The number one question asked by people who attend our webinars & in-person events that has now been asked too many times to not be turned into a blog post: what are the four skills necessary to become a data scientist?
In any order of importance, they are: programming, statistics, communication, & domain expertise.Read More
Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational theory in artificial intelligence and data science. In 1959, Arthur Samuel defined it as a "Field of study that gives computers the ability to learn without being explicitly programmed".Read More
While there are many effective methods to teaching any curriculum, if you place a student in one method that benefits more from a different method, how will you know that was wrong or even more so, how do you know which method would be best for them, or combination suited? Enter human learning, real-time data driving machine learning, stemming from interactions between humans and the software medium chosen to teach them.Read More
How can data science, specifically computer vision, help us find the right shoe? By far, the worse part about buying a pair of brand new shoes is when you realize you have to either break them in or they didn’t fit as well as you thought they would before you bought them. New Balance teamed up with Volumental, a company with several PhDs in machine learning, have created and implemented 3D scanners that will now scan your feet in New Balance’s flagship stores: Boston, New York, and San Francisco.Read More
Is sentiment analysis useful in a field as impacted as finance? A grey area of prediction within investments has always been the “buzz” in current events, generated by news sources and general responses. From an outsider’s viewpoint, these events do have an effect on pricing, but how do we determine if it is a negative or if it is a positive effect? NN IP has found predictive value in sentiment analysis.Read More
Data science expertise and machine learning demand exploded when the world shifted gears at the presence of practical data analytical techniques. But how the data is collected is as much as a problem as how it is being executed and analyzed. Enter a solution, by those who created AI assistants: Google, Amazon, etc.Read More