Complete Guide to Analytics Engineering

Course details

This detailed course covers the topics and skills needed for success as an analytics engineer. Instructor Connor Dickson illustrates how analytic engineering bridges the gap between data engineering and data analytics, and can help you be a Swiss Army knife of analytics skills. After completing this course, you should be able to work with data using the most popular tools such as SQL, Python, dbt, Tableau, and more.

Learning objectives
Understand and have practical abilities with the most common functions and uses of SQL, as it is the backbone of analytics engineering.
Gain a foundational ability to manipulate datasets in python.
Build a complete data pipeline (extract, transform, load and extract, load, transform).
Understand the importance of data modeling, and be able to create analytical dbt queries.
Be able to administer cloud data warehouses and data lakes.
Build visualizations in a BI tool such as PowerBI or Tableau.

Concepts

0. Introduction

01 - Introducing the complete guide to analytics engineering
02 - Course contents overview
03 - GitHub Codespaces introduction
04 - CoderPad introduction

1. Overview of Analytics Engineering

05 - Introduction to analytics engineering
06 - Analytics engineering is a hybrid role
07 - The lifecycle of data
08 - The evolution of analytics engineering
09 - Democratization of data and semantic layers

2. Overview of Databases, Data Lakes, and Data Warehouses

10 - Databases, data lakes, and data warehouses oh my
11 - Relational databases
12 - Nonrelational databases
13 - Data warehousing
14 - Data lakes - An alternative storage method
15 - How do databases support decision-making
16 - Database best practices

3. Manipulating Data with Python pandas

17 - What is Python, and why do we use it
18 - Our Python environment and dataset
19 - Kernels, running Python code, and other basics
20 - The pandas Python library
21 - DataFrames, data series, and data types in pandas
22 - Selecting, sorting, and filtering data with pandas
23 - Solving common data type problems with Python pandas
24 - Cleaning data with pandas
25 - CoderPad solution - Solve an analytical task with Python

4. Analyzing Data with Python pandas

26 - Analytical functions in pandas
27 - Grouping data in pandas
28 - Merging multiple data frames with pandas
29 - Creating new custom calculated columns with pandas
30 - Creating rolling averages with window calculations
31 - CoderPad solution - Calculate averages with Python window functions

5. Leveraging Python to Extract Transform Load

32 - Why move data using ETLs
33 - ETL vs. ELT
34 - Connecting to our database and API
35 - Data fetching
36 - Merging and overwriting existing data
37 - DAGs
38 - Benefits and drawbacks of ETL tools

6. Modeling Data with SQL

39 - Introduction to SQL for analytics engineering
40 - The SELECT statement
41 - Filtering data results with the WHERE clause
42 - Aggregate functions in SQL
43 - SQL date functions
44 - Inner joining multiple tables
45 - Left joining multiple tables
46 - Other types of SQL joins
47 - Common table expressions
48 - CoderPad solution - Modeling data with SQL

7. dbt

49 - What is dbt
50 - Semantic layers for modern data workflows
51 - DAGs to visualize data model layers
52 - Building, running, and testing
53 - dbt macros

8. Visualizing Data with Business Intelligence Tools

54 - Introduction to business intelligence
55 - Tableau setup
56 - Connecting to different data sources in Tableau
57 - Dimensions vs. measures
58 - Creating calculated fields
59 - Use the right chart for visualization
60 - Blending multiple data sources together
61 - Crafting an interactive dashboard
62 - Data visualization best practices

9. Working with Stakeholders

63 - Why analytics engineering teams need stakeholder relationships
64 - Concise, quick communication
65 - Managing a project from start to finish
66 - Ensuring your stakeholders use your product

Conclusion

67 - Brief overview of topics covered
68 - What to do next