It's not overstating things to say that I use Microsoft OneNote in every part of my life. I use it to create and track projects at work; categorize, list and annotate the technologies I work with; share information with my family (from groceries to the punch-down list my wife uses when she flips houses, to … Continue reading How I Work – Using OneNote For My Daily Task List
We're in a series on the "Keys to Effective Data Science Projects" - and we've completed the hardest part - identifying the problem(s) we want to solve. Note that each problem gets it's own project - you're not going to predict when a hurricane will hit and how much money you'll get in your bonus … Continue reading The Keys to Effective Data Science Projects – Part 3: Import Initial Data
Processes are great things. They give you a place to start, a roadmap, and a way to explain to your stakeholders (like customers) what you're going to do and the order you'll do it. In addition, a process compresses information into shorter information so that you can "peg" it as you work through it. Then … Continue reading The Keys to an Effective Data Science Project – Part 1: The Team Data Science Process
Microsoft has recently been on a tear introducing R into, well, everything. And now there are several R offerings - from Microsoft R Server and Microsoft R Open to R Services in SQL Server (2016) and now the Microsoft R Client. But is the Microsoft R Client a client? So it's a command-line, or a … Continue reading Is the Microsoft R Client a…client?
As you're learning R (you *have* read the post about that, right?) you'll inevitably make some mistakes. That's normal when learning any language, and it's especially common when you already know another programming language. I'm creating this post because this is the first thing you'll run into - let's get some good habits going right … Continue reading R Misteaks -Episode I
(Complete Table of Contents here: http://aka.ms/backyarddatascience) What, Why, How In a previous notebook I introduced the R programming language and environment. While R is very powerful, widely used and has multiple packages, another language called “Python” is also popular with Data Scientists. Yes, you can do amazing things in R – in fact, part of … Continue reading Python for the Data Scientist
(Complete Table of Contents here: http://aka.ms/backyarddatascience) Journal Everyone uses a computer for lots of things, from e-mail to chat, from gaming to office work. And yet, there are some specific needs a Data Scientist has for their primary system. While I don’t recommend a specific brand or model (these things change too quickly to make … Continue reading The Data Scientist’s Computer
Caffeinated Data Science 