Data Visualisation with Python

Micro-Credentials

Data Visualisation with Python

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  • Study Hours

    Study Hours

    60 Hours

  • Total Fee

    Total Fee

    £585.00

  • Delivery

    Delivery

    Online

  • Modality

    Modality

    Self Paced

Programme Overview

Developed in collaboration with Tableau, this course provides an introductory lens into the world of digital marketing. It will give you the confidence to understand and participate in digital marketing-focused conversations. By completing this course, you will have the foundations to enter a digital marketing career or complement an existing professional skill sets with improved digital technology capabilities. 

The course focuses on developing the knowledge and skills needed to successfully work in a digital marketing environment, while also being able to complete entry level digital marketing activities. In this course, learners get practical experience defining a digital marketing strategy including identifying customers and their behaviour and segmentation, understanding SEO and SEM strategies, and a foundation for creating digital marketing strategies. Throughout the course, learners will apply their learning to create an integrated marketing plan. Beginning with the basics, learners will identify personas, map the customer journey, define their campaign objectives, and create a digital channel plan based on business/organisational objectives. 

The curriculum was developed in consultation with experienced, senior digital marketers, and industry professionals who regularly develop digital marketing strategies and campaigns.

Learning Outcomes

  1. Utilise best-practice design principles to assess and improve advanced data visualisations. 
  2. Conduct data analysis using Python programming to support the implementation of data visualisation techniques for ongoing business performance.
  3. Prepare exploratory visualisations utilising different Python plotting libraries to inform business decisions.
  4. Critically reflect on learning by describing the processes followed, justifying approaches taken to create the data visualisation workflow, and addressing the ethical issues in specific organisational contexts. 

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