Daniel Gutierrez wrote a series of articles (three of them) at Big Data Republic presenting a number of free education opportunities focused on Big Data. His first article was focused on fairly high level, not really technical, resources with a target of upper or mid-level managers.
Daniel suggests joining a local Meetup.com group with an orientation toward a shared learning experience as well as looking into one of the many special interest groups with a big data focus homed at LinkedIn.com. He points to the Big Data/Analytics/Strategy group on LinkedIn as being one of his favorites.
We’re reminded by Daniel that there is a wealth of knowledge shared and available to watch on YouTube. He calls out the “What is Big Data” part 1 and 2 videos by IBM as specific examples as well as “What is Big Data,” by Explainingcomputers.com.
White papers, Webinars and Blogs are also resources that we are reminded not to forget as a great resources for learning. He offers “Big Data: Harnessing a Game Changing Asset” (registration required) at InformationWeek.com as a specific White Paper example and then points us toward Big Data Republic Webinars section. Noting that the blogosphere is alive with big data educational content, Daniel encourages us to check out an aggregator of big data blogs, Planet Big Data, to get a handle on whats available.
The last resource we’re given in this article is the granddaddy of big data educational resources: Big Data University. It is pointed out that the “Big Data Analytics – Demos” course is a good choice for managers, as it provides scenarios and demos showing big data analytics at work.
The whole of this, first in a series of articles, can be read at: Free Big Data Education: A Management Perspective.
The second article in Daniel’s series is focused on technical topics In the realm of big data with the intended target of serving the needs of IT personnel, managers with technical responsibilities, consultants, and developers who are new to this area. He points to IBM’s Big Data Hub as a particularly good big data portal and enumerates the following videos as a good place to start to learn about the different components of the Hadoop platform:
It is also noted that the many vendors of Big Data products produce and make freely available training material. Cloudera’s video library is called out along with a video of Cloudera CEO Mike Olsen entitled, “What is Hadoop”.
Daniel goes on to reiterate the suggestion from his first article to seek out local Meetup.com groups before recommending a powerful learning resource called Coursera. Coursera is a growing collaboration of 33 well known schools including Stanford, Caltech, Princeton, Duke, Brown, and Columbia. Specifically called out is the course, Web Intelligence and Big Data. Also mentioned is another valuable free online course resource is Edx.org, a not-for-profit consortium among MIT, Harvard, and UC Berkeley.
The second article of Daniel’s series can be read at: Free Big Data Education: A Technical Perspective.
The third and final part of this Free Big Data Education series finishes up with the area of big data known as data science. Data science— and the driving force behind it, machine learning— is the process of deriving added value from data assets. Commerce and research are being transformed by data-driven discovery and prediction. The skills required for data analytics at massive levels span a variety of disciplines and are not easy to obtain through conventional curricula.
Daniel again recommends seeking out local Meetup.com groups. As insight into these groups, we’re directed to visit the Field Report category at his blog, Radical Data Science, where he’s written intimate accounts of various meetings attended.
Using resources found within the Massive Open Online Course (MOOC) movement Daniel has built a Data Science “pseudo degree program” to follow. These free courses (some offer certifications) offer an excellent path toward obtaining the requisite background for becoming a data scientist.
- Lower-Division Courses
- Upper-Division Courses
- Graduate Courses
A selection of free data science books is also presented:
- Mining of Massive Datasets
- Bayesian Reasoning and Machine Learning (pdf)
- Information Theory, Inference, and Learning Algorithms
- Gaussian Processes for Machine Learning (pdf)
- The Elements of Statistical Learning
- Introduction to Machine Learning (pdf)
- Think Bayes (pdf)
- An Introduction to Data Science
This final part of Daniel’s series can be read at: Free Big Data Education: A Data Science Perspective.
- Big Data Explained: Real World Examples of Big Data (fliptop.com)
- Free eBooks | O’Reilly Media: Big Data Resources in Finance & Media Via the Pros (starbridgepartners.com)