Archive Monthly Archives: June 2017

Speaker Tip: Warm Up Your Audience With Conversation

Ever give a talk when you walk up to the lectern and everyone is chatting and not paying attention? You end up wasting several minutes of your time slot while everyone settles in. It kills your talk’s momentum and makes it harder to get everyone’s energy up. You’re starting your talk in the hole.
This is an easy problem to fix. 
When you begin to setup your room, start a conversation with people in your audience. Try to get in right after the previous speaker leaves. That way you can setup your gear and still have plenty of time to schmooze. Start off by asking your audience to help you with AV. Most rooms are a little different, so you usually need to tweak your settings. (Bonus tip: Don’t wait until your presentation to adjust this stuff, that’s an amateur move.) “Is this text big enough?” “Can you hear me okay?” etc… If you do this enough times, you’ll be close, but it’s nice to get some feedback.
After you get setup, ask your audience more questions. Ask them where they are from. Ask them about their tech stack. Gather information for your talk. Use this information to customize your presentation to the people who in the room.
Have a bunch of Java people? Reference some of their culture or relevant technologies. Doing a web talk in a room full of web noobs? Spend more time on the basics. Have a room full of .NET people? Explain new concepts using familiar terms. For example, I use attributes in C# to explain decorators in TypeScript. Use local jokes and references. Ask people about their concerns and try to address them in your talk.
Make your talk a conversation, not a monologue. 
Ask people about previous talks in the conference or other conference activities. Specifically, ask them about talks they enjoyed. Besides being fun, it gets people to associate you with other good speakers. If you have a bunch of people from a previous talk, you can reference that talk in your own. This is also a good way to learn about new speakers or topics to check out.
There are other benefits to starting with conversation. Leading the conversation allows you to take control of the room early. That way, when your time slot begins, you’ll already have everyone’s attention. This maximizes your speaking time and the value you deliver. By leading the discussion, you can build energy. You can joke around with your audience and build a rapport. You can begin your talk with an engaged and energetic audience, which is ideal.

The next time you give a tech talk or presentation at work, show up early and start a conversation. It’s a great way to get things moving in the right direction.

R for .NET Developers: Why Bother?

I spend most of my time working with Microsoft web technologies, or as I like to refer to it, “.NET and Friends”. While I’m a big fan of the web, I’m always looking into new areas of development. One of those areas is data analysis. We are awash in data and learning how to process it is a valuable skill. Until recently, there wasn’t much in the Microsoft ecosystem for doing this kind of work. This isn’t a bad thing, but it’s nice to be able to use familiar tools to learn new things.
Fortunately, Microsoft has made some serious investments in the data analysis space. You aren’t going to be using C#, but Visual Studio now supports R. R is a language made “by statisticians for statisticians”. It’s one of the premier data science technologies and a great way to learn statistics. Microsoft also has R support in SQL Server.
In this post, I’m going to cover a few of the reasons R is worth a look. Even if you are not planning on donning the data scientist hat anytime soon.

The Power of Polyglot

This is sometimes forgotten in the .NET world, but different languages are good for different things. If you build web applications, you already know this. For example, if you want to build a modern web application, you need at least three different languages (JavaScript, CSS, and HTML). More likely you’re looking at six or more (JavaScript, Typescript, SASS, CSS, C#, HTML, XML, and Markdown).
Every language does certain things better. You should use the language that does the job best, rather than trying to shoe horn your language of choice. In the data analysis space, this is no different. The two most popular languages for data analysis are R and Python. While Python is a viable option (and supported in Visual Studio as well), R is purpose build for data analysis. You can do data analysis in either, but R does it with less code. 

In addition to the productivity benefits of using the right tool for the right job, it’s good for your personal development to learn new programming languages. The Pragmatic Programmer recommends learning a new one each year. Learning new languages improves your thinking and makes you better at your primary development stack.

Data Is The New Oil

“There’s gold in them servers.”

