This section of our beginning python training class always stumps students.  Firstly, because they need to know the difference between a function and a method.  Secondly, they need to understand object oriented programming concepts.  Thirdly, they need to realize that python has three types of methods.  Then they need to know how to use each method, which means they need to know the purpose of each method type.  Then they have to understand mutable versus non-mutable types.  The list goes on.  As part of our python tutorial, I hope to shed some light on this confusing topic.

To begin, the difference between a function and a method in python is that a method is defined within a class.  Here is an illustration:

#function

	def greeting():
	                print "Hello, I hope you're having a great day!"

	class HSGPrinter(object):
	                #method
	                def greeting(self): 
	                                print "Hello, I hope you're having a great day!"

As should be obvious, the second definition of greeting is encapsulated within the HSGPrinter class and is , therefore, refered to as a method.

The astute reader will notice that the greeting method contains one parameter named self.  For those who know C++ , Java or C#, self is equivalent to this i.e. it is a reference to the invoking object:

In most business circles, the question of whether or not a website truly helps a company's business has become somewhat moot. Simply put, a website is a necessary evil, like it or not. The question is no longer, should a company have a website, but rather, is the website optimized to ensure the best potential results. Of course, it is important to understand what is meant by "helping a company."

Many businesses are under the assumption that a website is going to turn into cold hard cash for the company. Well, that could be the case if the organization is using a type of e-commerce platform to buy and sell goods. Many businesses are service oriented and as such, the website serves an entirely different purpose.

Disruptive technologies such as hand-held devices, cloud computing and social media are rattling the foundations upon which traditional businesses are built. Enterprise customers have grown smarter at ensuring the latest technological trends work in their favor. Everyone is trying to zero in on their core competencies by employing commodity services to run their business.

Likewise, enterprise application vendors need to zero in on their core competencies and enhance more value to the businesses of their clientele by leveraging standards-based commodity services, such as IaaS and PaaS, provided by leaders in those segments (e.g. Amazon EC2, Google Cloud Platform etc.).

What else enterprises need to do is learn to adopt new and emerging technologies such as cloud, utility and social computing to build on them to penetrate new market avenues.

New small and medium-sized entrants into the market are constantly challenging enterprises given their ability to rapidly turnaround and address the requirements of the customers in a cost-effective manner. Additionally, these new advancements also affect how enterprises create, deploy, and manage solutions and applications. If you take the example of Force.com, for instance, you find that it’s a common war zone for enterprise application vendors to furnish SME markets with their applications, with the new entrants mostly having an edge.

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”