Learning Data Science
I've been working at New Relic for over two years, where I've been developing the .NET agent which monitors performance of web applications. This year the company has released an analytics product called Insights, a data analysis tool for understanding your business through the lens of the operation of your web applications. It's the company's first foray into data science, or big data, or whatever buzz word you want to apply to the analysis of large data sets.
Like a lot of developers, I am intrigued and at the same time mystified by the broad subject of data science. To fully understand how to analyze data, there is an implied understanding of statistics. But how much statistical thinking, or training, is required to be considered a data scientist? Then there are programming languages like R targeted to data analysis, as well as data analysis libraries for general-purpose languages such as Python. Where does one start?
I've attended a few local data science meetups, met some entrepreneurs in the field, and even run through some R tutorials. I tried to take some online courses, in Statistics, and in data science with R, but I never completed them. So why would you want to read what I have to say about this topic?
Beginning today, I will be blogging about my personal experience with data science. I'll post about how I go about learning, what I actually learn, what I'm confused about, and what I think about applying this new science to various topics. If you want to get a concise lesson on how data science applies to climate change, or to genetics, this is not the place to get it! But if you want to go on a journey with me, or just see how I go about figuring this stuff out, then you've come to the right place.
I'm committing myself to blogging something every week. That's a tall order, yes, but in addition to covering data science, I may talk a bit about learning or life as it applies to committing myself to a plan. Welcome to my world!