Centre for Continuing Education

Python Programming Course 1B: Data Analytics

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 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 and Spyder, 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, data visualisation and machine learning.

Outcomes

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

  • use object-oriented functions
  • query data in an external database
  • perform predictive analyses using linear and logistic regression
  • analyse text
  • produce data visualisations.

Content

This course covers the following topics:

  • Object-oriented programming
  • Database access
  • Predictive analysis
  • Text analytics
  • Data visualisation
  • Integration with Tableau Desktop visualisation software.

Intended audience

This course is suitable for professionals 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., basic (high school) algebra, percentages, probability, averages, line/bar charts. It is assumed you have completed Python 1A: An Introduction or equivalent.

Delivery style

  • Workshop
  • Computer-based training on your own device

Materials

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

Bring your own device

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

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.

Features

  • Expert trainers
  • Central locations
  • Free, expert advice
  • Course materials – yours to keep
  • CCE Statement of Completion

Python Programming Course 1B: Data Analytics

<p>{block name:“Course Tagline - Data Analysis and Analytics”}</p><p>The ability to extract useful insights from big data is one of the most highly-valued skills in today’s knowledge economy. Python’s

...
Python Programming Course 1B: Data Analytics

<p>{block name:“Course Tagline - Data Analysis and Analytics”}</p><p>The ability to extract useful insights from big data is one of the most highly-valued skills in today’s knowledge economy. Python’s

...