Irish Language Data Science lecture at Engineers Ireland

I may be the first person to coin Data Science in Gaeilge!

I gave the following lecture to Engineers Ireland which is the Irish professional body for Engineers. The lecture is about “Data Science and the benefits for engineering” and is entirely in Irish.

It was an interesting exercise to brush up on my Gaelic and also to see the wealth of resources that now exist for using Irish with modern technical vocabulary. If you are curious or are trying to get your Gaelic up to scratch then please get in touch!

In terms of content, it covers what data and data science look like and how traditional engineering problems might benefit from the application of data science.

The full video is linked below.

And here are the slides 2016-04 Engineering Ireland_04_Gaeilge.

The Guerrilla Analytics Principles

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There is now a page on guerrilla-analytics.net giving an overview of the 7 Guerrilla Analytics Principles.

I designed the principles to help avoid the chaos introduced by the dynamics, complexity and constraints of data projects. You will find the principles helpful if you work in Data Science, Data Mining, Statistical Analysis, Machine Learning or any field that uses these techniques.

The Guerrilla Analytics Principles have been applied successfully to many high profile and high pressure projects in domains including Financial Services, Identity and Access Management, Audit, Fraud, Customer Analytics and Forensics.

You can read more about the Guerrilla Analytics Principles in my book Guerrilla Analytics: A Practical Approach to Working with Data. Here you will find almost 100 practice tips from across the Data Science life cycle showing you how to implement these principles in real-world situations.

Do you have your own data science experiences and principles? Let me know by getting in touch!

3 Lessons I Learned From Writing a Data Science Book – ‘Guerrilla Analytics – a practical approach to working with data’

Writing

One of the biggest challenges with writing a significant piece like a book chapter or entire book is to estimate how long it will take and plan accordingly. My best reference was my PhD which was still significantly shorter than the book’s target 90,000 words. This blog post is about the book writing process as I experienced it. I hope it helps other authors setting out on such an endeavour.

Since ‘Guerrilla Analytics: A Practical Approach to Working with Data‘ is about operational aspects of agile data science, I recorded some data on the book writing process itself. Specifically, every time I finished a writing session, I recorded the number of words I’d written on that date.

My 3 Lessons

  • Progress tapers off. You’ll get more work done in the first half of your project. Don’t expect this rate of progress to be sustained all the way to your deadline.
  • Be realistic about how much you can write in a session. I found it difficult to write more than 1,500 words. Anything more was the exception for me. Track your progress and re-plan accordingly.
  • Weekends are better than weekdays. Obvious maybe! Expect to set aside your free time on weekends to get your project over the line. It is difficult to get significant amounts of work done on weekdays.

Progress tapers off

  • Here is my progress towards my goal of 90,000 words over an 8 month period. The plot shows the words written per session and the total word count.writing_log_progress

    I began writing in late September and finished in June the following year. The line shows my total words written and the bars show the number of words written in individual writing sessions. Two things stand out:

  • Progress is faster in the first half of the project. This was because it is easier to get all your ideas ‘onto paper’ early in the writing. Once you have about 3 quarters of your manuscript complete, you need to be more careful about consistency of language and flow of content. This slows you down.
  • Time off work is really productive. There are two clear bursts of productivity as shown by the dense groups of grey bars where a large number of words was written in many successive sessions. The two periods are Halloween (when I took a week off work) and Christmas when I worked for a week from my family home.

How much did I write in a typical session?

Here’s how much I wrote in each writing session.

Words per session

I typically wrote about 1,000 words with the odd session where I wrote over 3,000 words. This is important when you plan your project. If you’re anything like me, writing more than 1,000 words will be an exception. If you only write on weekends then you’re looking at only 2,000 words per week. That’s well under 100,000 words in a year allowing for holidays and other disruptions.

Are you thinking about writing something and have questions? Feel free to get in touch and best of luck!

Guerrilla Analytics – the book! Book contract signed for Autumn 2014

Great news! I will be publishing a book on Guerrilla Analytics with Morgan Kaufmann in Autumn 2014. After lots of proposal crafting and contract negotiations the contracts have finally been signed and I can begin work. It will be about 90,000 words on Guerrilla Analytics covering topics such as:

  • what is data analytics and where does guerrilla analytics fit within that?
  • the principles of guerrilla analytics
  • worked examples at each stage of the data analytics workflow from data extraction and receipt through to delivery of work products. All of these examples will be supported by practice tips, case studies and war stories. This will be a real practitioners book that will help you survive real analytics projects in fast paced dynamic environments

You’ll find this book useful if you are:

  • a Senior Manager and you want to know that you have the right team and technology in place to deliver reproducible, tested analytics that stand up to audit and scrutiny and can be handed over easily when resources roll off your project
  • an analytics Manager who has several reports. You do want your team to be independent and agile without having to micro manage their work. You want to keep it simple so that everybody on the team can maintain data provenance and understand one another’s work without repeated inefficient hand-overs and explanations
  • a data analyst who wants to do high quality work, interact in a team but not be burdened with unnecessary process and team rules.

I’m looking forward to getting started! Stay tuned for more updates and some snippets of the book as it evolves.