Have you heard of "machine learning", and you're trying to figure out exactly what that means? I'll give you my definition, provide some examples of machine learning, and explain at a high level how machine learning "works".

As the Head Coach of The Data School, I need to be on top of all things Tableau so that I can pass that knowledge onto our trainees. What I really hope they pick up is my method for learning and my constant to desire to get better at my craft. While that's fantastic, unless they have an approach to problem solving, a thirst for learning, and a passion for sharing, I have failed as their coach, teacher, and mentor. A poster and web site to assist designers and journalists to select the optimal symbology for data visualisations, by the Financial Times Visual Journalism Team. The FT Visual Vocabulary is at the core of a newsroom-wide training session aimed at improving chart literacy.

If you're interested in the exciting world of data science, but don't know where to start, Data School is here to help. Data science can be an overwhelming field. Many people will tell you that you can't become a data scientist until you master the following: statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more. So, what exactly is data science? This workflow doesn't necessarily require advanced mathematics, a mastery of deep learning, or many of the other skills listed above.