact of the results, detailing how they can affect brand decisions, marketing strategies, or customer engagement in the beauty & wellness industry.
- Provide a final summary that synthesizes technical findings with practical, actionable insights for decision-makers.
Evaluation Criteria
- Depth of model evaluation and justification for chosen metrics and methodologies.
- Effectiveness in communicating complex results in an accessible language.
- Relevance and clarity of business recommendations derived from data analysis.
- Quality, structure, and thoroughness of the DOC file report.
This concluding assignment should require approximately 30-35 hours of work. It is crucial that your DOC file is comprehensive, well-organized, and demonstrates a seamless integration of technical evaluation with strategic business insights in the beauty & wellness sector.