When implementing a digital transformation most companies start with a Proof of Concept (POC) in order to prove the value. However, doing a POC where there is a lack of business engagement or clear problem definition is a problem for disaster.

Want to get better results in your predictive model? Use behavioral data over demographic or survey data.

Companies often perform penetration testing in order to take preventive action against cybersecurity attacks, however hacking can be applied to our personal lives as well. We either hack our own life or we allow someone else to hack it and wreak havoc on it.

To make your data science career a reality, it is essential to have a plan of action. In this post, I outline my 5 keys steps for creating a plan that will help you see results!

In this four part series on how to learn data science, I previously outlined the dimensions of data science and provided resources for learning data science. However, as previously mentioned, the best way to learn data science is to work through a whole problem from beginning to end.  

In a previous post I outlined the dimensions of data science and provided a checklist for the skills needed in each of the four areas. In order to help you navigate these dimensions and create your own learning journey, I’ve compiled a list of my favorite books, articles, and classes for each of these areas along with some general tool recommendations.

One of the most common questions I get asked is, “what skills do I need to be a data scientist?”. To help with this question I wanted level set on what I believe are the core competencies of data science and break down these dimensions into a format that could be used as a checklist to self evaluate yourself for your learning journey.

Working in the world of data, humility is usually the last thing I think about. I’m not a proud person by normal standards, you won’t find me shouting in meetings when I don’t get my way, or telling people how much more I know than them. However, it’s recently come to my attention that I need a dose of humility,  especially in my work with data.

If someone would have told me 10 years ago I would be working in data science I would have laughed, and then most likely asked, “What is data science?” As much as I love planning my entire life, and have a Type A personality, finding the world of data has been one of the best unexpected things that has happened to me.

After every meetup, networking event or presentation I attend on analytics, the question often asked is what tool you use and why. A valid question, as new tools enable us to progress faster and easier in the field of analytics. However, the conversation quickly goes from which tool do you use, to an identity crisis and heated debate. Admit it, we’ve all sat through an awkward conversation of two people discussing why their tool/language is superior. 

So you’re not planning on becoming a data scientist or analyst anytime soon, but is it still important to become data literate? What does data literacy even mean?

We all know that literacy is foundational for one to be a functioning member of society. However, there no longer exists a time were merely knowing how to read and write is enough to survive in the age of information. As all things evolve, even literacy has evolved. When computers became affordable society soon followed and become computer literate. Schools began offering classes to teach computer literacy and libraries offered the community access to computers. Without being computer literate one would find difficulties getting a job, but now we face a new type of literacy we must learn and that is data literacy.

Everyday my inbox fills up with new articles and posts on R tutorials, data viz tips, and the latest and greatest tricks to increasing my modeling predication accuracy. I enjoy reading many of these articles, but often I find that only focusing on the “hard” skills leaves me in a rut. 

One of the first things any good data scientist does before building a predictive model is to explore their data. The temptation can be to rush through this exploration stage in order to get to a working product faster.  However, if we take just a little more time in this area we can save hours of work, build better models and troubleshoot issues much faster. To get you started, I’ve outlined a few principles for data exploration.

As data scientists, it is easy to get wrapped up in the data. You go to conferences and get stickers that say “I heart data”, you drink out of “I heart data” mugs, and if you are really on your high priest pedestal you wear a shirt that lets everyone know how much better you are because you trust DATA!