Online Resources Can Help You Get Started in Big Data
The Big Data revolution is both exciting and surprising. There are so many possibilities that there is, essentially, no way to predict what Big Data will assist, discover, or unlock next. A Friday news report on NPR detailed how researchers at Microsoft believe that studying search logs could lead to early diagnosis of pancreatic cancer.
For established IT professionals, certification is an obvious path to greater involvement in the booming Big Data business sector. Many Big Data certifications, on the other hand, require a certain level of acquired knowledge on the part of the learner. You need a foundation of understanding on which to build you Big Data skill set.
One excellent means of broadening your Big Data knowledge base is free online learning. The impressive library of MOOCs at edX, for example, includes a wealth of Big Data offerings, hosted by some of the most prestigious universities, and biggest tech companies, in the world.
Among the available offerings are:
? Foundations of Data Structures (offered by Indian Institute of Technology Bombay)
? Big Data Analysis with Apache Spark (offered by University of California, Berkeley)
? Introduction to Apache Spark (offered by University of California, Berkeley)
? Distributed Machine Learning with Apache Spark (offered by University of California, Berkeley)
? Data Science Essentials (offered by Microsoft)
? Implementing Predictive Solutions with Spark in Azure HDInsight (offered by Microsoft)
? Implementing Real-Time Analysis with Hadoop in Azure HDInsight (offered by Microsoft)
? Principles of Machine Learning (offered by Microsoft)
? Processing Big Data with Azure HDInsight (offered by Microsoft)
? Wiretaps to Big Data: Privacy and Surveillance in the Age of Interconnection (offered by Cornell University)
? Machine Learning for Data Science and Analytivs (offered by Columbia University)
? Knowledge Management and Big Data in Business (offered by Hong Kong Polytechnic University)
All you need to take advantage of this wealth of free knowledge is a PC or laptop and a reliable internet connection. Many are even self-paced, allowing you to determine your own best study schedule and work at whatever pace suits your abilities.
If you have a little money to spend, then there are numerous other options. The University of California, San Diego, for example, offers a foundation-level Big Data specialization via online learning facilitator Coursera. The program consists of five courses and a capstone project and confers a Big Data certificate.
The introductory course is $59, and each subsequent installment is $89, which means you can get a university-level certificate for an economical $504. (If you qualify for financial aid, then you don't even need to pony up the full cost.)
If you're feeling a little more ambitious, and you already have a solid computer science background, then you can go even bigger. Also via Coursera, the University of Illinois at Urbana–Champaign offers a master's degree to aspiring data scientists. The total tuition load is under $20,000, which is fairly economical when you consider that a MBA from the University of Phoenix will set you back almost $30,000.
Let's scale the discussion back to introductory learning efforts, for a moment. There are a lot of good reading lists online, and if you have access to a good public library, then many of the recommended titles are likely available to you free of charge. There's a very solid introductory reading list here, another one here, and a third here that is limited in scope solely to 2014, evidence that our knowledge of Big Data is truly booming.
(There is some overlap between lists, but getting the same recommendation from multiple sources just makes the overall endorsement stronger, wouldn't you say?)
It would probably take years to intake all of the information described here, and we've barely scratched the surface. If you want to learn about Big Data in 2016 and you don't feel like the world at large has much to offer to you, then you're just being willfully ignorant. Get in there and start learning!