Data Analysis with SQL#

Data Analysis with Databases

You’ll learn how to perform data analysis using SQL (via Python), covering:

  • Database Connection: How to connect to a MySQL database using SQLAlchemy and Pandas.
  • SQL Queries: Execute SQL queries directly from a Python environment to retrieve and analyze data.
  • Counting Rows: Use SQL to count the number of rows in a table.
  • User Activity Analysis: Query and identify top users by post count.
  • Post Concentration: Determine if a small percentage of users contribute the majority of posts using SQL aggregation.
  • Correlation Calculation: Calculate the Pearson correlation coefficient between user attributes such as age and reputation.
  • Regression Analysis: Compute the regression slope to understand the relationship between views and reputation.
  • Handling Large Data: Perform calculations on large datasets by fetching aggregated values from the database rather than entire datasets.
  • Statistical Analysis in SQL: Use SQL as a tool for statistical analysis, demonstrating its power beyond simple data retrieval.
  • Leveraging AI: Use ChatGPT to generate SQL queries and Python code, enhancing productivity and accuracy.

Here are the links used in the video:

Backward Data Analysis With Python Data Analysis With Datasette Forward