In Data Engineering, programming plays a key role. Whether you work with interfaces, databases, or are involved in data transformation and validation, programming skills are essential for every data engineer.
Introduction to Python
1h 18m 14s
English
Paid
Why Python?
Python is a programming language that has deservedly become extremely popular in recent years. Particularly in data processing, it is the dominant language because engineering tasks often overlap with Data Science tasks.
Unlike Java, Python is easier to learn, and therefore, it often becomes the first programming language for beginners.
What You Will Learn
The instructor Amit Jain will provide you with a basic understanding of Python:
- What Python is and how to perform simple mathematical operations with it
- What strings are and why they are useful
- How to use variables, logical (boolean) expressions, and loops
- How to reuse code with functions
- What lists, dictionaries, and modules are
- How to work with JSON and CSV file formats
- And, of course, how to write clean and understandable code in Python
About the Author: Amit Jain
Amit Jain is a software engineer and educator publishing introductory programming material on Python — one of the most beginner-friendly modern languages and the dominant choice for picking up programming from scratch.
His CourseFlix listing carries Introduction to Python — a foundational course covering language syntax, data structures, control flow, and the patterns that prepare students to move into Python's broader ecosystem (web, data, automation, ML).
Material is paid and aimed at absolute beginners. For the next steps after Python fundamentals, see CourseFlix's Python category page where this course sits alongside material from Real Python, Tech With Tim, and the wider Python teaching community.
Watch Online 15 lessons
0:00
/ #1: Course Introduction
All Course Lessons (15)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Course Introduction Demo | 02:12 | |
| 2 | What is programming | 05:42 | |
| 3 | Python Development Environment | 05:18 | |
| 4 | main.py | 04:10 | |
| 5 | Math expressions | 03:02 | |
| 6 | Strings | 06:07 | |
| 7 | Strings-demo | 07:32 | |
| 8 | Variables | 05:36 | |
| 9 | Loops | 03:46 | |
| 10 | Functions | 07:45 | |
| 11 | Lists and Tuples | 05:06 | |
| 12 | Dictionaries and Sets | 04:01 | |
| 13 | Indexing and Slicing | 05:05 | |
| 14 | Reading/Writing json | 05:50 | |
| 15 | Reading/Writing csv | 07:02 |
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Frequently asked questions
What prior knowledge or skills do I need before taking this course?
This course is designed for beginners, so no prior programming experience is necessary. However, familiarity with basic computer operations and a general understanding of data engineering concepts may be helpful. The course starts with fundamental programming principles and gradually introduces Python-specific topics.
What types of projects or exercises will I work on during the course?
Throughout the course, students will engage with practical exercises such as working with math expressions, manipulating strings, and handling data structures like lists, tuples, dictionaries, and sets. Additionally, students will learn to read and write JSON and CSV files, which are common tasks in data engineering.
Who is the target audience for this course?
The course is targeted at individuals interested in data engineering who are new to programming or those looking to transition into a role that requires programming skills. It is also suitable for anyone who wants to learn Python from scratch with a focus on data-related applications.
What specific Python tools or platforms will I learn to use in this course?
Students will become familiar with the Python development environment, including setting up and running Python scripts through 'main.py'. The course covers essential Python tools such as loops, functions, and data structures that are crucial for data manipulation tasks.
What topics are not covered in this course?
The course does not cover advanced Python topics such as object-oriented programming, Python libraries for data analysis like Pandas or NumPy, or Python's application in machine learning. It focuses on foundational programming concepts and basic data handling techniques.
How much time should I expect to commit to this course?
The course consists of 15 lessons, and while the total runtime is not specified, students should allocate sufficient time to engage with lesson materials and complete exercises. A typical beginner might spend several hours per lesson, factoring in time for practice and review.
How will the skills learned in this course benefit my career in data engineering?
The programming skills acquired in this course are fundamental for data engineering. Understanding how to manipulate data structures, read/write JSON and CSV files, and perform basic data transformations are essential tasks in data engineering roles. These skills provide a solid foundation for more advanced studies and practical applications in the field.