Image Source: MIT
Earlier in the year I signed up for this, and then last month I received the e-mail that the course was starting. MIT's Tackling the Challenges of Big Data was a great deal of information to take in over the period of a month.
Each week they offered a series of videos for us to watch, and then we would take an assessment to test our knowledge. Today I finally read the flyer for the course where it states "This course is designed to be suitable for anyone with a bachelor’s level education in computer science". This is very true as parts of the course goes into great details of computations.
The professors in the course very much reminded me of my graduate school professors at George Washington University, although a few, such as Professor Michael Stonebraker, were very outspoken. In one of the weekly discussions we were asked if we agreed with his outspoken opinions - this is something that I as a college student couldn't do in the past. I enjoyed his lectures a great deal.
Professor Stonebraker said everyone will move to columnar databases in 10 years. Why isn't adoption at a faster pace? Because data warehouses are "sticky" and slow to move. Professor Stonebraker felt that HDFS was too slow to report on.
What is different from this MOOC than SAP's? For one, MIT charges for the course. Additionally a few times MIT offered live office hours with the professors. In one of them, Professor Sam Madden was asked about SAP HANA and In-Memory Computing - he said he found it interesting, particularly with the amount of data that can be stored in memory.
This course takes up a lot more time than the SAP MOOC's; I spent several hours, every Saturday, watching videos and doing the assessments. MIT also had a nice feature where we could speed up the lecture 1.5 times, 2 times - this made some of the video watching go faster. The other thing I did differently in this course was I did not take any notes, yet I still succeeded with the assessments. I've felt with the SAP MOOC's assessments I needed the notes to answer the assessments.
Another area of the course was around big data visualization. Professor David Karcher lead this and during his office hours he made a point that big data visualizations need to be "real-time" and interactive. This made me think of SAP BusinessObjects Design Studio and SAP BusinessObjects Lumira
We learned about Hadoop, map reduce, and the technologies behind that. We also learned business cases about health, fraud, machine learning and more. I also learned about Data Wrangler with Lumira - for Cleaning and Transforming Data and Map-D Tweetmap
"Big Data Analytics" is not about BI as I think of SAP BI; in the course it is about fast algorithms and machine learning. Diginomica's Dennis Howlett has been encouraging me to get out the "SAP Zone" and this course did that.
While course registration is closed, you can sign up to receive alerts for the next MOOC. I recommend it.
ASUG Annual Conference
Learn more about Big Data from customer sessions at ASUG Annual Conference:
Adobe Shares Managing Big Data Across a Logical Data Warehouse with SAP HANA, SAP IQ, and Hadoop
Norwegian Cruise Line’s Industry-Leading Blueprint to Leverage Big Data for Competitive Advantage
Also learn about "Big Data" or HANA from the following ASUG Pre-conference sessions:
Jump Start ASUG Annual Conference SAPPHIRE with a Pre-Conference Session - Back and Better than Ever
In-depth sessions on EIM at ASUG Annual Conference: Try a pre-con!
If you have a big data story to share, ASUG invites you to submit an abstract for SAP dcode for Las Vegas (aka SAP TechEd) - call for presentations is planned to start April 21st.
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