English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 5h 26m | 581 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
Introduction
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 the 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 a DataFrame to a file
31 Solution pandas
NumPy
32 Introduction to NumPy
33 Array creation and attributes
34 Array operations
35 Indexing and slicing
36 Linear algebra and statistics
37 Write a NumPy array to a file
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 Understand PySpark for data engineering
46 What is Hadoop
47 Importance of visualization tools in data engineering
48 On-premises vs. cloud data engineering
Web Scraping with Python
49 HTML basics
50 HTML parents, children, and descendants
51 Understand web scraping
52 BeautifulSoup basics
53 Installing BeautifulSoup
54 Get HTML from a web page
55 Scrape the web page
56 Export data as a TXT file
Advanced Built-in Functions
57 Generators in Python
58 Python generator classes and iterators
59 Iterables in Python
60 filter() and map() functions
61 any() and all() functions in Python
Logging in Python
62 What is logging
63 Custom logging
64 Logging best practices
Capstone Project
65 Capstone Project Retail sales analysis
66 Solution Capstone project
Conclusion
67 Next steps
Resolve the captcha to access the links!