Introduction
Introducing the complete guide to analytics engineering
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Course contents overview
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GitHub Codespaces introduction
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CoderPad introduction
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1. Overview of Analytics Engineering
Introduction to analytics engineering
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Analytics engineering is a hybrid role
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The lifecycle of data
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The evolution of analytics engineering
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Democratization of data and semantic layers
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2. Overview of Databases, Data Lakes, and Data Warehouses
Databases, data lakes, and data warehouses…oh my!
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Relational databases
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Nonrelational databases
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Data warehousing
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Data lakes: An alternative storage method
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How do databases support decision-making?
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Database best practices
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3. Manipulating Data with Python pandas
What is Python, and why do we use it?
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Our Python environment and dataset
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Kernels, running Python code, and other basics
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The pandas Python library
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DataFrames, data series, and data types in pandas
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Selecting, sorting, and filtering data with pandas
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Solving common data type problems with Python pandas
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Cleaning data with pandas
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CoderPad solution: Solve an analytical task with Python
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4. Analyzing Data with Python pandas
Analytical functions in pandas
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Grouping data in pandas
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Merging multiple data frames with pandas
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Creating new custom calculated columns with pandas
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Creating rolling averages with window calculations
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CoderPad solution: Calculate averages with Python window functions
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5. Leveraging Python to Extract Transform Load
Why move data using ETLs?
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ETL vs. ELT
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Connecting to our database and API
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Data fetching
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Merging and overwriting existing data
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DAGs
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Benefits and drawbacks of ETL tools
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6. Modeling Data with SQL
Introduction to SQL for analytics engineering
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The SELECT statement
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Filtering data results with the WHERE clause
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Aggregate functions in SQL
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SQL date functions
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Inner joining multiple tables
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Left joining multiple tables
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Other types of SQL joins
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Common table expressions
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CoderPad solution: Modeling data with SQL
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7. dbt
What is dbt?
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Semantic layers for modern data workflows
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DAGs to visualize data model layers
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Building, running, and testing
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dbt macros
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8. Visualizing Data with Business Intelligence Tools
Introduction to business intelligence
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Tableau setup
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Connecting to different data sources in Tableau
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Dimensions vs. measures
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Creating calculated fields
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Use the right chart for visualization
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Blending multiple data sources together
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Crafting an interactive dashboard
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Data visualization best practices
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9. Working with Stakeholders
Why analytics engineering teams need stakeholder relationships
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Concise, quick communication
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Managing a project from start to finish
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Ensuring your stakeholders use your product
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Conclusion
Brief overview of topics covered
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What to do next
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