Data is money. Large companies are using the data you generate as a goldmine. Uses range from using data to optimize advertising to using it to make even more addictive products. In addition to user generated data, we also have the mountains of data generated by IOT devices. Sometimes we can use it for small gains, like using a Nest Thermostat to optimize your heating and cooling, but sensor networks can have a much greater impact.  We have access to more data than in all of human history. If you can figure out how to mine insights from that data, you will be rewarded handsomely.

If You Care About Truth, Data is For You

“There are three kinds of lies: lies, damned lies, and statistics.”

With plenty of data comes plenty of people using that data to manipulate you. Every political cause has a stable of statistics behind it. Even if they fall apart under scrutiny, people believe them because numbers sound fancy. People trying to sell you something use numbers to appear more credible. If you want to thrive in our data soaked economy, it’s essential to become data literate, so you can spot these manipulations.

R is for Learners

R has several features that make it a great tool for learning about data analysis. First, it’s really easy to learn. R is a simple language that you can pick up in a few hours. Additionally, R has an easy to use built in help system. If you need info on any command or method, it’s a few keystrokes away. R also has a lot of built in data sets to play with statistical techniques. This includes lots of popular demo statistical data sets that are well known in the statistics community.

Playing Nice With Others

As data analysis becomes more prevalent in the enterprise, you’re probably going to wind up working with data analysts and data scientists. Learning about some of the tools and techniques they use gives you common ground. It’s the same reason software developers should develop business and industry knowledge. Being able to connect with your team members on their terms makes you more than a run of the mill software developer.


If you’re an enterprise developer, R is worth a look. You can use R to learn valuable new skills using familiar tools. With a little effort, you’ll be able to slice and dice data for fun and profit.

To learn more, check out my post on R Resources

A Few of My Favorite R Resources

In an effort to improve my data analysis skills, I’ve been learning and speaking about the R programming language. Even if you don’t want to be a data scientist, (whatever the hell that means this week) learning some analysis skills can pay dividends. Data literacy is an essential skill in our data soaked economy and R is a good learning tool for analysis skills.

One of the harder things to do when starting in a new area is finding useful resources. It’s tough to find the digital needle in the web powered haystack. To make your life a little easier, here’s a list of the R resources I found to be useful.

Setting Up R

There are three paths to getting R setup on your machine. If you’re a Visual Studio 2017 user, the easiest way to get R is to install the Data Science workload in Visual Studio. This will get you the Microsoft flavor of R and R Tools for Visual Studio.

Installing R Tools for Visual Studio

If you’re not into Visual Studio, you can also install an R interpreter and R Studio. R Studio is a free R IDE. For interpreters, you can go with either the Microsoft flavor or the standard CRAN flavor of R.

R Windows Installer
Microsoft R (optional)
R Studio

If neither of those options work for you, you can also run R in a Jupyter Notebook. Jupyter is a web-based environment that makes it really easy to mix text and code. It’s used in many contexts including scientific research and virtual textbooks. To setup a local copy, start off by installing Anaconda. Anaconda is a data science environment that includes a plethora of handy analysis tools. After you install Anaconda, you’ll need to install R using the conda package manager. Then you can run Jupyter using the “jupyter notebook” command.

Anaconda Download


conda install -c r r-essentials
jupyter notebook


R Studio Cheat Sheets
A collection of useful R related guides in PDF format.

R Tutor Tutorials
This site came in handy a few times while trying to find specific R issues.

Flowing Data
Flowing data has a variety of useful articles on R and other data topics.

Don’t forget about the built-in R help system. Prefix any command with a question mark and it’ll search the R documentation for you. (Example: “?kmeans”)


I skimmed through a bunch of books on R, but the one I really liked was R: Recipes for Analysis, Visualization and Machine Learning. The writing was clear and the content was pragmatic. The task based format was easy to follow and implement. Another book that I used was R for Data Science.

R: Recipes for Analysis, Visualization and Machine Learning
R for Data Science
This list of resources is enough to help you get started in learning R. Go forth and learn how to slice and dice your data.