Course Outline
Introduction
- Versatility of Python for tasks ranging from data analysis to automated web extraction
Data Structures and Operations in Python
- Integers and floating-point numbers
- String and byte handling
- Tuples and lists
- Dictionaries and ordered dictionaries
- Sets and immutable sets
- Data frames (pandas)
- Type conversions
Object-Oriented Programming Principles in Python
- Inheritance mechanisms
- Polymorphism
- Static classes
- Static methods
- Decorators
- Additional concepts
Data Analysis Using Pandas
- Data cleansing techniques
- Leveraging vectorized operations in pandas
- Data manipulation
- Sorting and filtering datasets
- Aggregate functions
- Time series analysis
Data Visualization Techniques
- Generating plots with matplotlib
- Integrating matplotlib within pandas
- Creating high-quality visualizations
- Rendering data in Jupyter notebooks
- Alternative Python visualization libraries for government reporting
Data Vectorization with Numpy
- Constructing Numpy arrays
- Standard matrix operations
- Utilizing universal functions (ufuncs)
- Array views and broadcasting
- Performance optimization by eliminating loops
- Profiling performance using cProfile
Processing Large-Scale Data with Python
- Developing and maintaining distributed applications in Python for federal systems
- Data management: Interacting with SQL and NoSQL databases
- Distributed computing via Hadoop and Spark
- Application scalability strategies
Interoperability with Other Languages
- C# integration
- Java integration
- C++ integration
- Perl integration
- Other language bindings
Multi-Threaded Programming in Python
- Module management
- Synchronization techniques
- Thread prioritization
Data Serialization Standards
- Serializing Python objects using Pickle
User Interface Development with Python
- GUI framework options for Python applications
- Tkinter
- Pyqt
Maintenance Scripting in Python
- Proper exception handling and raising protocols
- Structuring code into modules and packages for government projects
- Understanding symbol tables and programmatic access
- Selecting testing frameworks and implementing Test-Driven Development (TDD) in Python
Web Technologies in Python
- Packages for web service processing
- Automated web scraping
- Parsing HTML and XML data structures
- Automating web form submission
Summary and Future Directions
Requirements
- Familiarity with programming fundamentals through intermediate proficiency
- Fundamental understanding of mathematical and statistical principles
- Comprehension of core database architecture
Target Audience
- Software engineers and 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.