Advanced Analytics Engineering: Real-World Practice

Course details

This course shows engineers they can reach a new level in their work. Instructor Connor Dickson shows you how to tackle topics that stump some of the best engineers, such as unstructured and array data and how to work through problematic data. Plus, learn skills that will help you stand out from the crowd, like the ability to make tough decisions around data and infrastructure and effective communication processes.

Learning objectives
Troubleshoot problematic data (missing data, inconsistent data, changing data types).
Work with common and rare data types, including JSON, XML, YAML, double precision, unstructured data, and time series data.
Learn data encryption techniques for working with personal identifying information.
Learn decision-making and communication processes.
Build common database schemas (flat, relational, star, and snowflake schemas).

Concepts

0. Introduction

01 - Introduction to advanced analytics engineering
02 - GitHub Codespaces introduction
03 - Introduction to CoderPad

1. Database Schema

04 - What are database schema
05 - Flat schema
06 - Relational schema
07 - Star schema
08 - Snowflake schema

2. Advanced SQL Practice

09 - Advanced SQL techniques
10 - Recursive common table expressions (CTEs) and when to use them
11 - Improving query performance by indexing tables
12 - Updating database tables
13 - Window functions
14 - Solution - Time series data analysis with Python

3. Complex and Problematic Data

15 - Common stumbling blocks in analytics engineering
16 - Working with JSON files and fields
17 - XMLs and their uses
18 - Time series data
19 - Dealing with missing data in SQL
20 - Solution - Create tests for data with Python
21 - Solution - Create tests for data with SQL

4. Decision-Making in Analytics Engineering

22 - Decision-making introduction
23 - Getting to the root of the problem
24 - Investigating the data
25 - Planning data projects
26 - Avoiding tech debt

5. Protecting Data

27 - Why protect and encrypt data
28 - Handling sensitive data
29 - Encrypting data for protection
30 - Protecting your database
31 - Legal and ethical considerations

6. Communication Best Practices

32 - Communication facilitates analytics engineering
33 - Working with stakeholders
34 - Project management for analytics engineering
35 - Leading analytics engineering teams
36 - Data dictionaries

Conclusion

37 - Brief overview of topics covered
38 - What to do next