Tasks and Duties
Task Objective
The goal of this task is to familiarize yourself with the process of exploratory data analysis (EDA) and data cleaning using Python. You will use publicly available datasets related to beauty and wellness trends to practice identifying data quality issues, generating summary statistics, and visualizing data distributions. You will develop a comprehensive understanding of data anomalies, missing values, and outliers, and document your findings along with the Python code used.
Expected Deliverables
A well-organized DOC file containing a complete report on your analysis. This should include an introduction, methodology, code snippets, findings, discussions on challenges, and recommendations for further analysis. The document should also contain screenshots of your plots and tables, and your code should be well-commented.
Key Steps to Complete the Task
- Identify a publicly available dataset relevant to beauty and wellness.
- Perform data cleaning by handling missing values, duplicates, and inconsistencies.
- Conduct an initial exploratory data analysis to summarize the main characteristics of the dataset using statistical techniques and visualizations.
- Create visualizations (e.g., histograms, box plots, scatter plots) to illustrate patterns and potential issues in the data.
- Document your process, findings, and insights in a detailed DOC file.
Evaluation Criteria
Your submission will be evaluated based on the clarity of your documentation, the correctness and completeness of the data cleaning and EDA process, the quality of visualizations, and the relevance and depth of insights drawn. The task should reflect careful analytical reasoning and be detailed enough to be replicated by another data science analyst.
This task is designed to be challenging and to simulate real-world data analysis scenarios, requiring approximately 30 to 35 hours of work from start to finish.
Task Objective
This week's assignment focuses on creating insightful data visualizations and building a compelling narrative around beauty and wellness data trends using Python. The core objective is to transform raw data into visually appealing charts and graphs that inform decision-making. You will explore advanced visualization libraries in Python such as Matplotlib, Seaborn, or Plotly to craft a story that communicates key findings effectively. Moreover, the task emphasizes the importance of context and narrative in data visualization.
Expected Deliverables
Submit a DOC file that includes a detailed report of your work. Your report should contain an introduction to the data and the business context, a description of the methods used to create your visualizations, screenshots or embedded images of the charts, and an analysis section where you interpret the results. Include code snippets and explanations alongside the visuals to demonstrate your methodology.
Key Steps to Complete the Task
- Select or simulate an appropriate dataset concerning beauty and wellness trends from publicly available sources.
- Identify key metrics and trends worth visualizing.
- Create a variety of charts (e.g., line plots, bar charts, heat maps) that best represent the data insights.
- Develop a narrative that connects these visuals logically to communicate an underlying trend or insight clearly.
- Compile your visualizations, code excerpts, and analytical insights in a well-formatted DOC file.
Evaluation Criteria
You will be assessed on creativity, data interpretation skills, the effectiveness of the visual storytelling, and the technical accuracy of your Python code. The DOC file should be professionally organized and sufficiently detailed so that someone with basic Python knowledge can follow your analytical process. The overall effort should reflect a deep engagement in transforming raw data into insightful business narratives.
This task is expected to require approximately 30 to 35 hours of work.
Task Objective
The focus of this task is to introduce you to predictive modeling and machine learning using Python. You will work on designing and implementing a basic predictive model that leverages publicly available beauty and wellness data. The objective is to learn how to prepare data, select appropriate machine learning algorithms, train the model, and evaluate its performance. This exercise is essential for understanding how predictive analytics can forecast trends or customer behaviors in the beauty and wellness sector.
Expected Deliverables
Submit a DOC file containing a comprehensive report. Your report should include an introduction to the predictive problem, details on data preprocessing steps, the methodology used for model building, a description of the model parameters, performance metrics, and a critical analysis of model results. Attach code snippets and visualizations (such as performance curves or confusion matrices) to support your analysis.
Key Steps to Complete the Task
- Select or simulate a dataset reflecting key beauty and wellness variables.
- Perform necessary data preprocessing, including feature selection and handling missing values.
- Choose a suitable machine learning algorithm (e.g., linear regression, decision tree, or logistic regression) for prediction.
- Train the model, validate it using proper cross-validation techniques, and evaluate its performance.
- Document the process, results, and potential improvements in a detailed DOC file.
Evaluation Criteria
Your submission will be evaluated on technical accuracy, clarity of explanations, the robustness of the predictive model, and the thoroughness of your analysis. Each section of the DOC file should be well-structured and provide clear evidence of your understanding of both the modeling process and its application. The final document must reflect a clear, logical process from initial data handling through to model evaluation, aiming for a complete analysis within the allocated 30 to 35 hours.
