CF

Python for Data Engineers

2h 21m 18s
English
Paid

Python for Data Engineers is a 19-lesson 2 hours 21 minutes self-paced course by Andreas Kretz. If you want to take your skills in Data Engineering to the next level, you are in the right place.

Course facts

Lessons
19
Duration
2 hours 21 minutes
Level
All levels
Language
English
Updated
Instructor
Andreas Kretz
Price
Premium

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

Additional

https://github.com/team-data-science/python2

Who teaches Python for Data Engineers? Andreas Kretz

Andreas Kretz thumbnail

Andreas Kretz is a German data engineer and one of the most widely followed independent voices on data engineering as a career discipline. He runs the Plumbers of Data Science brand and has been publishing tutorial material continuously since the field consolidated around the modern lake-house stack (Spark, Kafka, Snowflake, Databricks, Airflow).

His CourseFlix listing is the largest single-author catalog under this source — over thirty courses spanning data-pipeline construction, streaming architectures, the cloud-native data stack on AWS / Azure / GCP, the Python and Scala tooling that dominates the field, and the soft-skills / career side of breaking into data engineering. Material is paid and aimed at engineers transitioning into data work or already-working data engineers picking up specific tools.

What lessons are included in Python for Data Engineers?

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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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Frequently asked questions

What prerequisites are needed before starting this course?
This course is designed for data engineers at various stages, so no specific prerequisites are required. However, a basic understanding of programming concepts and data structures would be beneficial. Familiarity with any programming language, particularly Python, will help you grasp the course material more effectively.
What types of projects or tasks will I learn to build?
Throughout the course, you'll learn to create reliable data pipelines and process data efficiently using Python. You will work with tools like the pandas library for data transformation, integrate with APIs using the Requests library, and interact with PostgreSQL databases. You'll also practice writing unit tests and handling exceptions to ensure reliable code.
Who is the target audience for this course?
The course is aimed at data engineers at any stage of their career. Whether you are a beginner looking to enter the field or an experienced professional seeking to enhance your skills in Python and data engineering, this course will provide valuable insights and hands-on practice.
How does the depth of this course compare to other data engineering courses?
This course covers Python's advanced features and its application in data engineering comprehensively. It includes lessons on pandas, NumPy, API integration, and working with PostgreSQL databases. The inclusion of object-oriented programming and unit testing sets it apart by providing a strong foundation in both Python programming and practical data engineering tasks.
What specific tools and platforms are covered in this course?
The course covers a range of Python tools and libraries essential for data engineering. You will learn about pandas for data manipulation, NumPy for numerical computations, and the Requests library for working with APIs. Additionally, you'll gain experience with PostgreSQL databases and JSON for data interchange.
What topics or tools are not covered in this course?
While the course provides a comprehensive overview of Python in data engineering, it does not cover machine learning algorithms or tools specifically designed for big data processing such as Apache Spark. The focus is primarily on building data pipelines and processing data using Python.
How can the skills learned in this course be applied to other areas or careers?
The skills acquired in this course, such as using Python for data manipulation, working with databases, and integrating with APIs, are highly transferable. These competencies are valuable in various fields like data analysis, machine learning, and software development. Mastery of Python and its libraries can open opportunities in any domain requiring data processing and automation.