Return to course: Analytics Engineering
Analytics Engineering
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Analytics Engineering
Grades
Resources
Module 1: Welcome to Analytics Engineering!
Module 1: Lesson
Module 2: Data Fundamentals
Module 2: Instructor Lesson
Interactive Lesson: Data Architecture Challenge
Interactive Lesson: Data Detective Challenge
Module 2: Homework - BigQuery Data Structures
Module 2: Homework - Fivetran BigQuery ELT
Module 2 Quiz
Module 3: SQL for Analytics Engineers
Module 3: Lesson
Module 3: Walkthrough - SQL SELECT / DISTINCT
Module 3: Walkthrough - SQL FILTERING / WHERE
Module 3: Walkthrough - SQL ORDER BY / LIMIT
Module 3: Walkthrough - SQL AGGREGATIONS
Module 3: Walkthrough - SQL Aggregations, String Filtering, Having
Module 3: Instructor Walkthrough - SQL Inner, Left, and Complex Joins
Module 3: Instructor Walkthrough - SQL Subqueries in FROM and WHERE
Module 3: Instructor Walkthrough - SQL CASE Statements
Module 3: Instructor Walkthrough - SQL Set Operators & DateTime Functions
Module 3: Instructor Walkthrough - SQL String Functions
Module 3: Instructor Walkthrough - SQL Scalar and Numeric Functions
Module 3: Instructor Walkthrough - SQL Performance Optimizations
Module 3: Instructor Walkthrough - SQL Styling and Formatting
Module 3: Instructor Walkthrough - SQL Interview Questions & Tips
Module 3 Quiz
Interactive Lesson: SQL Rescue Quest
Interactive Lesson: Advanced SQL Space Station
Module 4: Data Modeling and Architecture
Module 4: Lesson
Interactive Lesson: Data Modeling
Interactive Lesson: Normalization Ride Share
Interactive Lesson: Slowly Changing Dimensions
Module 4 Quiz
Module 5: dbt and Github
Module 5: Lesson
Interactive Exercise: Github Workflows
Interactive Exercise: dbt Incremental Materialization
Module 5 Quiz
Module 6: Data Quality and Testing
Module 6: Lesson
Interactive Lesson: Anomaly Detection Bollinger Bands
Interactive Lesson: Data Quality Investigation
Interactive Lesson: Great Expectations
Interactive Lesson: dbt Testing
Module 6 Quiz
Module 7: Programming for Analytics Engineers
Module 7: Lesson
Interactive Lesson: Python Food Delivery
Module 7 Quiz
Module 8: Visualization and Reporting
Module 8: Lesson
Interactive Lesson: Dashboard Design Simulator
Module 8: Homework - Looker Studio Marketing Sales and Spend
Module 8 Quiz
Module 9: AI Tools Mastery
Interactive Lesson: AI Tools for Analytics Engineering
Module 10: Analytics Engineering Capstone Project
Capstone Intro
Accounts and Access
Module 2 Quiz
Question 1: Which of the following is an example of a structured data type?
*
A) Text file
B) SQL table
C) Image file
D) Audio recording
Question 2: What is a common use for the "VARCHAR" data type?
*
A) Storing numbers
B) Storing true/false values
C) Storing date/time values
D) Storing variable-length text
Question 3: What is the key difference between an array and a list in programming languages?
*
A) Arrays are unlimited in size
B) Lists must contain only text
C) Arrays typically have fixed size and type; lists are more flexible
D) Lists are only used in Excel
Question 4: What is the primary purpose of a Data Warehouse?
*
A) Storing raw unstructured logs
B) Running mobile applications
C) Centralizing and analyzing historical business data
D) Hosting websites
Question 5: In a data warehouse, what is a "fact table"?
*
A) A table that stores metadata
B) A table that contains descriptive information
C) A lookup table
D) A table that stores numerical measures and business events
Question 6: What is the role of a "dimension table" in a star schema?
*
A) To describe attributes related to facts (e.g., customer, product)
B) To normalize fact tables
C) To store transactional logs
D) To calculate measures
Question 7: What is data denormalization often used for in data warehouses?
*
A) To reduce data duplication
B) To make OLTP faster
C) To simplify queries and improve performance
D) To enforce referential integrity
Question 8: Which layer of a data warehouse architecture is responsible for storing raw data?
*
A) Presentation Layer
B) Staging Layer
C) Analytics Layer
D) Reporting Layer
Question 9: What does the ETL process stand for?
*
A) Encode, Translate, Load
B) Extract, Transfer, Load
C) Extract, Transform, Load
D) Evaluate, Test, Log
Question 10: What is the purpose of the Semantic Layer in a modern data stack?
*
A) To define user roles
B) To handle data encryption
C) To provide a consistent and business-friendly view of the data
D) To manage cloud billing