Online courses are a great way to share knowledge with others; that is why I have decided to launch a few courses. The first course is Intro to Azure ML Studio – Regression. This is a smaller course and should take about 2 hours to complete.
Azure ML Studio is a drag-and-drop interface for doing machine learning.
Topics are all based upon Azure ML Studio, and they include:
R is a hugely popular language among data scientists and statisticians. One of the difficulties with open-source R is the memory constraint. All the data needs to be loaded into a data.frame. Microsoft solves this problem with the RevoScaleR package of the Microsoft R Server. Just launched this week is an EdX course on Analyzing Big Data with Microsoft R Server.
According the syllabus:
Upon completion, you will know how to use R for big-data problems.
Full Disclosure: I work at Microsoft, and the course instructor, Seth Mottaghinejad, is one of my colleagues.
If you are looking to learn data science but do not have the time or money for a full master’s degree, Data Society might be your answer. Data Society is an online data analysis skills training program that is designed by educators and curated by data science experts. The learning experience is online and includes:
Printable step-by-step guides
Reusable Coding Templates
Opportunities to build a Portforlio
There is one other completely awesome feature of Data Society. For every membership purchased, they provide a free membership to help someone in need. Data Society is currently running a Kickstarter to build a community for learning data science. Your support would be greatly appreciated (I am not involved in the project but I am always happy to share innovative educational opportunities for data science).
Recently, I was able to get a brief interview with Merav Yuravlivker, one of the founders of Data Society.
There are many data science learning resources on the web, how is Data Society different?
We understand that most people do want to learn these skills, but don’t feel like they have the time, the money or the background. We eliminate all those barriers to entry by providing short lessons that are taught intuitively with real data sets. It’s the first platform that’s designed with working professionals in mind. Not only do we teach our students how to analyze data, but we also have a separate track for managers that teaches them how to implement data-driven strategies in their teams, how to hire a data scientist, and how to communicate effectively with their employees. Our courses are not just videos, each course includes ready-made data analysis templates in R that decrease the time it takes to do the work, a step-by-step printable guide that can be used as a reference for every stage of the analysis, and live, dynamic forums where students can get all of their questions answers by the Data Society team as well as other students. In short, we provide everything someone needs to learn new skills in a much shorter amount of time.
What is the Kickstarter about?
Our Kickstarter campaign is about building that community around learning data science and helping others solve problems – we’ve already released the first three courses in our curriculum, and we’re excited to give our supporters an opportunity to see exactly how their contributions can make an impact. Our mission is to increase data literacy across the workforce – we know that data analysis skills are widely valued and sought-after, which is why we’re partnering with non-profits who help veterans and low-income individuals get back to work. For every membership bought off of Kickstarter, we will give one to someone who can use these skills to become more marketable and improve their life.
Is there anything else you would like to tell me about Data Society?
The most frequent compliment we get from students is that they didn’t feel intimidated to learn data analysis skills. As an educator, that is the biggest reward to me because we’re opening up possibilities for individuals who didn’t think that they had the ability to analyze data and pull insights from it. Our goal is not to turn everyone into a data scientist, but rather to give everyone the ability and confidence to get new data, look at the data they already have, ask “How can this data help me solve this problem?”, and then discover those insights that will help them make better decisions.