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Python for Data Engineers

2h 21m 18s
English
Paid

If you want to take your skills in Data Engineering to the next level, you are in the right place. Python has become the main language for data analysis and machine learning. In this course, you will learn how to use it effectively to create reliable data pipelines and process data efficiently.

Course Overview

This comprehensive training program is crafted for data engineers at any stage. Whether you're just starting your journey in data engineering or are already experienced and wish to expand your skillset, this course will equip you with all necessary tools for success.

Why Python for Data Engineering?

Python's flexibility and extensive libraries make it a preferred choice for data engineers. By mastering Python, you will be well-prepared to handle complex data tasks and contribute to real-world projects with confidence.

Learning Outcomes

Upon completing the training, you will have a solid foundation in Python and Data Engineering. You'll be ready to tackle complex tasks and deliver value in real projects.

Key Skills You Will Learn

  • Utilize advanced features of Python
  • Transform data using the pandas library
  • Integrate with APIs and work with PostgreSQL databases, dates, and JSON
  • Master object-oriented programming, including classes, objects, and data validation
  • Develop competencies in writing unit tests and handling exceptions
  • Apply modules and leverage NumPy for numerical computations

About the Author: Andreas Kretz

Andreas Kretz thumbnail

I am a senior data engineer and trainer, a tech enthusiast, and a father. For more than ten years, I have been passionate about Data Engineering. Initially, I became a self-taught data engineer and then led a team of data engineers at a large company. When I realized the great demand for education in this field, I followed my passion and founded my own Data Engineering Academy. Since then, I have helped over 2,000 students achieve their goals.

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#1: Introduction
All Course Lessons (19)
#Lesson TitleDurationAccess
1
Introduction Demo
01:51
2
Classes
04:38
3
Modules
03:07
4
Exception-handling
08:56
5
Logging
05:13
6
Datetime
08:05
7
JSON
09:55
8
JSON Validation
15:11
9
UnitTesting
16:45
10
Pandas: Intro & data types
08:44
11
Pandas: Appending & Merging DataFrames
07:50
12
Pandas: Normalizing & Lambdas
04:13
13
Pandas: Pivot & Parquet write, read
06:18
14
Pandas: Melting & JSON normalization
08:16
15
Numpy
04:48
16
Requests (Working with APIs)
11:16
17
Working with Databases: Setup
04:07
18
Working with Databases: Tables, bulk load, queries
08:13
19
Conclusion
03:52
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