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

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 and user-friendly development environments, make it easy to clean, combine, analyse and visualise data from a variety of sources. The creation and distribution of routine reports can be automated, saving valuable work time.

This is a practical ‘learning-by-doing’ course. With the help of expert guidance and supervision, you will develop your own Python script to automate the management, analysis, visualisation and reporting of a real-world dataset (provided).

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 use the CCE Python self-assessment tool if you are unsure which course level to enrol in.

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: Level 1A and learn to apply them to contemporary data analytics: managing and analysing data, and visualising and publishing insights to inform decision-making.

Outcomes

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

• import data from different sources, including files and databases
• manage data using the Python Data Analysis Library (Pandas)
• perform descriptive and predictive analyses
• create basic graphs and visualisations
• automate the creation of routine reports.

Content

• Reading data from files and databases
• Working with Pandas data structures (Series and DataFrames)
• Cleaning, indexing, querying, sorting, aggregating and merging
• Descriptive and predictive data analysis
• Data visualisation
• Automating the creation of Word and PDF reports

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, i.e., high school algebra, percentages, probability, averages. It is assumed you have completed Python Programming Course: Level 1A or have equivalent knowledge.

Delivery modes

• Face-to-face, presenter-led learning using your own device
• Online learning via the platform Zoom

Delivery style

Face-to-face classes

These classes run in a classroom and you need to bring your own device with Anaconda Python installed. 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

You will need your own device with Anaconda Python installed.

Materials

Training materials, including a mixture of step-by-step instructions, examples and exercises are provided electronically using Dropbox. These materials will serve as a useful reference when working with Python in future.

Before the course

Anaconda Python Version 3.x is required and should be downloaded free of charge from Anaconda before class.