| Introduction to SQL | - Explain when and why to use databases, including PostgreSQL and SQL Server.
- Identify the appropriate SQL data types for a given set of data and describe how data is stored.
- Create and refine queries to extract insights from a database using best practices for SQL style.
- Create a view to save queries for future use.
- Utilize aliasing to clarify the intent or contents in a column.
|
|---|
| Intermediate SQL | - Utilize best practices for writing clear and readable code.
- Filter, compare, and summarize data with SQL.
- Summarize data using aggregate functions.
- Write SQL queries using aliasing with arithmetic and functions.
- Debug SQL code by identifying and resolving errors and inconsistencies in data and code.
|
|---|
| Joining Data in SQL | - Join data from multiple tables.
- Define one-to-many, one-to-one, and many-to-many relationships between tables.
- Write a query combining multiple joins.
- Determine the best type of join to use in a given context.
|
|---|
| Data Manipulation in SQL | - Transform and manipulate data using the CASE statement, simple subqueries, correlated subqueries, and window functions.
- Compare summarized values to detailed data using subqueries.
- Format and annotate subqueries so that they are clear and readable.
- Determine when it is necessary to use subqueries, joins, and common table expressions.
|
|---|
| PostgreSQL Summary Stats and Window Functions | - Create queries for analytics and data engineering using window functions.
- Fetch, rank, and bin values in rows.
- Utilize aggregate functions as window functions.
- Define frames to change the behavior of a window function.
|
|---|
| Functions for Manipulating Data in PostgreSQL | - Identify common data types and their characteristics.
- Manipulate date and time data using built-in functions and operators.
- Parse and manipulate text data types using functions.
- Execute full-text searches using PostgreSQL extensions.
|
|---|
| Introduction to Statistics | - Determine whether to use descriptive or inferential statistics to draw accurate conclusions in a given context.
- Determine variance, standard deviation, and quantiles to measure spread and find outliers.
- Generate random samples and measure chance using probability.
- Create histograms to plot normal distributions.
- Quantify the strength of a linear relationship between two variables.
|
|---|
| Exploratory Data Analysis in SQL | - Join tables to understand how they are related.
- Use functions to summarize numeric data in new ways.
- Address inconsistencies in case, spacing, and delimiters in text and character data.
- Aggregate date/time data to construct time series and identify gaps.
|
|---|
| Data-Driven Decision Making in SQL | - Utilize simple SQL queries to extract and aggregate data.
- Use complex queries and sub-queries to gain insight into customer preferences.
- Use nested queries and correlated nested queries to categorize data.
- Apply OLAP extensions in SQL to aggregate data.
|
|---|
| Understanding Data Visualization | - Graphically represent the spread of a variable using histograms and box plots.
- Interpret data plots and explore the relationship between two continuous variables using scatter plots and line plots.
- Utilize shape and color to make data visualizations clearer and easier to understand.
- Identify and avoid common plot problems.
|
|---|
| Data Communication Concepts | - Identify needed elements to tell stories with data.
- Apply best practices for visualizations to curate presentations to your audience.
- Create structured reports to appropriately describe findings.
- Create structured presentations that communicate insights and inspire action.
|
|---|