Course Outline
Introduction
- The breadth of Python: from analytics to web scraping
Python Data Structures and Operations
- Integer and floating-point numbers
- String and byte handling
- Lists and tuples
- Standard and ordered dictionaries
- Sets and immutable sets
- DataFrames (pandas)
- Type conversions
Object-Oriented Programming in Python
- Inheritance mechanisms
- Polymorphism
- Static classes
- Static methods
- Decorators
- Additional OOP concepts
Data Analysis with Pandas
- Data cleaning techniques
- Leveraging vectorized operations in pandas
- Data wrangling
- Sorting and filtering datasets
- Aggregation operations
- Time-series analysis
Data Visualization
- Charting with matplotlib
- Integrating matplotlib with pandas
- Best practices for high-quality visualizations
- Visualizing data within Jupyter notebooks
- Exploring alternative Python visualization libraries
Vectorization with NumPy
- Constructing NumPy arrays
- Standard matrix operations
- Utilizing ufuncs
- Views and broadcasting in NumPy
- Performance optimization by eliminating loops
- Performance profiling with cProfile
Handling Big Data with Python
- Developing and maintaining distributed applications
- Data persistence: Interacting with SQL and NoSQL databases
- Distributed computing with Hadoop and Spark
- Application scaling strategies
Interoperability with Other Languages
- C#
- Java
- C++
- Perl
- Additional language integrations
Multi-Threading in Python
- Module management
- Synchronization techniques
- Task prioritization
Data Serialization
- Object serialization using Pickle
GUI Development with Python
- Framework options for building graphical interfaces
- Tkinter
- PyQt
Python for Maintenance Scripting
- Proper exception handling: raising and catching
- Structuring code into modules and packages
- Navigating symbol tables and code access
- Selecting test frameworks and applying TDD in Python
Python for Web Applications
- Web processing libraries
- Web scraping techniques
- Parsing HTML and XML content
- Automating web form submission
Wrap-Up and Future Steps
Requirements
- Foundational to intermediate programming proficiency
- Familiarity with mathematics and statistics
- Understanding of database concepts
Target Audience
- Software Developers
Testimonials (7)
Got to know a lot of new thngs.
Roland - Diehl Aviation
Course - Advanced Python - 4 Days
We covered the topics in sufficient depth, which gave us time to discuss many of them. It was comprehensive enough.
Gergo - Diehl Aviation
Course - Advanced Python - 4 Days
We got a lot of new informations about Python what we will be able to use in our daily work in the future. The exercises were really interesting and challenging enough.
Zsolt - Diehl Aviation
Course - Advanced Python - 4 Days
training was good overall, my favorite part: dashboard & pyqt
Balazs - Diehl Aviation
Course - Advanced Python - 4 Days
Plenty of examples - and the trainer willing to bend backwards to help us with topics we were weaker in.
Wei Lit Teoh - HP Singapore (Private) Ltd.
Course - Advanced Python - 4 Days
Lots of exercises
Fanny Stauffer - UCB Pharma S.A.
Course - Advanced Python - 4 Days
The trainer gave a clear and systematic teaching. He usually gave the reasoning and fundamental knowledge behind the commands. He also gave us time to do the exercises and practice.
