Python Programming Course 1B: Data Analytics

Data analysis and analytics. Uncover insights and transform your organisation.

COVID-19 update: arrangement of our courses

We are now delivering courses both online and in-person. Please check the delivery format for each class before you enrol.

Please note that course materials for all classes (excluding prescribed textbooks) are shared electronically within 48 hours of a course starting. Printing is not available.

The ability to extract useful insights from big data is one of the most highly-valued skills in today’s knowledge economy. Python’s massive library of functions makes it easy to examine, filter, transform and visualise datasets in ways not possible with spreadsheets or relational databases. With the availability of user-friendly development environments such as Jupyter Notebook, it’s easier than ever to get started.

This course is part two of a two-part Introduction to Python series. We recommend enrolling in both courses to gain entry level skills. Enrolment in both courses is not compulsory.

Please ensure Anaconda Python is installed on your device before class. For further details, see 'Before the course'.

Aims

In this course, you will build on the fundamental Python programming skills acquired in the Python Programming Course 1A: An Introduction and learn to apply them to contemporary data analysis tasks, including social media analytics, text analytics and data visualisation.

Outcomes

By the end of this course, you should be able to:

• import and export data
• work with Pandas series and dataframes
• query data in an external database
• perform descriptive and predictive analyses
• analyse text
• create basic graphs and visualisations.

Content

This course covers the following topics:

• Reading from and writing to data files
• Understanding, creating and using Pandas series and dataframes
• Data cleaning, indexing, querying, sorting, aggregating and merging
• Database access and queries
• Descriptive and predictive data analysis
• Text cleaning and analysis
• Data visualisation

Intended audience

Suitable for professionals, students and academics who want to develop more advanced Python skills and get a taste of Python’s extensive data analysis and visualisation capabilities.

Prerequisites

It is assumed you have computer and data literacy knowledge to the level of performing basic data analysis tasks, ie, high school algebra, percentages, probability, averages. It is assumed you have completed Python 1A: An Introduction or have equivalent knowledge.

Delivery style

Delivery options:

• presenter-taught training on your own device, on University premises
• online training via the platform Zoom.

Face-to-face classes

You are required to bring your own device. You should ensure it is fully charged as access to power is limited. Please note that the University does not carry any responsibility for your lost, stolen, or damaged devices whilst on the University premises.

Online classes

If you are attending an online class, you will need your own device.

Materials

You will be provided with online training materials including a mixture of step-by-step instructions, examples and exercises. These materials will serve as a useful reference when working with Python in the future.

Before the course

Please ensure Anaconda Python is installed on your device before class. Version 3.x is required and can be downloaded from Anaconda.