Python for Data Engineering: from Beginner to Advanced

Course details

Get up and running with the basics of Python before progressing to more advanced topics specific to data engineering. In this hands-on, interactive course, join instructor Deepak Goyal to practice performing a wide range of data engineering tasks in Python to boost your technical know-how, prepare for an interview, or land a new role. This course includes Code Challenges powered by CoderPad. Code Challenges are interactive coding exercises with real-time feedback, so you can get hands-on coding practice to advance your coding skills. Deepak helps you boost your skills as a Python programmer with six specific coding challenges. Explore language basics, Python collections, file handling, Pandas, NumPy, OOP, and advanced data engineering tools that use Python. The course ends with a capstone project focused on retail sales analysis.

Concepts

0. Introduction

01 - Welcome to the course
02 - What you should know
03 - CoderPad tour

1. Python Basics

04 - Introduction to Python and data engineering
05 - Setting up your Python environment
06 - Explore a Google Colab worksheet
07 - Variables and data types
08 - Operators and expressions
09 - Control structures
10 - Functions
11 - Modules and packages
12 - String manipulation
13 - Error handling
14 - Solution - Conditions

2. Python Collections

15 - Collection overview
16 - Python collections - Tuples
17 - Python collections - Lists
18 - Python collections - Sets
19 - Python collections - Dictionaries
20 - Solution - Collections

3. Python File Handling

21 - File I O overview
22 - Working with CSV files
23 - Working with JSON files
24 - Solution - File handling

4. pandas DataFrame API

25 - Introduction to pandas
26 - Read files as DataFrames
27 - Data cleaning and preprocessing
28 - Data manipulation and aggregation
29 - Data visualization
30 - Write DataFrames as files
31 - Solution - pandas

5. NumPy

32 - Introduction to NumPy
33 - Array creation and attributes
34 - Array operations
35 - Indexing and slicing
36 - Linear algebra and statistics
37 - Write DataFrames as files
38 - Solution - NumPy

6. OOP with Python

39 - Understanding classes and objects
40 - Implementation - Classes and objects in Python
41 - Understand OOP features - Abstraction, inheritance, and more
42 - Solution - OOP

7. Advanced Data Engineering

43 - Tips to write efficient Python code
44 - What is ETL in the data engineering world
45 - What is Hadoop
46 - Understand PySpark for data engineering
47 - Importance of visualization tools in DE
48 - On-prem vs. cloud data engineering

8. Capstone Project

49 - Capstone project - Retail sales analysis
50 - Solution - Capstone project

Conclusion

51 - Next steps