As a child, I loved telling stories. I’d take my favorite book and TV characters and create a world where they would oh-so-conveniently meet. Say, a magical anime girl wanders Narnia until she encounters the now-villainous Power Rangers.
As an adult in the corporate world, I still want to tell stories. But now I find that people are more critical of which stories I tell them.
It must be in the form of numbers, they said.
It’s a data-driven world, they said.
In Cole Nussbaumer Knaflic’s book Storytelling with Data, she argues we can do just that: tell stories with numbers.
language + math = data storytelling
She takes traditional storytelling concepts then re-interprets them for “adult-appropriate” tables and charts. She teaches us to edit our charts, the same way authors do their stories, by borrowing principles of visual design.
My key takeaways from the book can be found below (click for larger size), but they can be summarized as follows:
Context is king. The form your data will take depends on your audience and what you want them to do with the data.
Choose the right graph to best express the key message (I’ve made a flowchart in my notes to help with that).
Following on #1, design around this message.
Present your data as you would a story, with a beginning, middle, and end.
P.S. Sorry about the terrible handwriting. My normal penmanship’s already pretty bad, but writing on a tablet made it worse!
The Philippine analytics industry is still in it’s infancy. There is a demand for the skills NOW, and this demand will grow even more in the coming years.
This is the key takeaway from the Big Data Analytics Conference 2016, held at Enderun Tent last 15 November 2016. It was the first conference of it’s kind and scale in the Philippines, gathering participants from the IT, business, academic, and health industries.
As someone considering a possible career shift, I wanted to find out if there will be a market to shift to, and what are the kind of skills they’re looking for.
Below are my notes from the event, along with some insights.
You never actually analyze and visualize data, but this course is worth taking as it’s a good introduction to using Power Pivot and Power Query–both of which are useful for managing large amounts of data in Excel. Just make sure you manage your expectations.
This was my gateway course to the program. Excel enthusiasts at work had recommended it as a good introduction to PowerPivot, and it was only later that I found out the course was part of a larger data science program.
My primary purpose for taking the course was increasing my proficiency in Excel. I currently manage a large-scale project with an equally large-scale tracking spreadsheet. The spreadsheet easily gets out of hand due to the sheer number of assets involved and because it pulls data regularly from multiple data sources. I was hoping the course would help me clean up the data and make it sustainable to maintain in the long run.
Because of this, I’m reviewing the course from a more practical Can I use this at work? perspective rather than its relation (or lack of) to data science.
It took me about a month to complete, starting September 2016. You can follow my progress in the MS Data Science Program by using my tag Microsoft Professional Program.