What Your Skills and Buggy Whips Have In Common!

In the movie, Other People’s Money, Danny DeVito stars as a ‘Larry the Liquidator’ who comes to a failing New England company to buy it and sell off the pieces. He gives a compelling speech to the stockholders comparing the company’s product to buggy whips. I want to share a few lines from that speech.

“You know, at one time there must've been dozens of companies making buggy whips. And I'll bet the last company around was the one that made the best buggy whip you ever saw. Now how would you have liked to have been a stockholder in that company? You invested in a business and this business is dead. Let's have the intelligence, let's have the decency to sign the death certificate, collect the insurance, and invest in something with a future.”

What does this have to do with your skill set? Recent times have seen seismic shifts in technology and if your skills are based on legacy technologies, you could be in trouble. When I started in IT, mainframes were the core platform and languages like COBOL where supreme. Too many professionals did not adapt to emerging technologies and ended up without a job and no prospects. The sad thing is that it sneaks up on you. As long as you have work, you don’t see the erosion in demand for your skills. Then one day, you lose your job and must dust off your resume only to find your skills have atrophied and employers want something else. It happens suddenly and without warning. 

What can you do? Step one, become aware. Don’t stay safe in your office coding away “business as usual”. Go to technical events, read blogs, and find other media to see what is happening. Read up on the new features of the platform you use whether it is database, web application, mobile, or whatever applies. Where is it going? Are you enhancing your skills with the emerging technology? Do not rely on your employer to do this for you. It is your responsibility. 

What are the major new trends? I think the critical new trends are Data Science, Big Data, and Cloud computing. I mean these as broad categories which can encompass things like robotics and Internet of Things (IoT).  These technologies are intertwined and can only be appreciated when taken together. Here’s what I mean. Data Science uses statistical analysis to gain new insights. Often the volume of data is massive or unstructured which is where Big Data tools like Spark and Hadoop come in. But wait, my organization does not have the personnel and resources to set up such a complex platform. What do we do? Enter Cloud computing. Cloud platforms such as Azure support instant creation of complex data science platforms that can be dynamically scaled up and down as needed. Tools such as R and Python can be used to perform the analysis and the resultant data can be used on premise or in the cloud. 

What about your skills? If you work with data, I recommend learning about data science first. I get a lot of questions about which language someone should learn. In general, I don’t think it matters because a language is a technical implementation of a paradigm and will come and go while the underlying requirements the language services will continue. Regardless of the language you use, you will learn the concepts and can apply them using other tools fairly quickly. However, if your organization does not have a standard data science language, I recommend starting with the R programming language. 

Why R and not Python? Both are free and open source. Both are very popular. However, for non-programmers I think R is easier to learn. Python has many distributions available with various delivered extensions called modules. Python also has two incompatible versions which are 2.7 and 3.n. The Python creator decided to make improvements to the language that are not backward compatible. The last original version of Python release was 2.7. All new versions will not be compatible with 2.7 and earlier. Bear in mind, most Python code is still in version 2.7. Many Python modules have not been upgraded to 3.n so you may find a module you want is not available yet. 

If you learn data science with R and need to use Python, you can easily switch because when Python does data science work, it emulates the way R does things. For example, the most popular Python data wrangling package, Pandas, was designed to mimic R’s data frames. The number crunching library, NumPy, emulates R’s vector processing. 

If you work with data, you need to learn about Data Science. To do that, R may be the best place to start. There are many good books, videos, and training courses available to help. You can download and install R from the Comprehensive R Archive Network (CRAN) at the link: https://www.cran.r-project.org/

I recommend you also download a great open source development environment called RStudio which can be downloaded from: https://www.rstudio.com/products/rstudio/download/#download

To help get you started, you can view my YouTube videos below:

Introduction to R Programming: https://www.youtube.com/watch?v=_vhQRQsSobY

Introduction to R Studio: https://www.youtube.com/watch?v=eJv8nFXI0Mk&feature=youtu.be

If you want a full day of hands on training, I and a colleague will be offering a class on December 8th entitled ‘Data Science with R Programming from A to Z Workshop’. You can register for this at: https://www.eventbrite.com/e/data-science-with-r-programming-from-a-to-z-workshop-tickets-39263385844

Use discount code Microsoft for 20% off the price. 

There are two ways to handle new technology. One is to avoid it, fearing it will be difficult to learn. The other, far more useful way, is to be like a kid in a toy store. Embrace all the cool new things as opportunities to have fun, grow, and learn. I have found that both take a lot of energy, so I prefer the later. New tools offer you new and exciting ways to do your job. I am glad I left COBOL behind and I hope you will join me in embracing the future!