The line between IT consulting and management consulting is quite often blurred, with overlaps between the two fields habitually happening. Worse still, most people do not understand who an IT consultant really is, or what he/she does. There are those who think the job entails fixing computers, others – selling computers and associated accessories. This is misleading though.

In a nutshell, IT consultants are professionals who aid businesses in deciding what computer tools and technologies are best placed to grow and sustain a profitable business. They work hand in hand with clients to help integrate IT systems into the latter’s business. They show clients how to use technology more efficiently, and in so doing, the client is able to get a higher return on their technology investments, and ultimately, increase the bottom-line.

IT consultants, or IT advisories, could work independently or for a consulting firm, with their clientele spread across all sorts of businesses and industries. Companies hire or contract the consulting firm to come in and analyze their IT systems and structure.

The job itself is not short of challenges, however, and the path to becoming a successful IT consultant is fraught with its fair share of ups and downs. But hey, which job isn’t? Experience is the best teacher they say, and only after you’ve worked as a consultant for a number of years will you finally gain invaluable understanding of what is expected of you. Learning from the experiences of those who’ve been in this business for long is a good starting point for those who decide to venture into the world of IT consultancy.

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 Python, we can create three types of methods in a class:  instance or regular method, classmethod and staticmethod.  Instance methods are associated, as the name infers, with an instance or object of the class and take self as the first parameter.  Classmethods take a reference to the class, cls, as the first parameter of the class.  Staticmethods, for the most part, are convenience methods that could be declared as functions since they really do not have much to do with the class itself.  They were probably added at some time after the advent of Python in order to make the language more object oriented i.e. minimize the number of free floating functions. 

Refer the our article static, class and regular methods in Python for a detailed explanation on this subject.

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.”