- Overview of data analysis and its importance
- Types of data (structured, unstructured, etc.)
- Data sources and collection methods
- Data processing and cleaning techniques
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- EDA techniques and tools (e.g. visualizations, statistics)
- Finding patterns and trends in data
- Identifying potential problems and limitations in data
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- Fundamentals of data visualization (e.g. design principles, chart types)
- Best practices for creating effective visualizations
- Tools for creating visualizations (e.g. Excel, Tableau, ggplot)
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- Techniques for dealing with missing or incomplete data
- Techniques for handling outliers and anomalies
- Techniques for transforming and aggregating data
- Tools for data wrangling and cleaning (e.g. I will use Pandas)
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- Best practices for communicating data findings to different audiences
- Data storytelling techniques
- Working on a data analysis project or case study (e.g. identifying a problem, collecting and cleaning data, analyzing and visualizing results, communicating findings)
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