- “Executives and managers are being asked to make major decisions based on the output of an algorithm that they didn’t create and don’t always fully understand” – Dr Thomas Erwin, Head of Global Lighthouse, KPMG International, in the KPMG’s report Guardians of Trust
- Harvard Business Review (HBR), in this article, argued on the need for “Analytics Translator” to bridge the ever-widening gap between the business and the analytics teams
Are these related? Is the mistrust due to the fear of the unknown? I faced the same dilemma, on encountering a Machine Learning & Analytics algorithm the 1st time. Can I trust it? Looks like magic! But, all around, businesses and teams were embarking on Machine Learning programs. Is there more to it than a mere black box? It was time to look for answers.
Looking for answers
I turned to the internet for answers. The quantum of information was mind-boggling. I lost my way after a few introductory videos of the most “popular” online courses. I, then, bought the “best & recommended books” and the concepts were overwhelming. I’m no data scientist, a PhD in Mathematics, or an AI expert. The concepts were written with an advanced audience in mind. I was just a curious beginner.
Analytics & Machine Learning involves multiple disciplines coming together, which makes it extremely complicated to start off with. When I started, there were a gazillion questions:
- Do I need to know mathematics, statistics & probability?
- What tools are available?
- Is R or Python the best choice?
- How much of coding and algorithm should I know?
- Where do I get started?
I lacked access to a comprehensive guide to get me started “quickly”.
After floating around, I invested into attending a class-room based Machine Learning course. The course clarified lots of doubts, and gave rise to many more. It served one key purpose though – providing a structure to get started. Further research led me to many books, YouTube videos and blogs. I’ve spent countless hours pouring into the topics and filled pages with concepts, notes, and my interpretations.
Sharing the knowledge
During my work, I deal with a variety of people – internal & external. They come from varied backgrounds – CxOs, Head of Departments, Business & Technology teams, experienced & freshers, and many more. I see them go through the same set of struggles, I had been through.
My objective is to help beginners and consumers of Analytics & Machine Learning programs, overcome the initial step and get started confidently. Through this blog, I want to share my learnings and experiences. In turn, I want to enrich my knowledge of the subject. This blog will cover the breadth of the Analytics & Machine Learning topics – serious practitioners are welcome to contribute and further enrich with the depth.
This blog will cover the fundamental concepts (I had forgotten a whole lot of mathematics since school days, and had to refresh it all over) and explore the application for business problems and needs. This blog is targeted at:
- The curious cat struggling to get a grip on starting off
- Business users consuming Machine Learning & Analytics programs results
- Executives taking critical business decisions
- Project managers coordinating Machine Learning programs
My aim to cover at least 1 key topic every week. Watch this space for new posts, and please don’t forget to share your views and comments. It will help enrich our experiences.
“The scariest moment is always just before you start” – Stephen King
Let’s get started…