Python for Data Engineering: from Beginner to Advanced

Python for Data Engineering: from Beginner to Advanced

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 50m | 412 MB

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.

Table of Contents

1 Welcome to the course
2 What you should know
3 CoderPad tour

Python Basics
4 Introduction to Python and data engineering
5 Setting up your Python environment
6 Explore a Google Colab worksheet
7 Variables and data types
8 Operators and expressions
9 Control structures
10 Functions
11 Modules and packages
12 String manipulation
13 Error handling
14 Solution Conditions

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

Python File Handling
21 File IO overview
22 Working with CSV files
23 Working with JSON files
24 Solution File handling

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

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

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

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

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

51 Next steps