Experimentation and Discovery

Experimentation and Discovery

The New Year and Me

Note: I wrote this post in January, 2023, but am just getting around to publishing it as I try again to get my thoughts out. I'm not too big on new year resolutions, but I definitely feel a change, or an impetus to change, in this new year.

Calculus and History

I’ve been reading Steven Strogatz [http://www.stevenstrogatz.com/]' book, Infinite Powers, How Calculus Reveals the Secrets of the Universe [https://amzn.to/2K89pVM] and I’m marveling at how exciting it is to learn about how calculus came about, from Archimedes’ brilliant methods more than two millennia

The Mathematics of Security

Originally written on May 25, 2019. I’ve been reading Joshua Holden’s The Mathematics of Security [https://amzn.to/2K71Caz], subtitled Cryptography from Caesar Ciphers to Digital Encryption. The book begins with definitions of ciphers, plaintext, ciphertext, cryptanalysis and other common terms used in the general field of cryptography,

Pandas Cheatsheet helps memory retention

A cheatsheet is like a reference page, with the difference that it provides a quick lookup of the most common features. I really like this pandas cheatsheet [https://www.dataquest.io/blog/pandas-cheat-sheet/] for that reason. When I haven't done data-wrangling or cleaning in a while, it helps

Diving into Data Science: My Last 2 Weeks of Attending Conference, Meetup & Workshop

It's been a somewhat turbulent but interesting last couple of weeks. In my quest for education and knowledge in the data science field, I attended ODSC West 2017 [https://odscwest.pathable.com/] in San Francisco from November 4th through the 6th, and two local data science meet-ups, one

Hypothesis Generation vs. Hypothesis Confirmation

I'm just starting to get back to data science and glad to have found Hadley Wickham [http://hadley.nz/]'s and Garrett Grolemund [https://github.com/garrettgman]'s book, R for Data Science [http://r4ds.had.co.nz/]. It looks to be a good refresher for

Biodiversity and Data Science

From a quick review of measurements of biodiversity that I found on Wikipedia, there appear to be at least a couple of mathematical formulas for calculating it. Yet I don't think of biodiversity in such an abstract way. To me the term relates more to how natural our

TIL: recodes and imputations

When I get a data set to explore, the first thing I think about is the cleanliness of the data. If it's survey data, there may be inconsistencies in the collection method, especially if the data spans years of surveys. From reading the freely-downloadable textbook, Think Stats [http:

Posting Style

I've been getting back into exercising my data science muscles, and as I do continue, I'd like to put together some interesting blog posts. But I don't want to wait until I've got a full data story together. So I'm

Musings on where I am and career plans

It's been about two years since I started taking the Coursera data science specialization courses. I completed I think six of those courses. I still have the Regression Models, Practical Machine Learning and Developing Data Products courses to complete before doing a Capstone project. I'm not

Services development

Over the past six to nine months I've been gravitating toward services development in Java. Having worked in .NET/C# for most of the past dozen years, I have had to enable my frustration shielding as I learned not just a new language but mostly a new set

Brainstorming a data science project

I've finished the R programming course [https://www.coursera.org/course/rprog] and have now moved on to the next course in the sequence, Getting and Cleaning Data [https://www.coursera.org/course/getdata]. In this one, I'll have the chance to further practice with R,

Data Science with R

The John Hopkins' Coursera course on R Programming [https://class.coursera.org/rprog-005] started two weeks ago and I thought I'd share some of my thoughts on the course and my growing knowledge of R [http://www.r-project.org/]. The programming assignments in this course are, thankfully,

Data Skeptic

I was able to get in a couple of workouts at the gym while on vacation this week, giving me an opportunity to catch up on some podcast listens. The Cloud of Data [http://cloudofdata.com/category/podcast/] podcast didn't have any new shows but I discovered a

My Week in Review: Podcast, Resources and Warming up to R

I had an interesting week in the world of Data Science this week. I started learning some R programming, continued to find new and interesting data science learning resources, and had an interesting coincidence occur relating to data science as practiced at my company. Predictive Analytics and Infer On Tuesday

Enrolling in a Data Science Specialization Program

As I said in my first post [http://knowledge-from-data.ghost.io/learningdatascience/] on this blog, I want to write about my personal experience learning and figuring out data science. Over the past week I have had a breakthrough in clarity that was fueled in part by watching a colleague discuss

Python and Machine Learning

Today, I read a little more in the HDInsight eBook [http://blogs.msdn.com/b/microsoft_press/archive/2014/05/27/free-ebook-introducing-microsoft-azure-hdinsight.aspx] and ran a word count MapReduce program on the HDInsight emulator. The really canned approach as presented in the book was not keeping my interest. I'

Azure HDInsight

As I thought about how I would get started digging into data science this past week, my idea was to lay some groundwork for some playful programming that would allow me to get some data analysis experience with a skill (programming) that I am already comfortable with. Then I received

Getting Started

Learning Data Science

I've been working at New Relic [http://www.newrelic.com] for over two years, where I've been developing the .NET agent [http://newrelic.com/.NET] which monitors performance of web applications. This year the company has released an analytics product called Insights [http://newrelic.com/insights]