代做The dataset contains raw Ensemble Data API extracts from Adidas' Instagram and TikTok accounts.代写P
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The dataset contains raw Ensemble Data API extracts from Adidas' Instagram and TikTok accounts. This data is not cleaned at all and represents the kind of unstructured data we often work with in practice. You will need to make decisions about how to process, filter, and analyze this information. There is a lot of data to sift through, including metrics, timestamps, captions, and engagement data.
Your goal is to identify factors driving audience engagement with Adidas.
Guidelines
· Scope: We encourage you to prioritize the aspects you find most important/promising. In your writeup, briefly mention what you would explore further if you had more time.
· Use of Tools: You are welcome to use programming languages (Python recommended) and GenAI tools that you find helpful.
· Data: The dataset includes raw API responses from Ensemble Data containing Instagram and TikTok data for Adidas. You'll need to parse and clean this data to make it usable for analysis. Feel free to reference: https://ensembledata.com/apis/docs for more information on the data fields.
Deliverables
1. A 2-page report of your findings and recommendations (strict limit)
o Brief overview of your approach
o Key insights from your analysis
o Recommendations based on your findings
o Suggestions for future research directions
2. Code repository that can be shared upon request
o Well-documented code for your data processing and analysis
o Comments explaining your analytical decisions
If you use generative AI tools (Claude, ChatGPT, etc.) in your analysis:
· You must explicitly document which parts of your work used AI assistance
· Explain your prompting strategy and why you chose to use AI for those specific tasks
· Show how you verified or expanded upon AI-generated insights
· Include your prompts and the AI-generated outputs in an appendix (not counted in the 2-page limit)
Evaluation Criteria
We will evaluate your submission based on:
· Technical proficiency in data processing and analysis
· Ability to identify meaningful patterns in complex data
· Quality of insights and recommendations
· Clear communication of findings
Remember that interesting null results are completely fine, if your analysis shows them to be convincing!