If you’re a young, up-and-coming data scientist, then you’re probably looking for any opportunity to make a name for yourself in the industry. As a young professional, looking for that “big break” is important. In order for you to truly get your first opportunity to add a success to your resume, you need to really stand out from the competition. You need to have skills that no other contender has. One of the easiest ways to do this is by learning as much as you can, even outs

Data science. It’s a term you’ve heard used before over and over again in recent years, and even more so on this site. That’s because it’s an incredibly important sector in today’s society. Almost every major business uses data science in some way, shape or form. Data scientists study and analyze large sets of data and then offer businesses solutions to problems with this information. These businesses use data science for a variety of reasons, from the traditional to the unex

In the (literally) ever-growing field of data science and machine learning, it can be difficult even for those involved to stay up to date on the latest, cutting-edge information and research. And with the ever-increasing public awareness in all things AI, it is as good a time as any to learn more about the ever-increasing world of data analyses. With that said, I have compiled three books that I think every data scientist should read: Machine Learning Yearning, by Andrew Ng

History is an important teacher. It can remind us of where we came from and offer insights into how we can improve our past processes. It is important that we look back on history often, if only for a few moments. That is precisely why I have created this series of blogs. In this third entry, I will continue to take a quick glance back at the history of data science. 1997: This was an interesting year for the industry, as we saw our first glimpses of the push to change the in

Data science, as I’m sure we all know, is an industry steeped in history. Many people assume that it is an industry born within the last 10 years, however, this is not true. Last month I’d written a blog that showcased the storied history of data science. Naturally, data science has such a lengthy history that I simply couldn’t fit it all into one single post. That being said, I figured I would continue the series and highlight a few other important moments in data science’s

If you’re currently the owner of a business, you understand how important data scientists are. For those that are unaware, data scientists look at, collect and analyze large sets of data and then report their findings in an easy-to-read manner to be used to solve problems. Data scientists are in high demand in several industries as more companies are finding themselves overwhelmed by large sets of data. If you’ve just hired a data scientist, or are looking to do so, you might

In my first blog, I discussed business analysis and its importance to any industry. My job as a business analyst is to look at any pre-existing company, analyse it from top to bottom, and provide a bevy of solutions to increase profitability, productivity, organization, etc. For decades, this job could only be completed by a human being. However, as our world constantly moves towards automation and relies heavily on technology, new software has emerged that could potentially