- Your SQL file (*.sql): All exploratory and analytical queries used for this project, written with correct and efficient syntax and commented throughout with context for both the query and the result. Ensure the correct file format by using a coding text editor like Sublime Text.
- Clean the data as appropriate by removing duplicates, addressing nulls, and reformatting data types (e.g., string to date) before you export the query results as a file.
- Select only the columns that will be used in your analysis before you export the query results to your Excel workbook.
- Add new columns for categorical or numerical data as appropriate using your other features before you export the query results as a file (e.g., math, string, date functions).
- Describe the steps you took to wrangle the data in a handling summary in a tab at the beginning of your workbook.
- Analyzing the Data in SQL
- Exploratory data analysis: Use these questions to get to know your data. Record your answers in Sheet 2 (Analysis & Charts) the workbook:
- What’s the count of observations (rows) and features (columns)?
- Are there any features that are dependent on other features in your data?
- What’s the data type of each feature — categorical or numerical? integer, string/text, date, timestamp?
- Answers to your hypothesis-driven business questions: As you write out your answers in Sheet 2, consider the following:
- What fields did you combine to find interesting insights?
- What actions can someone take as a result of your analysis and charts?
- Interpreting the Data in SQL and Using Excel to Visualize
- Use Sheet 2 (Analysis & Charts) to summarize relevant numbers for various data points and interpret the reasons for these numbers for your audience.
- Add any analysis that did not result in insights to Sheet 4 (Reference).
- Create visualizations using the best chart format to highlight insights.
- Use the results from your analysis to compile a business recommendation that addresses the problem you sought to