Python Training - Intermediate - Hong Kong
Learn efficient Python programming for data analysis.
Python Training - Intermediate - Hong Kong
Frequently Asked Questions
Meet your Trainer
Tamara Shatar holds a PhD in Agricultural Data Science. She has extensive experience, including many years working as a research scientist focused on data analysis, modelling using machine learning, simulation and other techniques. While working in both academia and at the Australian CSIRO, part of her role included teaching a variety of data analysis skills. She is consistently well reviewed by her students.
"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
Python Course Outlines
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Python Basics
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Data Analysis in Python
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Python Intermediate
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Python Certification
Skills Test
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What do I need to know to attend?
Python Intermediate Hong Kong Learning Outcomes
- 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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What is Remote Training?
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Login from your desk -
Instructor-led Training -
Your Questions Answered -
Innovative Content -
Microsoft Certified Trainers
