Python for data analysis and visualisation

Overview

Scholarships available

We have funded places available for those who live or work in the Tay Cities region, This is funded by DigiTay, the Tay Cities Region Deal Digital Skills Project, the . Please complete our application form to apply for a scholarship. 

Course details

In this course you will learn about good practices of developing Python code for working with data through the following topics:

• Structuring Python code: functions, classes, exceptions, modules.
• Software engineering practices: testing, debugging, profiling, documenting, organising, storing under version control.
• Using Python for data analytics: collecting data (API interaction, web scraping, interfacing databases) and using popular Python libraries (numpy, pandas, matplotlib) for their processing and visualisation

Python code is supplied and explained for each topic. Your key learning outcomes are:

• Master concepts of modelling, design, and programming in Python and gain practical skills in applying these concepts
• Be confident with effective documentation, layout, debugging and testing
• Be able to use Python programming and development tools
• Be able to load into Python data from standard formats and perform some descriptive data analysis.

The time commitment for this course is typically six to eight hours per week. 

Who is this course for?

The course is aimed at professionals with a high level of numeracy who are seeking to understand the core programming skills needed for data analytics.

Teaching format

This is a self-paced online learning short course with lecture content, interactive elements, and access to a masterclass with the course leader after completion of the course.

Course requirements

This course is suitable for those who are competent in programming using Python (including notebooks, packages, data manipulation, design and use of pipelines, model evaluation functions) but are not expert programmers.

Coursework involves creating code to solve a specific problem, together with a short report that describes the approach taken and critically evaluates the results. This code can be developed either using learners’ equipment (such as a laptop or PC), or with cloud-based tools such as CoLab and Kaggle Notebooks, or Jupyter notebook. A good internet connection is more important than powerful computational equipment.