Course Outline
Introduction
- The versatility of Python: spanning from data analytics to web scraping.
Python Data Structures and Core Operations
- Handling integers and floating-point numbers.
- Working with strings and byte sequences.
- Utilizing tuples and lists.
- Managing dictionaries and ordered dictionaries.
- Implementing sets and frozen sets.
- Working with pandas DataFrames.
- Data type conversions.
Object-Oriented Programming in Python
- Concepts of inheritance.
- Polymorphism techniques.
- Defining static classes.
- Implementing static functions.
- Using decorators.
- Additional OOP concepts.
Data Analysis Using Pandas
- Techniques for data cleansing.
- Leveraging vectorized data within pandas.
- Data wrangling strategies.
- Sorting and filtering datasets.
- Performing aggregate calculations.
- Time series analysis.
Data Visualization
- Generating plots with matplotlib.
- Integrating matplotlib with pandas.
- Creating high-quality visual diagrams.
- Visualizing data directly in Jupyter notebooks.
- Exploring other Python visualization libraries.
Vectorization with NumPy
- Constructing NumPy arrays.
- Standard matrix operations.
- Utilizing universal functions (ufuncs).
- Array views and broadcasting mechanics.
- Performance optimization through loop avoidance.
- Performance profiling using cProfile.
Big Data Processing with Python
- Developing and maintaining distributed applications.
- Data persistence: Interfacing with SQL and NoSQL databases.
- Distributed computing using Hadoop and Spark.
- Strategies for application scalability.
Interoperability with Other Languages
- Integration with C#.
- Integration with Java.
- Integration with C++.
- Integration with Perl.
- Interfacing with other languages.
Multi-Threaded Python Programming
- Module management for threading.
- Thread synchronization techniques.
- Thread prioritization.
Data Serialization
- Serializing Python objects using the Pickle module.
UI Development with Python
- Options for GUI frameworks:
- Tkinter.
- PyQt.
Python for Maintenance Scripting
- Proper exception handling patterns.
- Structuring code into modules and packages.
- Managing symbol tables and programmatic access.
- Selecting testing frameworks and implementing TDD in Python.
Python for Web Applications
- Libraries for web data processing.
- Automated web crawling.
- Parsing HTML and XML structures.
- Automating web form interactions.
Conclusion and Path Forward
Requirements
- Familiarity with programming from beginner to intermediate levels.
- Understanding of mathematical and statistical concepts.
- Basic knowledge of database principles.
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.
