Stanford University has a refresher course in Statistics beginning on January 15th, 2015.
That information came into my email inbox today. Yes, I am enrolled. As this course begins one week after the two courses I will be taking to begin my Data Science program with Coursera, I have been contemplating. A lot.
As I see it, there are two (and only two) distinct courses of action I can follow in the short term:
1. Java programming and Hadoop for the computer programming aspect of this discipline. Java is in my distant past. I have not kept up with trends and updates in quite a while. Hadoop and I just met.
2. R (and RStudio, and RCommander, and...) and Python (but 2.0 or 3.0?) alternatively. Both are known to me, but seem to be waning in the "new age" of data science. Given a discipline only about 4 years old, I do have difficulty using that phrase. There it is, anyway.
I am inclined to the second option for expedience. I'm not certain that is an adequate determinant. I feel as old (after another birth day on December 31st) as R sounds from its press. (There is a decidedly animalistic competition going on right now in this area of software development.) Python is going to require some brushing up.
Either way, additional brain time will be required, even after the courses begin. After three days of scrubbing nothing less than the internet itself for information, courses, videos, articles, and individuals selling their wares like a very bad used car salesman, I am strongly inclined to follow the course outline with R, while pondering the choice made to align the course to it. Yet, now would really be an awesome time to display some real confidence in the course, and the instructors who have designed (and will be teaching) it. Yes?
I want to be very good within the discipline, but I also want to be relevant and ready for creating an income as a result of it. There is and must be no doubt that this specialty will require ongoing (non-stop) learning--one of it's strongest attractions for me. I learned several degrees ago that the best one can hope for is a place in the starting grid. I want the best possible place in the starting grid. I'll do the rest.
So, this weekend will be spent looking at things like Bioconductor, and Java, and... everything else I can research effectively. Class starts Monday. I intend to be there.
Recent predictions indicate that, in the near future, data analytics will be available"in a box". (Buck Woody, MicroSoft) Given my penchant for breaking things so as to learn how they work, this is not a totally bad thing either way. Tools should be mastered, not merely recognizable--or even understood.
Besides, I know that nobody thinks the way that I do. I hear that all the time! :)
Any recommendations? Suggestions? Ultimatums?
Welcome to 2015, and an AMAZING journey I am about to begin.
Because of a very special program for US military veterans, Coursera--in association with the National Education Association, The Department of Veteran Affairs, and the US Department of Education (and others), has agreed to participate in a very special program where veterans are being permitted to enter study for ONE Verified Certification of their choice.
I have chosen to study for the Data Scientist Certification program through Coursera as a benefiiary of this special opportunity. I believe every vet should make themselves available to this incredible opportunity.
In this blog, I will post my journey to that certification. Having done my due diligence, I am appalled at what I have committed to. Yet, as a lifelong student, I am pleased to see I have, once again chosen to follow a seeming unlikely path. The field is vast, has been voted "The Sexiest Job of 2014", and is littered with some of the most incredible talent, educators, and organizations in the world.
My Certification, for instance, will come from Coursera and their partner Johns Hopkins University. My training will be led by not one, but three of the top names in the field, and will interact with many more. I have learned that there is a deep need for data scientists today. These professionals are very difficult to find, however because of the incredible depth and breadth of knowledge in several distinct disciplines required. The beginning pay in the US is not too bad ($112-$120k for those found, and hired), but given the requirements could quickly approach minimum wage when comparing to the hours required to do the work.
Data Scientists are usually part of the leadership of a team of professionals, each with one very special area of expertise, which may require them to "step in" and carry the burden for another team member when things get "interesting". From what I have learned, things can get interesting often, a lot.
The field will include much of my life experience, and offer me a real possibility to contribute work of not only the highest quality (personal standard), but real significance in those areas I will choose to apply my learning (long range goal). Of course, the learning will necessarily continue well past this particular journey; just another benefit of my choice of study.
The scope of the learning itself is vast, and there are areas where I must confess to be weak--at the moment. That just whets my learning appetite. But, because of the situation around this proposition, only my very best work will be acceptable to anyone, including myself. That will be a singular challenge given not only my several other endeavors, health, and general outlook on things.
I hope to learn how to find, ask, and answer those questions which consume my thinking, my writing, and my activity from day to day. I hope to become so proficient in this field as to be able to do the same for others, as well (the real goal).
It won't be pretty, or clean, or easy. Just the way I like it. The idea of "leaving it better than you found it" is a prime directive for me on this journey. While this will be a personal reflection on my part, I hope this blog will become a hub of dialogue for my classmates, instructors, and those desiring to know the realities of the study, the profession, and the work.
I hope you will share the journey with me. From a "clean screen" of "What IS A Data Scientist?" to "I AM A Data Scientist!", this blog will consider, share, ask, inquire, complain, and boast the small and large things of life when you choose this path.
Let's begin, and continue a conversation along the way, okay?
Bud
