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Дано:
DATA ANALYST CASE STUDY
CONTEXT
We have partnered with a music publisher seeking to secure funding by monetizing future royalties. The publisher will receive immediate cash in exchange for a portion of future royalties, calculated as the initial cash amount plus interest. We need to evaluate the viability and potential of their artist roster using the provided data.
DATA PROVIDED
You will find two datasets: "Author Information" and "Revenue Information by Song."
1. Author Information
AuthorID: Unique identifier for each author.Birthdate: Date of birth of the author.SignDate: Date when the author signed with the publisher.
2. Revenue Information by Song
AuthorID: As above.SongID: Unique identifier for each song.Year: Revenue generation year.Format: Revenue channel (e.g., Digital for streaming).Revenue: Annual revenue per author, song, and channel.
Задание:
YOUR TASK
Conduct analyses to assess the quality of the publisher's roster using the provided data.
Analysis 1: Determine the growth trajectory of authors. Suggest relevant KPIs and statistical tools for this analysis.Analysis 2: Explore various data dimensions like SongID, Format, SignDate, and BirthYear. Consider questions such as song performance over time, author diversity, and productivity.
Questions for Consideration
Address potential data inconsistencies, such as incorrect revenue allocations. Describe your approach to data cleaning and analysis adjustments.Propose methods to gain deeper insights into the roster's performance beyond the provided data.
DESIRED OUTPUT
Perform Analyses 1 and 2 using SQL and/or Python. Provide a well-documented project file with visualizations. Ensure flexibility in the analysis tools (e.g., comparing specific AuthorIDs efficiently).Please provide a very short summary of the analysis you have conducted before our follow-up conversation, which includes:Graphs or table.Short description of what the graph or table illustrates.For Questions 1 and 2, describe your thought process and approach.
Evaluation Criteria
Proficiency in SQL/Python.Ability to translate vague problems into concrete analyses.Skill in presenting complex data insights in an easily understandable format for non-experts.
Note
All data is fictional and intended for this exercise only. Realistic, logical insights might be limited.