Presentation Principles

Continuing the topic from the previous post, it might be useful to consider tactics used by strategy consultants to improve the quality of their presentations.

Horizontal and Vertical Flow

The slide title should generally be the takeaway for the slide. One should be able to determine the "story" of the presentation from reading all the slide titles.

More information here.

Pyramid Principle

This well-known principle bears repeating. This helps one transform complex data into actionable insights. You may see one summary of this here

Part 1

Part 2

MECE Mutually exclusive Completely exhaustive

This is another approach to simplify our complex environment into understandable ideas.

Overall, the above remain ideas on paper until executed. As with most things in life, one can improve your presentation skills via practice, practice, practice.

Any other presentation tips come to mind? Share yours in the comments below!

Data Analytics 101 Part 5 of 5: Presenting findings and recommendations

All that hard work and we are almost at the end. Business (and many parts of life) require effective communication:  your analysis can come to live with the right narratives and visualisations. It is key to be able to  effectively communicate with data, present findings and make recommendations to decision makers.

Data within a business context

If you want to know more about any of the topics explored in the module, you can find further resources here:

• Access the Storytelling with Data resources in the form of a blog and a podcast to find out more about the techniques for data storytelling and for practical examples of the techniques discussed in this module.
• ICAEW presents webinars on various topics including one on data driven decision making.
• TowardsDataScience has an article containing further information on data storytelling.

Data visualisation for presentations

If you want to know more about any of the topics explored in the module, you can find further resources here:

• The Office for National Statistics discusses their findings on their use of visualisations to communicate to the wider public.
• ICAEW has a webinar called ‘The 7 Hats of Data Visualisation Design’ that looks at the skills needed to be a ‘superhero’ in data visualisation.
• This article discusses exactly when a visualisation is a good choice for communicating findings.
• For inspiration when creating visualisations: this Tableau article shares some beautiful examples, and this article on Analytics Vidhya also demonstrates what good visualisation looks like.

And this concludes the five-part series on Data Analytics, hope this has been useful for you, and thanks for reading!

Data Analytics 101 Part 4 of 5: Decision Making

What do you do with all the insights developed in the previous stages of data analytics?
Yes, it is time to decide. Data-driven decision making is a key skill set to acquire and hone in this big data era.

Time series modelling

If you want to know more, you can find further resources here:

• TowardsDataScience demonstrates how to forecast data, including discussing how to identify the best model.
• The statsmodels library is used frequently in this module. Find out more about the time series analysis (tsa) package including a list of commands.
• Learn more from Machine Learning Mastery on autocorrelation and partial autocorrelation.

Anomaly detection

If you want to know more, you can find further resources here.

• DataCamp provides useful cheat sheets and tutorials that detail the various uses and commands in packages. These include a cheat sheet for the sklearn package that is used frequently within this module.
• DataCamp also has tutorials examining the sklearn package to perform KMeans clustering and Principal Component Analysis.
• TowardsDataScience provides an article explaining how to use techniques such as Principal Component Analysis and clustering to detect anomalies.
• Medium has an article explaining unsupervised learning and how it can be used in detail.This includes examining different types of clustering, dimension reduction techniques such as PCA and methods.

Ethical considerations

If you want to know more, you can find further resources here:

• Access this link for the UK Government’s Framework for incorporating Data Ethics into public sector data projects. This includes practical advice to ensure ethical issues are considered throughout.
• Royal Society have examined Data Ethics in depth through a series of seminars, roundtables and in-depth research with professionals in various fields, summarised in this report.
• The Alan Turing Institute is dedicated to using analytics and Artificial Intelligence ethically. To ensure this, they have published an online guide about data ethics.

Any other resources that are relevant to decision making? Please share in the comments below.