Advanced Certificate in Health Data Journalism: Results-Oriented
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⢠Data Acquisition and Cleaning: This unit will cover various methods for acquiring and cleaning health data for journalistic purposes. Topics may include data scraping, APIs, and data wrangling techniques. ⢠Statistical Analysis for Health Journalism: Students will learn the basics of statistical analysis, including descriptive and inferential statistics, and how to apply these techniques to health data. ⢠Data Visualization and Design: This unit will focus on best practices for data visualization, including chart selection, color theory, and typography. ⢠Investigative Journalism and Data Analysis: Students will learn how to combine data analysis techniques with investigative journalism to uncover hidden trends and stories in health data. ⢠Ethics and Legal Considerations in Health Data Journalism: This unit will cover the ethical and legal considerations involved in using health data for journalistic purposes, including privacy concerns and informed consent. ⢠Communicating Health Data to Different Audiences: This unit will focus on how to tailor data storytelling for different audiences, including the general public, policymakers, and healthcare professionals. ⢠Big Data and Machine Learning in Health Journalism: Students will explore the use of big data and machine learning techniques in health journalism, including predictive modeling and natural language processing. ⢠Advanced Data Analysis Techniques: This unit will delve into more advanced data analysis techniques, such as time series analysis and multivariate regression, and their applications in health journalism.
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