Python Intermediate - Training Course
Use Python for efficient Data Analysis
Use Python for efficient Data Analysis
4.70
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.
Upon completion of this course you will be able to:
Students should have attended our Python Beginner course or have a foundational knowledge of Python and pandas.
Python BeginnerLearn Python with this beginner-friendly Python course. Get started with Python programming, JupyterLab, and data analysis using pandas, no prior coding experience required.
Python AdvancedAdvance your data skills with our Python advanced course. Automate analysis, write efficient Python code, and build interactive dashboards using pandas and Bokeh.
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
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.
Do I need to complete Python Beginner before taking this course?
Yes, we recommend completing our Python Beginner course first, or having equivalent knowledge. You should be familiar with Python syntax, working in JupyterLab, and using pandas DataFrames before enrolling in the Intermediate course.
What will I learn in Python Intermediate?
This course builds on the fundamentals and introduces more advanced techniques, including creating user-defined functions, lambda expressions, list comprehensions, handling missing data, conditional formatting with pandas styles, importing and manipulating datasets, and basic modelling with scikit-learn.
Will I learn to work with real-world datasets?
Yes. You’ll gain hands-on experience working with realistic datasets. You’ll practise importing data, cleaning and transforming it, exploring relationships using visualisations, and even building simple predictive models using scikit-learn.
What libraries are used in this course?
In addition to Python’s built-in functionality, the course uses key data science libraries including pandas, NumPy, Matplotlib, Seaborn, and scikit-learn. These are all commonly used in professional Python workflows.
Will I learn to create my own functions?
Absolutely. A key part of the course focuses on user-defined functions, including parameters, scope, *args and **kwargs, and lambda expressions. You’ll also explore how to write reusable and efficient code.
Do I need to install anything before class?
No. We’ll provide guidance on setting up your environment using Anaconda. If you’ve already completed our Beginner course, you’ll already have everything installed.
Python BeginnerLearn Python with this beginner-friendly Python course. Get started with Python programming, JupyterLab, and data analysis using pandas, no prior coding experience required.
Python AdvancedAdvance your data skills with our Python advanced course. Automate analysis, write efficient Python code, and build interactive dashboards using pandas and Bokeh.
At Nexacu, we are proud to be the trusted training partner for hundreds of leading organisations across Australia, New Zealand, and around the world. From government agencies to multinational corporations, we help teams build practical skills and achieve real outcomes through expert-led training.
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