Creating an enum in Python prior to Python 3.4 was accomplished as follows:

def enum(**enums)::
      return type('Enum',(),enums)

then use as:

Animals=enum(Dog=1,Cat=2)

and accessed as:

Animals.Dog

The new version can be created as follows:

from enum import Enum

class Animal(Enum):
    Dog=1
    Cat=2

Structure Rule Language

To aid in the ease of rule authoring, Blaze Software, now Fair Isaac, created the proprietary Structure Rule Language (SRL), an object-oriented programming language designed to enable those with little or no background in software development to pen rules. Although the capabilities of this language are far too extensive to detail in this article, we can examine the basic rule syntax.

Rules in the SRL take the following form:

rule RuleName [at | priority M] is

One of the biggest challenges in pursuing a career in software development is to figure out which language you want to work. In addition to commonly used software programming languages like C, C++, C# and Java a lot of new programming languages such as Python, Ruby on Rails have surfaced especially because they are used by a lot of consumer based start-ups these days.

It could then be a daunting task to figure out the technical language you should learn which helps you prosper in a software engineering career no matter the technology advancements that happen in the marketplace. Learning a fundamental and universal language like C# could be a great start to your career as the language is very mature and extensively used by companies large and small

What is C#

Similar to Java, C# is a multi-paradigm, object oriented language developed by Microsoft. C# is intended for use in developing software components meant to be deployed in distributed environments. So in essence, learning C# can enable you to write applications for large and complex server side systems that use sophisticated operating systems as well as compact mobile operating systems such as Android

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.”