Task Objective
This task is designed to introduce you to text analytics and sentiment analysis using Python. You will work on a project that involves analyzing customer reviews or textual feedback related to beauty and wellness products. The objective is to learn how to preprocess text data, extract key insights, and determine sentiment (positive, negative, or neutral). This hands-on assignment will require you to use Python libraries such as NLTK, TextBlob, or similar tools to transform textual data into actionable insights.
Expected Deliverables
You are expected to submit a DOC file containing your complete report. The document should incorporate an introduction to the task, a detailed description of the text preprocessing steps, methods used to conduct sentiment analysis, your findings in terms of overall sentiment distribution, and visualizations to support your conclusions. Include Python code snippets and a detailed explanation of each step to enhance clarity and reproducibility.
Key Steps to Complete the Task
- Select or create a dataset consisting of customer reviews or feedback relevant to beauty and wellness.
- Preprocess the text data by cleaning, tokenizing, and normalizing the content.
- Implement sentiment analysis using appropriate Python libraries, and classify the sentiment.
- Create visualizations (such as word clouds or sentiment distribution charts) to represent the results.
- Compile your work, along with challenges encountered and lessons learned, into a comprehensive report in a DOC file.
Evaluation Criteria
Your analysis will be assessed based on the comprehensiveness of the text preprocessing, the appropriateness of the analytical methods applied, clarity in the presentation of results, and the overall coherence of your report. The final DOC file should be self-contained and detailed enough for a reviewer to understand your approach and replicate the analysis if needed. This task aims to provide practical knowledge on handling unstructured data, requiring a commitment of 30 to 35 hours of effort.
Task Objective
In Week 5, your assignment involves developing a strategic proposal based on data-driven insights. This task requires you to use your knowledge of Python for data analysis to inform a strategic plan aimed at enhancing performance in the beauty and wellness market. You will engage in a planning and strategy exercise that combines data analytics with business strategy, ensuring that your recommendations are well-supported by data trends and analysis. The focus is on synthesizing information from various data exploration tasks to provide actionable recommendations.
Expected Deliverables
Create a DOC file report that outlines your full strategic proposal. The document must include sections such as an executive summary, data analysis review, strategic objectives, detailed recommendations, and an implementation plan. Be sure to support your recommendations with data insights, graphs, and code snippets used to derive your conclusions.
Key Steps to Complete the Task
- Review existing publicly available data on beauty and wellness trends, paying attention to recurring themes and anomalies.
- Perform any necessary data analysis using Python and compile key insights.
- Formulate strategic objectives based on your findings that address issues and opportunities in the market.
- Develop detailed recommendations and an action plan that outlines how these strategies can be implemented.
- Document each phase of your analysis, strategy formulation, and proposed implementation step-by-step in a DOC file.
Evaluation Criteria
Your strategic proposal will be evaluated on the clarity of the business problem identification, the integration of data-driven insights, feasibility and innovativeness of the recommendations, and the overall structure and quality of the report. It should reflect a clear, well-thought-out plan that connects data analysis with practical business strategies. This comprehensive exercise should take around 30 to 35 hours, allowing for in-depth analysis and a robust proposal document.
Task Objective
The final week’s task is centered on creating an in-depth final report that captures your work and insights from the internship period. You will compile your analytical procedures, models, and strategic recommendations into one cohesive DOC file. Beyond merely summarizing your previous tasks, this assignment requires you to evaluate the performance of your efforts by outlining successes, challenges, and areas for further improvement. The focus is on synthesizing multiple aspects of data analysis—from initial exploration to predictive modeling and strategic proposals—into a report that demonstrates your holistic understanding of applying Python in data science within the beauty and wellness industry.
Expected Deliverables
Your final deliverable is a DOC file report structured into several clearly defined sections: an introduction and background, methodologies used across various tasks, a summary of key findings from each task, performance evaluations, challenges encountered, lessons learned, and future scope for improvement. The document should be detailed, well-organized, and reflect your journey throughout the internship experience.
Key Steps to Complete the Task
- Review all previous tasks and gather all analytical outputs, charts, models, and strategy documents.
- Write a comprehensive summary of your methodologies, analyses, and results.
- Include sections that critically evaluate the performance and effectiveness of each approach taken.
- Discuss insights gained, challenges faced, and how these could be addressed in a real work scenario.
- Compile all the information into a cohesive DOC file that includes visual aids, code snippets, and data-driven insights.
Evaluation Criteria
The final report will be judged on its comprehensiveness, clarity, logic, and professional presentation. Special attention will be given to how well you integrated diverse components of your internship experience into a coherent narrative. The DOC file should be thorough, well-edited, and rich in content, demonstrating a sound understanding of data science applications using Python in a real-world context. This task is intellectually demanding and is expected to require approximately 30 to 35 hours of work to produce an outstanding document.