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Data School


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".

Why I created the FT Visual Vocabulary in Tableau - The Information Lab


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.

How to launch your data science career (with Python)


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.

Machine Learning Education: 3 Paths to Get Started


Machine learning is the predictive heart of big data analytics, and one of the key skills that separates data scientists from mere analysts. But getting started with machine learning can be a challenge. Here are a few ways beginners can get off the ground with their machine learning adventure. Machine learning is a vast field with many different specialties, so it's quite easy for a beginner to get overwhelmed. For instance, one specialty called deep learning powers many of today's artificial intelligence breakthroughs.

Machine Learning with Text in Python (online course)


Data School's 8-week Master Course begins September 28. More than two-thirds of the available spots are gone! Learn more about the course and enroll: This info session was recorded on September 13. View the chat history and complete Q&A:

Getting started with machine learning in Python (webcast)


Have you heard about machine learning, but you don't really understand what it's good for? Or you understand the basic idea, but you're struggling to apply it using Python? In this video, I'll explain the essential ideas behind machine learning. Then, we'll build our first machine learning model in just a few lines of code using Python's scikit-learn library. This is a recording of a webcast hosted by Trey Hunner of Weekly Python Chat:

Machine Learning with Text in scikit-learn (PyCon 2016)


Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn. Subscribe to the Data School newsletter: OTHER RESOURCES My scikit-learn video series: My pandas video series: JOIN THE DATA SCHOOL COMMUNITY Blog: