Future Skills: Persuasive Presentation With Data Storytelling - 生產力學院
Future Skills: Persuasive Presentation With Data Storytelling
10013986-07
香港九龍達之路78號
2024-02-05
張小姐 - 2788-5013 | 余小姐 - 2788-5029
maggiecheung@hkpc.org
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[只提供英文内容]

Programme Highlights

Through this workshop, you will learn how to tell a story with data that resonates with your audience.

You will learn the skills that make up the art of data storytelling: from data analytics and insights, communicating with data, creating impactful data visualisations, and finally to developing attractive PowerPoint and delivering stunning presentations.

Learning Outcomes

By taking this course, you will be able to

  • Design and develop effective data visuals based on analytics results from descriptive, diagnostic, and predictive analytics
  • Formulate strategies in developing data story to create a persuasive presentation
  • Acquire the skills in developing concise and attractive presentation and understand the way of handling challenges during the presentation

Date

05-06 Feb 2024 (Mon & Tue)

Time

09:30 – 17:00

Duration

Total 12 lecture hours

Medium

Cantonese
(supplemented by English and with English handouts)

Certificate of Achievement

A Certificate of Attendance will be awarded to participants who have attended 75% or above of the course.

Course Structure

Session 1-2
(A) Essential Concepts of Data & Analytics
  • Introduce the basic concepts of data and analytics illustrated by descriptive, diagnostic, predictive, and prescriptive analytics
  • Discuss the latest technology development in data and analytics with some real-life examples of machine learning model applications, and the related challenges in communicating data insights
(B) Introduction to Data Visualisation
  • Discuss different types of data visual and typical tools for visualization creation, e.g. Excel, Cognos, Tableau, Qlik, and Power BI. Examples of selecting correct types of visuals are shared
  • Introduce some general tips on creating visuals by illustrating some real-life examples of ineffective visuals and potential improvement areas to create effective visuals
  • Improve understanding of picking right visuals through group exercise of examining different real-life visual examples in Hong Kong and overseas to identify potential improvement areas
  • Introduce the Gestalt Design Principle to improve data communication and discuss the ways of drawing audience’s attention from the visuals
  • Discuss some case studies from descriptive, diagnostic, and predictive analytics to demonstrate the process from generating data insights to visual creation to support management presentation
Session 3-4
(C) Introduction Data-driven Storytelling
  • Share the importance of storytelling and the major reasons of using storytelling approach to present data analytics
  • Introduce the 6-step approach in constructing the data story for management presentation
  • Practice the ways of generating insights and ideas from data and choosing different effective visuals to support storytelling. A step-by-step illustration of creating data-driven storytelling visual is shared from the process of defining problem statement to creating data visualisation
  • Share the practical tips in delivering persuasive data presentation
      • Discuss the key reasons of presentation don’t work
      • Introduce the VAK Learner Model – reviewing the three major learning styles: Visual, Auditory, and Kinesthetic, and explaining the implications of different styles in data-driven storytelling
      • Discuss the engagement model with audiences and the key steps in developing management presentation
(D) Put All Together (Group Presentation)
  • Recap the 6-step data-driven storytelling and key steps in preparing management presentation
  • Group Presentation: applying data-driven storytelling techniques and persuasive presentation skills
(E) Next Steps – Continuous Improvement Cycle and Key Recap

Trainer

Dr Lawson Law

Dr Law has possessed rich data analytics experience in banking and finance with over 25 years of experience in various analytics areas. He has obtained Doctorate Degree in Engineering and Master of Science Degree in Information Systems.