Welcome to our exploration of data science, where we will lay the foundation for understanding and working with data effectively. In this series, we'll begin with the basics of data foundations, providing an essential overview of key concepts. From there, we'll dive into data literacy, addressing data gaps, understanding bias, and the importance of statistics and data visualizations. We will also touch upon the fascinating realm of causal analysis, using historical examples like the case of cholera to illustrate these concepts in action.
Next, we'll move on to data collection, exploring the types and quality of data, the importance of handling messy or missing data, ensuring data accuracy, and understanding the validity and representativeness of your samples.
Finally, we'll cover statistical thinking, starting with categorical variables, to help you develop a robust framework for analyzing and interpreting data, making informed decisions, drawing meaningful insights, and so on.
Stay tuned for more in-depth lectures on these topics as we continue to build your data science expertise.