Unlike traditional online courses that charge a fee, limit enrollment and provide credit or certification, Moocs (massive open online courses) are usually free or low cost and can host hundreds of  thousands global participants.  Although MOOC have been around for years in the form of collective techie learning gatherings, participation in 2012 has ballooned at a rapid pace likened to FaceBook in its heyday.  According to The Year of the MOOCarticle in the New YorkTimes, edX, a nonprofit start-up backed by Harvard and MIT, had 370,000 registrants in the fall of its first official courses. This paled in comparison to the amount of students that Courseraattained in its first year of online learning opportunities, 1.7 million!

Will MOOCs Replace education as we know it?

Like any new trend, massive participation in online classes has its challenges. Lynda Weinman has ample experience when pointing out that they are by no means a replacement for formal education.  As a former digital animator, special effects designer and classroom college teacher, Linda paved the path for an earlier version of MOOC education in the mid 90’s when she founded Lynda.comas an aide to her own students. Over four million students and 2,200 courses later she’s confident when clarifying that many of the collegespartnered with Lynda.com use the tutorials as added features to their existing courses.  When asked in an interview with ReadWriteBuilders, if high technical companies look at online programs in terms of advancement as a supplement to traditional education or as a way for people to further their careers, Lynda feels that “it’sjust one example of something that you can do to enhance your attractiveness to potential employers. But [it’s also important to have] a portfolio and body of work, references that actually work out, showing that you had success in the past.”

MOOC Benefits:

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

Data has always been important to business. While it wasn't long ago that businesses kept minimal information on people who bought their products, nowadays companies keep vast amounts of data. In the late 20th century, marketers began to take demographics seriously. It was hard to keep track of so much information without the help of computers.

Only large companies in the '60s and '70s could afford the research necessary to deliver real marketing insight. The marketers of yesteryear relied upon focus groups and expensive experiments to gauge consumer behavior in a controlled environment. Today even the smallest of companies can have access to a rich array of real-world data about their consumers' behavior and their consumers. The amount of data that is stored today dwarfs the data of only a few years ago by several orders of magnitude.

So what kind of information are businesses storing for marketing purposes? Some examples include:

- Demographic information — age, gender, ethnicity, education, occupation and various other individual characteristics.

Unlike Java, Python does not have a string contains method.  Instead, use the in operator or the find method.  The in operator finds treats the string as a word list whereas the find method looks for substrings.  In the example shown below, 'is' is a substring of this but not a word by itself.  Therefore, find recoginizes 'is' in this while the in operator does not.

s = "This be a string"
if s.find("is") == -1:
    print "No 'is' here!"
else:
    print "Found 'is' in the string."
    
if "is" in s:
    print "No 'is' here!"
else:
    print "Found 'is' in the string."

#prints out the following:
Found 'is' in the string
No 'is' here!