Professional Certificate in Music Data for Researchers
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⢠Introduction to Music Data: Understanding the basics of music data, including types of data and common data structures used in the music industry.
⢠Data Collection Methods: Techniques for gathering music data, including web scraping, APIs, and data mining.
⢠Data Cleaning and Preparation: Techniques for cleaning and preparing music data for analysis, including handling missing data, outliers, and data normalization.
⢠Exploratory Data Analysis: Exploring music data to identify patterns, trends, and relationships, including visualization techniques and statistical analysis.
⢠Music Information Retrieval (MIR): Techniques for extracting information from music data, including pitch detection, tempo estimation, and genre classification.
⢠Machine Learning for Music Data: Applying machine learning algorithms to music data, including supervised and unsupervised learning, and deep learning.
⢠Ethical Considerations in Music Data Research: Understanding the ethical considerations involved in working with music data, including privacy, consent, and intellectual property.
⢠Case Studies in Music Data Research: Analyzing real-world examples of music data research, including successes and failures, and best practices.
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