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Python Training - Intermediate - Hong Kong
Learn efficient Python programming for data analysis.
Following on from our Data Analysis in Python course, Intermediate Python for Data Analysis will build on your knowledge of Python and pandas. The focus of this course is learning to work more efficiently in Python. You will learn to use control flow structures and loops and write your own custom functions and classes to automate analyses and improve efficiency. All classes delivered remotely, join us live online from Hong Kong.
Python Training - Intermediate - Hong Kong
Following on from our Data Analysis in Python course, Intermediate Python for Data Analysis will build on your knowledge of Python and pandas. Learn to use control flow structures and loops and write your own custom functions and classes to automate analyses and improve efficiency. Remote courses offered in Hong Kong.
Frequently Asked Questions
Course Introduction
Our Python courses were designed by Tamara Shatar, who holds a PhD in Agricultural Data Science. She focused her extensive experience and skills in modelling using machine learning, simulation and other techniques to create a course with depth and applicability.
The course is consistently well reviewed by students.
"Very well presented and structured. Thank you for such a fruitful day." - Python Basics, Sydney
"Great trainer, structure, material and manual. I have completed programming courses before but this one just made more sense! I'm sure it was the trainer. Looking forward to the Intermediate course when it is available." -Using R Beginner Sydney, Australia
Is Python compatible with Microsoft?
Python can be used within a number of Microsoft products. In Power BI, reusable Python scripts can be used to manipulate data and create visualizations. In Azure, machine learning models can be written in Python and trained and deployed within the Azure Machine Learning Workspace.
What is Remote Training?
Remote training at Nexacu, means our team of experienced trainers will deliver your training virtually. With remote learning students can access our usual classroom training courses via video conferencing, ask questions, participate in discussion and share their screen with the trainer if they need help at any point in the course. Students have the same level of participation and access to the trainer as they would in classroom training sessions.
Python Intermediate Hong Kong Course Details
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What do I need to know to attend?
Students should have attended these courses or be very familiar with the concepts covered in them. You will not be expected to write code from scratch but having these skills will better enable them to engage with the content.
Minimum requirements:
- comfortable working in Python and with
- Python built-in data structures
- lists, dictionaries, tuples and sets.
- pandas DataFrames
- DataFrame methods
- Basic familiarity with Matplotlib
Python Intermediate Hong Kong Learning Outcomes
In this course, you will learn to:
- create user-defined functions and classes
- use loops and other control structures, plus alternatives
- use method chaining and pipes to perform multiple operations on DataFrames
- create interactive visualisations with Bokeh
- write code to automate these processes
Python Intermediate Hong Kong Course Content
- Introduction
- Working more efficiently in Python
- Automating frequent data analysis operations
- Principles of working more efficiently with code
- User-Defined Functions
- When to create your own functions
- Function basics
- Parameters
- Positional vs keyword arguments
- Defining a function
- Indentation
- Scope
- *args and **kwargs
- Unpacking operators
- Order of arguments in a function
- Adding a docstring
- Assertions
- Loafing functions for reuse
- lambda expressions
- Loops and Other Control Structures
- if…elif...else
- for loops
- Loop over sequences
- Loop over ranges
- Enumerate
- Loop over pandas groups
- Loop over multiple lists while loops
- else, break and continue
- Saving results from a loop
- Combining loops and functions
- Loop and if-else alternatives
- np.where() and np.select()
- Conditional expressions
- List comprehensions
- Python built-in map() function
- Evaluating performance efficiency
- IPython and magic commands
- pandas map()
- pandas apply() and applymap()
- User-Defined Classes
- When to create your own class
- Defining classes
- Docstrings
- The __init__() method
- The self parameter
- Class objects – attribute references and instantiation
- Data attributes
- Methods
- Scope
- Dunder methods
- Performing multiple operations on DataFrames
- Method chaining
- Using pandas pipes with custom functions
- Interactive Visualisations with Bokeh
- Bokeh basics
- Working with Bokeh in Jupyter
- Glyphs
- Providing data
- Using the Bokeh toolbar
- Customising the Bokeh toolbar
- Creating links between plots
- Using interactive legends
- Add tooltips
- Plotting from a grouped pandas
- DataFrame
- Save your Bokeh chart
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