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Course Outline
Introduction
- Python versatility: from data analysis to web crawling
Python Data Structures and Operations
- Integers and floats
- Strings and bytes
- Tuples and lists
- Dictionaries and ordered dictionaries
- Sets and frozen sets
- Data frame (pandas)
- Conversions
Object-Oriented Programming with Python
- Inheritance
- Polymorphism
- Static classes
- Static functions
- Decorators
- Other
Data Analysis with Pandas
- Data cleaning
- Using vectorized data in pandas
- Data wrangling
- Sorting and filtering data
- Aggregate operations
- Analyzing time series
Data Visualization
- Plotting diagrams with matplotlib
- Using matplotlib from within pandas
- Creating quality diagrams
- Visualizing data in Jupyter notebooks
- Other visualization libraries in Python
Vectorizing Data in Numpy
- Creating Numpy arrays
- Common operations on matrices
- Using ufuncs
- Views and broadcasting on Numpy arrays
- Optimizing performance by avoiding loops
- Optimizing performance with cProfile
Processing Big Data with Python
- Building and supporting distributed applications with Python
- Data storage: Working with SQL and NoSQL databases
- Distributed processing with Hadoop and Spark
- Scaling your applications
Extending Python (and vice versa) with Other Languages
- C#
- Java
- C++
- Perl
- Others
Python Multi-Threaded Programming
- Modules
- Synchronizing
- Prioritizing
Data Serialization
- Python object serialization with Pickle
UI Programming with Python
- Framework options for building GUIs in Python
- Tkinter
- Pyqt
Python for Maintenance Scripting
- Raising and catching exceptions correctly
- Organizing code into modules and packages
- Understanding symbol tables and accessing them in code
- Picking a testing framework and applying TDD in Python
Python for the Web
- Packages for web processing
- Web crawling
- Parsing HTML and XML
- Filling web forms automatically
Summary and Next Step
Requirements
- Beginner to intermediate programming experience
- Knowledge of math and statistics
- Knowledge of database concepts
Audience
- Developers
28 Hours
Testimonials (7)
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.
Felicia Rezanda - HP Singapore (Private) Ltd.
Course - Advanced Python - 4 Days
The first 2 days were very informative. it gets messy when you get into frameworks because every projects has its own goals and requirements and sometimes the 'popular' framework isn't suitable.
Raphael Treccani-Chinelli - Nordic Semiconductor ASA
Course - Advanced Python - 4 Days
Very good overview about python on a lot of area of usage.
János Dóra - Robert Bosch Kft.
Course - Advanced Python
The prepared Jupiter Notebook examples were really good. Plenty of explanations for later, offline use, and we didn't have to spend half of the training copying the examples.
Csongor Miklos - Robert Bosch Kft.
Course - Advanced Python
I liked the most Jorge's attitude, and his experience in python. The greatest topic for me was the Machine Learning topic.