Learn Data Science and Machine Learning Essentials on edX

At one stage in my career, working for fifteen years in marketing, I realised that it wasn't just enough to understand what the data told me about my customers, but to also understand how to extract stories from the data. And to do that, I had to understand the techniques for analysing data - not just look at reports that other people gave me. I’m definitely not a data scientist, and my knowledge of statistical techniques is wafer-thin. But I knew enough to be able to understand the meaning of the data.

Roll forward 10 years, and we’re in a world that’s increasingly revolving around data and analysis. Where people talk about applying Machine Learning to a problem, and assume we all know what that means. And where doing ‘learning analytics’ is as simple as breathing. So how do you keep up to speed with changing techniques?

If you’re a little technical, then you might like to know that EdX are running a “Data Science and Machine Learning Essentials” course that’s free to join. I’ve just signed up alongside a few of the graduates who’ve recently joined our education team, and we’re going to see if we can create some kind of education analysis during it. Why not join us (you don’t have to be an aspiring Data Scientist to find this useful!). Here’s the details from the EdX team:

	edX: Data Science and Machine Learning Essentials ​Are you enterprising, tenacious, and creative? And maybe you have some experience with statistics, programming, and business analytics. You may well be ready to develop your career as a Data Scientist, and the demand for data science talent is exploding! Learn key concepts of data science and machine learning, and explore how to build a cloud data science solution with R, Python, and Azure Machine Learning from the Cortana Analytics Suite. Join experts from MIT and the industry, partnering with Microsoft, for this one-module-per-week, five-week edX course, starting on September 24. Learn about wrangling, munging, and visualizing, along with clustering, regression, and classification. See demos and participate in labs to find outliers, cleanse data, and build predictive models, based on real-world datasets. Don’t miss this chance to skill up on key concepts in data acquisition, preparation, exploration, and visualization, as you develop your career as a Data Scientist. And take advantage of the faculty-hosted office hour to address your specific questions.

This course is organised into 5 weekly modules each concluding with a quiz. By achieving a passing grade in the final course assessment you will receive a certificate demonstrating that you have acquired data science skills and knowledge. Apart from answering your questions on the forum, faculty (Cynthia Rudin, Professor of Statistics at MIT, and Dr Steve Elston, a self professed ‘big data geek’) will host an office hour to address questions you may have while undertaking this course.

	What you'll learn The data science process Overview of data science theory Data acquisition, ingestion, sampling, quantization, cleaning and transformation Building data science workflows with Azure ML Data science tools including R, Python and SQL Data exploration and visualization Building and evaluating machine learning models Publishing machine learning models with the Azure ML

The level of this course may be above my current skills, but I’m hoping I’ll find people around me that can fill any gaps. I’m sure you’ll find the same somewhere around your organisation. So, go on, sign up for something a bit different this month and geek out with me…

Find out more, and register free for the "Data Science and Machine Learning Essentials" course