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by Cathy Tanimura
SQL for Data Analysis
1. Analysis with SQL
1.1 What is data analysis?
1.2 Why SQL
1.2.0 What is SQL?
1.2.1 Benefits of SQL
1.2.2 SQL vs. R or Python
1.2.3 SQL as part of the analysis workflow
1.3 Database Types and How to Work with Them
1.3.1 Row-store databases
1.3.2 Column-store databases
1.3.3 Other flavors of data infrastructure
1.4 Conclusion
2. Preparing Data for Analysis
2.0 Types of Data
2.0.1 Database data types
2.0.1 Structured vs. Unstructured
2.0.2 First-party, Third-party, and Cloud Vendor data
2.0.3 Sparse data
2.0.4 Quantitative vs. qualitative data
2.0.5 Categorical vs. continuous
2.1 Profiling: Distributions
2.1.1 Histograms and frequencies
2.1.3 Binning
2.1.2 N-tiles
2.2 Profiling: Data Quality
2.2.1 Detecting duplicates
2.2.2 Deduplication with GROUP BY and DISTINCT
2.2.3 Missing data
2.4 Data cleaning
2.4.1 CASE transformations
2.4.2 Dealing with nulls: COALESCE, NULLIF, NVL
2.4.3 Casting and type conversions
2.3 Shaping Data
2.3.1 For which output: BI, Visualization, statistics, ML
2.3.2 Pivoting with CASE statements
2.3.3 Unpivot with UNION statements
2.3.4 PIVOT and UNPIVOT
2.4 Conclusion
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