Python Courses - Basics
Learn the basics of Python programming
Python is a very versatile programming language, broad in its application due to its simplicity and power. While Python can be used for everything from developing websites to programming robots, our courses focus on using Python for data analytics.
Our Python basics course focuses on the fundamentals of working with Python for Data Analysis. We introduce Anaconda and JupyterLab and the basics of Python syntax. You will learn how to work with objects in Python, access and manipulate dataframes, how to use functions and methods and create basic visualisations. Read our full Python Basics course outline below.
Python Courses - Basics
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 CSIRO, part of her role included teaching a variety of data analysis skills. She is consistently well reviewed by her students.
What is Remote Training?
Remote training at Nexacu, means our team of experienced trainers will deliver your training live online. 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. Students have the same level of participation and access to the trainer as they would in classroom training sessions.
What is Python?
Python is a general-purpose programming language that is easy to use and learn. It is open source and free to use, including for commercial use. The Python user community is very active. There are over 237,000 packages which have been contributed, which extend the functionality of the Python Standard Library. These packages contain functions to perform a wide range of tasks.
Python Basics Course Details
Python Basics Course Details
*Limited in-class seats due to COVID-19.
Python Basics Course Details
*Limited in-class seats due to COVID-19.
Python Basics Course Details
*Limited in-class seats due to COVID-19.
Python Basics Course Details
*Limited in-class seats due to COVID-19.
Python Basics Course Details
*Limited in-class seats due to COVID-19.
Python Basics Course Details
*Limited in-class seats due to COVID-19.
Python Basics Course Details
*Limited in-class seats due to COVID-19.
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
Python City Pages
Skills Test
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What do I need to know to attend?
It is aimed at those who have never used Python before.
Python Basics Learning Outcomes
- Creating and managing environments in Anaconda
- Installing packages in conda environments
- Working in JupyterLab and with Jupyter Notebooks
- Creating basic markdown files
- Creating and manipulate objects
- Using functions and methods
- Creating basic data visualisations
Python Basics Course Content
- Introduction
- Introduction to Python
- Introduction to Anaconda and JupyterLab
- The Python Standard Library
- Installing Python
- Anaconda
- JupyterLab
- Installing additional packages
- Anaconda and JupyterLab
- Using Anaconda
- Working with environments
- Launching JupyterLab
- Working in JupyterLab
- Using Jupyter Notebooks
- Basics of running code
- Markdown
- Shutting down kernels and the Jupyter Server
- Using Python as a Calculator
- Arithmetic operators
- Relational operators
- Logical operators
- The Python Standard Library
- Built-in functions
- Other functions in the standard library
- Working with Objects
- Objects in Python
- What are objects?
- Creating variables
- Naming rules
- Naming conventions for variables
- Data Types and Structures
- Built-in data types
- Built-in data structures
- Tuples, lists, ranges and dictionaries
- Pandas Series and DataFrames
- Which data structure should I use?
- Pandas DataFrame
- Attributes
- Methods
- Pandas functions
- Creating DataFrames
- Importing data into a DataFrame
- Uploading data in JupyterLab
- Accessing data within DataFrames
- Accessing specific rows
- Accessing specific columns
- Accessing data subsets by name or position
- Manipulating DataFrames
- Making changes in place
- Renaming columns and rows
- Replace a single value
- Replace multiple values
- Add data to a DataFrame
- Remove rows or columns
- Filter based on condition
- Sort data
- Working with data in DataFrames
- Calculating summary statistics
- Evaluation in Python
- Order of operations
- Evaluation with Numpy
- Vectorised arithmetic
- Vectorised functions
- Broadcasting
- Creating new columns with vectorised arithmetic and functions
- Functions vs Methods
- What is a function?
- Syntax for using functions in Python
- Syntax for using methods in Python
- Parameters and arguments
- Getting help with a function
- Overview of help documentation
- Exporting Data
- Export data to csv file
- Basic Data Visualisation
- Matplotlib
- Create a scatterplot
- Create a linechart
- Add text
- Add a legend
- Exporting plots
- Notebook to Markdown
- View your completed Notebook as a rendered Markdown file
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