About Manuel Amunategui
Data scientist with over 20-years experience in the tech industry, MAs in Predictive Analytics and
International Administration, co-author of Monetizing Machine Learning and VP of Data Science at SpringML.
From consulting in machine learning, healthcare modeling, 6 years on Wall Street in the financial industry, and 4 years at Microsoft, I feel like I’ve seen it all. And this has opened my eyes to the huge gap in educational material on applied data science. Like I say:
It just ain’t real 'til it reaches your customer’s plate
I am a startup advisor and available for speaking engagements with companies and schools on topics around building and motivating data science teams, and all things applied machine learning.
Reach me at amunategui@gmail.com
For more information:
Linkedin
Videos
Medium
amunategui.github.io
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Multitask? Why Divide When You Can Multiply Yourself? The Art of Multi-Purposing Your WorkSpend less time on obvious one-offs, automate if you have to, but if the task is rich and interesting, squeeze it for all its worth!
I’m not big on multitasking. Unfortunately, we all have to do it at times. For example, if I spend the morning working on three or four different tasks, they tend to be chores of the mindless kind. This works OK when tying loose-ends or running down a checklist. If it requires thinking, concentration or getting into the zone, then my morning will be dedicated to only one project. I put my phone out of sight, remove distracting tabs from my browser, and I may even turn of the music if it’s highly cerebral work. It’s protect my space and put-on-blinders time! Multi-Purposing Your WorkBut this certainly doesn’t mean that your output should be diminished whenever you are working on a single task. Its actually the opposite. If you are working on something big, something interesting that requires your undivided attention, it may be able to serve multiple purposes. For example, if you are gathering ideas for a new project, document your progression and insert time-stamps around big milestones and suddenly you have a ‘behind the scenes’ or a ‘how to guide’. Yes, with just a tad more effort you’ve doubled your output! Let’s look at some examples. E-Books, One Chapter at a Time
This is an approach I’ve used in creating e-books. First, I create a rough index of a book I want to write then I tackle it, one chapter at a time. Once I have a finished chapter, I publish it on Medium, LinkedIn, blog, etc. Not only does this yield a gratifying feeling as you work towards the full book, it also offers important feedback and criticism along the way. It may not work for all book types, but if your chapters tend to stand up on their own, It may work for you too. Blog-to-Video, Video-to-BlogIt doesn’t end there - for the longest time I’ve been creating videos that eventually become blog entries (or the other way around). This requires a little more work as you need to think about your blog as a script for your video or your video as an interview for your blog, but it can double your reach.
See an example at of blog: Convolutional Neural Networks And Unconventional Data - Predicting The Stock Market Using Images and corresponding YouTube: https://www.youtube.com/watch?v=IscG_bAeEic&list=UUq4pm1i_VZqxKVVOz5qRBIA Blog Entries and Videos as Focus GroupsAnd wait, there is still more. My YouTube and blog entries on data science have worked as free focus groups by counting the views, traffic hits, and comments. Whenever the numbers are really big, I know that there is a lot of interest there. This type of interest gauge has guided me on what type of class to create on Udemy. Creating a course takes time and the last thing you want to discover is that there is no interest there or the competition is fierce. By making free blog posts or videos on a topic will get you all the answers you need to help you size the market. “ Let’s Get Rich With quantmod And R” is my most popular video with almost 40k views, yet “Pseudo Coefficients: Get Your all-else-held-equal” took me over 3 months to make and barely registered any traffic - if you compare traffic numbers, the 80/20 rule/Pareto principle should jump out at you.
It is such a rush when previous work is re-purposed into something new. This can be done a lot more systematically when choosing the right type of work to tackle in the first place. Spend less time on obvious one-offs, automate if you have to, but if the task is rich and interesting, squeeze it for all its worth! Note: for those interested in learning applied data science, check out my new free eBook and course: How to Create and Sell Your Machine Learning Product Online
Thanks for reading! And please share this article with a friend or colleague or two :-) Best!
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