It’s been several months now since beginning three programs with Udacity out of Mountain View California.  The first, was the Flying Car Nanodegree. The second is the Data Analyst Nanodegree. Finally, the third program is the Deep Learning Nanodegree.

The Flying Car Nanodegree, FCND for short was quite challenging, involving writing code for linear algebra relating to physics and  geometry.  I had doubts I’d complete this nanodegree up until the last project submission. Fortunately I completed it and my persistence paid off after many months.

Now I am in the last days before completing the Data Analyst Nanodegree and looking forward to trying to complete the Deep Learning one also.

I highly recommend to anyone, enrolling in an online course about whatever topic interests you. Completing an online course is highly rewarding just based on the new knowledge you will have gained. The only draw back is that for me at least, these have taken a huge amount of my time and I’ve had to give up a lot of activities to make the time that I needed to work on this nearly all of the online courses that I’ve taken in the last ten years.

Getting back to the Data Analyst Nanodegree also referred to as the DAND.  This program is divided into two terms and I am almost done with the first one. The first term is broken into three modules; introduction to python, introduction to data analysis, and practical statistics.  Each module of instruction includes a project which requires the application of the concepts and techniques covered in the module. Therefore completing a nanodegree will give you the confidence needed to be proficient in the course topic.

Since I began taking courses involving computer programming I’ve been amazed at the results that can be derived from developing a software program. That’s it for today’s thoughts on data analysis. Thanks for stopping by!

Beginning October 2108, the intent of this blog will be discussing programming of autonomous agents, artificial intelligence, data science and analysis and supporting tools to facilitate accomplishing the tasks in these domains.



The video clip above was my submission for Project 2 of the Udacity Flying Car Nanodegree.

This is a demonstration of autonomous 3D flight planning using the A-star search algorithm to find a path solution for a simulated Quadrotor through a simulated 3D section of downtown San Francisco California as the physical environment.

This solution needs some improvement to reduce the number of nodes along the path to eliminate the need for the Quadrotor to double back when overshooting a given node. The process of reducing the number of waypoints or  nodes is called path pruning and there are several techniques available for achieving this.