Discover how to use visualisation techniques in Python to present key messages from complex data with hands-on examples, including building a dashboard to identify patterns and trends present.
If you’re looking to learn how to create advanced visualisations in Python, this course is ideal for you. An individual subscription gives you 3 months’ online access to:
As Well As
Our Industry and Actuartech Resource Libraries which feature curated additional content to assist you on your data science journey.
You can also request to access to a coding project to practice the skills you learn in this course.
Visualisations can be very useful at deriving insights from data. By using the open-source programming language Python, this course will illustrate how visualisations can be generated and applied in the data science pipeline. The course will walk through how, with the use of visualisation techniques in Python, we could better interpret the data we are working with, and how we can communicate those insights using dashboards. We will also show visualisation techniques can aid in preliminary data analysis and how to best deal with partially complete data sources.
The course aims to build on the initial visualisation techniques learned in Actuartech's Foundations in Python and the techniques presented here by developing different dashboards in Streamlit and Dash to showcase how visualisations can be presented in an interactive manner.
This course is aimed at users who have some experience with Python and would like to get a better understanding of the visualisation techniques available by way of hands-on examples. Users that would like to learn more about how visualisation can aid in the data processing stage and as a tool for preliminary visualisations will also benefit from this course. We make use of static Jupyter notebooks with the code and explanations embedded. Downloadable versions of the notebooks have been made available which you can run and edit to see how tweaks effects output. We encourage and encourage users to explore further visualisations beyond those presented in the examples.
Chapter 1 introduces and outlines the course, along with what to expect.
Chapter 2 provides an introduction to visualisations and dashboarding, along with initial considerations on which techniques to choose.
Chapter 3 covers techniques for visualising data using different Python libraries.
Chapter 4 discusses complex visualisations, including geographical plotting and visualising time series data.
Chapter 5 provides an overview of two dashboarding libraries in Python and walks though how to create interactive dashboards using both libraries.
Chapter 6 discusses options and considerations for deploying the dashboards developed in Chapter 5, and walks though how to deploy a Streamlit dashboard.
We have tailored packages available to ensure that corporate teams have the option to attend structured live lessons by our tutors, and the option to request a practical assignment and bespoke feedback. Invoicing option available.
