Python Intermediate - Training Course
Use Python for efficient Data Analysis
Following on from our Python Beginner course, Python Intermediate will build on your foundational knowledge of Python and pandas. You will learn how to manipulate data, create custom functions, plot with Matplotlib and display visualisations.
Understanding how to use Python for Data Analysis empowers you to be much more efficient and opens up the possibility of using a wide array of freely available tools. Read our full course outline below.
We are currently developing these courses. Please contact us to register your interest.
We are currently developing these courses. Please contact us to register your interest.
We are currently developing these courses. Please contact us to register your interest.
We are currently developing these courses. Please contact us to register your interest.
We are currently developing these courses. Please contact us to register your interest.
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.
"Great trainer, structure, material and manual. I have completed programming courses before but this one just made more sense!" - Using R Beginner Sydney
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 visualisations. 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 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 the 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 Course Details
Python Intermediate Course Details
Python Intermediate Course Details
Python Intermediate Course Details
Python Intermediate Course Details
Python Intermediate Course Details
Python Intermediate Course Details
Python Intermediate Course Details
Python Intermediate Course Details
Python Course Outlines
Python City Pages
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What do I need to know to attend?
Students should have attended our Python Beginner course or have a foundational knowledge of Python and pandas.
Python Intermediate Learning Outcomes
Upon completion of this course you will be able to:
- use the extensive data manipulation capabilities of pandas DataFrames
- customise the display of the output in Jupyter Notebooks
- use the plotting capabilities of Matplotlib to plot distributions and bar charts
- use the data visualisation library, Seaborn, and
- fit a basic model using scikit-learn.
Python Intermediate Course Content
- Introduction
- Python Intermediate
- User-Defined Functions in Python
- Function basics
- Parameters
- Positional vs keyword arguments
- Defining a function
- Indentation
- User-Defined Functions in Python (cont'd)
- Scope
- *args and **kwargs
- Unpacking operators
- Lambda expressions
- Conditional expressions
- List comprehensions
- Modify the DataFrame Display
- pandas options
- Working with pandas styles
- Applying a style that is not dependent on values
- Formatting values
- String formats
- Applying a style that is dependent on values
- Built-in conditional formatting
- Export Notebook as
- Export to PDF or HTML
- Create slides
- Copy vs View
- Setting with copy warning
- Working with Missing Values
- Missing values
- inf and -inf
- Removing missing values
- Replacing missing values
- Importing Data
- Importing into a pandas DataFrame
- Manipulating Data
- Summarise a dataset
- Report and display multiple summary statistics
- Ordering data
- Manipulating Data (cont'd)
- Working with dates
- Add columns with assign()
- Working with strings
- Reordering and dropping columns
- Manipulating Data (cont'd)
- Selecting rows based on values
- Grouping and summarising data
- Replacing values
- Concatenate data
- Bin continuous variables into categories
- Working with Relational Data
- Joining data from two DataFrames
- Visualising Distributions
- Visual representation of distributions with Matplotlib and Seaborn
- Histograms
- Boxplots
- Bar and column charts
- Multivariate Analysis
- Scatterplot matrix
- Bar and column charts
- Basic Modelling
- Create a linear model with scikit-learn