As we begin a new year and continue our way through the great resignation, many of us are looking to upskill or reskill to enter a job that fulfills our needs and brings us joy. As much as I am a fan of following one's passion, passion alone may not be enough.

For five years I’ve been taking a “Think Week” once a year. This usually happens between the time of Christmas and New Years when most people are already off work and it was easy for me to shut down distractions, reflect on the past year and plan for the year ahead. More recently though I have started to take a think week on a quarterly basis and I don’t see myself stopping anytime soon.

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.

The biggest myth about Candida is that it is caused by the food you eat. After years of changing my diet, I finally came to realize that for me, Candida is not activated by the food I eat but rather from stress.

Let’s be real. No one is going to care about your career as much as you do, and they shouldn’t. It’s your career. Most companies have a promotion system set up for you to progress in your career. However, this does not mean that progressing from Manager, to Sr. Manager, to Director etc. = additional happiness and fulfillment in your work. Although the salary may be higher, what most people are looking for in a career is work that is fulfilling and has meaning. The beauty of seeking fulfillment and meaning is that when you get these things right, success is bound to happen. 

If you’re looking to continue your development and growth, reading is key. A large factor into who I am today is based on the books I’ve read over the years. I continually find books to be my main source of inspiration, motivation, and learning. Because books have have played such an important role in my life, I wanted to share some of my favorite books I’ve read in the past year in the categories business, science, and personal development.

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!