ricklentz · January 23, 2018 04:35
diff --git a/DS745_hello.txt b/DS745_hello.txt
 Like others, I'm nearing the final stretch of this program (course 10/12).  I've supplemented the material in our program with Udemy and Udacity courses and am preparing to compete in my first Kaggle competition as part of a Udacity capstone project.  My dominant focus is on studying for a financial analysis credential that is required to advance at my current employer. 

 "I'm looking to increase information advantage by enriching analysis with machine learning workflows using current state of the art tools and methods." 

 I'm interested in unstructured data analysis more than visualization, namely because my organization is struggling to gain any value from it.  I've been asked by my mentor to investigate methods that leverage unstructured data to narrow risk premium for certain investment and security events. I'm searching for methods that aid in generating machine-encoded knowledge representations of text data.  I've looked at text summarizers (e.g. https://www.youtube.com/watch?v=ogrJaOIuBx4) and how IBM's Watson searches text to generate answers.  Maybe you have come across arXiv articles, Jupiter-notebooks, GitHub links, or demos in this space?

 The Glenn Simpson testimony was released recently.  It may be fun to use that text in this course: https://drive.google.com/open?id=10tz91Cb473_YfMX_Zbid0Be5OhJTZ_SZ

 My work and home commitments sometimes require me to step away from my Data Science studies at short notice.  I just put that out there in case there are timed assignments in this course (and the timer doesn't stop when I log off). 

 Over the past few years, I've become a huge Python fan.  I'm hoping I can leverage it for the work in this course.  If not, I guess it is still ideal for writing "Hot Dog, Not Hot Dog" classifiers :-)
	Like others, I'm nearing the final stretch of this program (course 10/12). I've supplemented the material in our program with Udemy and Udacity courses and am preparing to compete in my first Kaggle competition as part of a Udacity capstone project. My dominant focus is on studying for a financial analysis credential that is required to advance at my current employer.

	"I'm looking to increase information advantage by enriching analysis with machine learning workflows using current state of the art tools and methods."

	I'm interested in unstructured data analysis more than visualization, namely because my organization is struggling to gain any value from it. I've been asked by my mentor to investigate methods that leverage unstructured data to narrow risk premium for certain investment and security events. I'm searching for methods that aid in generating machine-encoded knowledge representations of text data. I've looked at text summarizers (e.g. https://www.youtube.com/watch?v=ogrJaOIuBx4) and how IBM's Watson searches text to generate answers. Maybe you have come across arXiv articles, Jupiter-notebooks, GitHub links, or demos in this space?

	The Glenn Simpson testimony was released recently. It may be fun to use that text in this course: https://drive.google.com/open?id=10tz91Cb473_YfMX_Zbid0Be5OhJTZ_SZ

	My work and home commitments sometimes require me to step away from my Data Science studies at short notice. I just put that out there in case there are timed assignments in this course (and the timer doesn't stop when I log off).

	Over the past few years, I've become a huge Python fan. I'm hoping I can leverage it for the work in this course. If not, I guess it is still ideal for writing "Hot Dog, Not Hot Dog" classifiers :-)