Data Engineering 101

1. Introduction to Data Engineering: Overview of the role and importance of data engineering in the data science and analytics ecosystem.

2. Data Storage Technologies: Basics of different data storage technologies such as relational databases, NoSQL databases, and data warehouses.

3. Data Processing Techniques: Introduction to data processing techniques including transformation, aggregation, and filtering.

4. Data Pipeline Architecture: Overview of data pipeline architecture, design patterns, and best practices.

Before learning Data Engineering 101, it's beneficial to have these skills:

1. Basic Computer Skills: Proficiency in using computers and navigating software interfaces.

2. Understanding of Data Concepts: Familiarity with basic data concepts such as databases, tables, and queries.

3. Basic Programming Knowledge: Understanding of basic programming concepts such as variables, loops, and functions.

4. Mathematics and Statistics: Basic understanding of mathematics and statistics concepts used in data analysis.

By learning Data Engineering 101, you gain the following skills:

1. Data Storage: Understanding of different data storage technologies and their use cases.

2. Data Processing: Knowledge of data processing techniques such as transformation, aggregation, and filtering.

3. Data Integration: Ability to integrate data from various sources and systems.

4. Data Pipeline Design: Skill in designing and implementing data pipelines for data processing and analysis.