Describe and differentiate between types of data analysis: Descriptive analysis, diagnostic analysis, hypothesis testing, predictive analysis, prescriptive analysis.Aggregate data: Purpose and common practices (grouping, joining/merging, summarizing, pivoting, etc.).Organize data: Purpose and common practices (sorting, filtering, slicing, transposing, appending, truncating, etc.).Clean data: Purpose and common practices (handling NULL, special characters, trimming spaces, inconsistent formatting, removing duplicates, imputing data, etc.) validating data. Import, store, and export data: Fundamental understanding of ETL (extract, transform and load) processes, data manipulation tools (SQL, R, Python, Microsoft Excel including aspects of Power Query), and common data storage file formats (delimited data files, XML, JSON).Describe data categories (Qualitative, quantitative, structured, unstructured, metadata, big data).Describe basic structures used in data analytics (Tables, rows, columns, lists).Describe basic data variable types (Boolean, numeric, string).
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