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Complete Guide to Python for Data Engineering: From Beginner to Advanced

سرفصل های دوره

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.


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

  • 02 - 1. Python Basics
  • 01 - Introduction to Python and data engineering
  • 02 - Setting up your Python environment
  • 03 - Explore the Google Colab worksheet
  • 04 - Variables and data types
  • 05 - Operators and expressions
  • 06 - Control structures
  • 07 - Functions
  • 08 - Modules and packages
  • 09 - String manipulation
  • 10 - Error handling
  • 11 - Solution Conditions

  • 03 - 2. Python Collections
  • 01 - Collection overview
  • 02 - Python collections Tuples
  • 03 - Python collections Lists
  • 04 - Python collections Sets
  • 05 - Python collections Dictionaries
  • 06 - Solution Collections

  • 04 - 3. Python File Handling
  • 01 - File IO overview
  • 02 - Working with CSV files
  • 03 - Working with JSON files
  • 04 - Solution File handling

  • 05 - 4. pandas DataFrame API
  • 01 - Introduction to pandas
  • 02 - Read files as DataFrames
  • 03 - Data cleaning and preprocessing
  • 04 - Data manipulation and aggregation
  • 05 - Data visualization
  • 06 - Write a DataFrame to a file
  • 07 - Solution pandas

  • 06 - 5. NumPy
  • 01 - Introduction to NumPy
  • 02 - Array creation and attributes
  • 03 - Array operations
  • 04 - Indexing and slicing
  • 05 - Linear algebra and statistics
  • 06 - Write a NumPy array to a file
  • 07 - Solution NumPy

  • 07 - 6. OOP with Python
  • 01 - Understanding classes and objects
  • 02 - Implementation Classes and objects in Python
  • 03 - Understand OOP features Abstraction, inheritance, and more
  • 04 - Solution OOP

  • 08 - 7. Advanced Data Engineering
  • 01 - Tips to write efficient Python code
  • 02 - What is ETL in the data engineering world
  • 03 - Understand PySpark for data engineering
  • 04 - What is Hadoop
  • 05 - Importance of visualization tools in data engineering
  • 06 - On-premises vs. cloud data engineering

  • 09 - 8. Web Scraping with Python
  • 01 - HTML basics
  • 02 - HTML parents, children, and descendants
  • 03 - Understand web scraping
  • 04 - BeautifulSoup basics
  • 05 - Installing BeautifulSoup
  • 06 - Get HTML from a web page
  • 07 - Scrape the web page
  • 08 - Export data as a TXT file

  • 10 - 9. Advanced Built-in Functions
  • 01 - Generators in Python
  • 02 - Python generator classes and iterators
  • 03 - Iterables in Python
  • 04 - filter() and map() functions
  • 05 - any() and all() functions in Python

  • 11 - 10. Logging in Python
  • 01 - What is logging
  • 02 - Custom logging
  • 03 - Logging best practices

  • 12 - 11. Capstone Project
  • 01 - Capstone Project Retail sales analysis
  • 02 - Solution Capstone project

  • 13 - Conclusion
  • 01 - Next steps
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