The mainstay of a corporation is the data that it possesses. By data, I mean its customer base, information about the use of its products, employee roles and responsibilities, the development and maintenance of its product lines, demographics of supporters and naysayers, financial records, projected sales ... It is in the organization of this data that advancements to the bottom line are often realized i.e. the nuggets of gold are found. Defining what is important, properly cataloging the information, developing a comprehensive protocol to access and update this information and discerning how this data fits into the corporate venacular is basis of this data organization and may be the difference between moving ahead of the competition or being the one to fall behind.

Whenever we attempt to develop an Enterprise Rule Application, we must begin by harvesting the data upon which those rules are built. This is by no means an easy feat as it requires a thorough understanding of the business, industry, the players and their respective roles and the intent of the application. Depending upon the scope of this undertaking, it is almost always safe to say that no one individual is completely knowledgeable to all facets needed to comprise the entire application.

The intial stage of this endeavor is, obviously, to decide upon the intent of the application. This requires knowledge of what is essential, what is an add-on and which of all these requirements/options can be successfully implemented in the allotted period of time. The importance of this stage cannot be stressed enough; if the vision/goal cannot be articulated in a manner that all can understand, the knowledge tap will be opened to become the money drain. Different departments may compete for the same financial resources; management may be jockeying for their day in the sun; consulting corporations, eager to win the bid, may exaggerate their level of competency. These types of endeavors require those special skills of an individual or a team of very competent members to be/have a software architect, subject matter expert and business analyst.

Once the decision has been made and the application development stages have been defined, the next step is to determine which software development tools to employ. For the sake of this article, we will assume that the team has chosen an object oriented language such as Java and a variety of J EE components, a relationsional database and a vendor specific BRMS such as Blaze Advisor. Now, onto the point of this article.

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.

Google is one of the most popular websites in the entire world that gets millions of views each day. Therefore, it should come as no surprise that it needs a strong and reliable programming language that it can rely on to run its searches and many of the apps that Google has created. Because of this, Google uses Python to ensure that every time a user uses one of their products, it will work smoothly and flawlessly. That being said, Google uses Python in a variety of different ways, outlined below.

Code.Google.Com
Since its creation, Google has always used Python as part of its core for programming language. This can still be seen today considering the strong relationship the two have with one another. Google supports and sponsors various Python events, and Python works to better itself so that Google remains on top of cutting edge material. One way that they do this is by working with code.google.com. This is the place where Google developers go to code, learn to code and test programs. And with it being built on Python, users can experience exactly what it is that they should expect once they start using the real site.

Google AdWords
Google AdWords is a great way for people to get their websites out there, through the use of advertising. Each time a person types in a certain string of keywords, or if they have history in their cookies, then they’ll come across these AdWords. The way that these AdWords are broadcasted to online web surfers is built on the foundation from Python. Python also helps clients access their AdWord accounts, so that they can tailor where they want their advertisements to go.

Beets
If you have loads of music, but some of it is uncategorized or sitting in a music player without a name or title, Beets is for you. This Google project uses Python and a music database to help arrange and organize music. The best part about Beets is that even if it doesn’t run exactly the way that you want, you can use a bit of Python knowledge to tailor it to be more specific to your desires.

Android-Scripting
Not only does Google run off Python, but Android also has its own value for the language. Whether you are someone who is just creating your own app for your phone or if you are someone who is looking to create the next app that gets downloaded multiple millions of times, you can use Python and Android-Scripting to create an app that does exactly what you want it to do.

YouTube
YouTube one just started as a video viewer on its own, but is now a billion-dollar company that is owned by Google. YouTube uses Python to let users view and upload video, share links, embed video and much more. Much like Google itself, YouTube relies heavily on Python to run seamlessly for the amount of traffic it gets daily.

Python is not your average coding language. Instead, it is a valuable and integral part of some of the biggest websites in the world, one of which is Google. And the resources listed here are just a fraction of what Google uses Python for in total.

Related:

What Are The 10 Most Famous Software Programs Written in Python?

The Future of Java and Python

Ranking Programming Languages: Which are Gaining Popularity?

Top 10 Software Skills for 2014 and Beyond

Working With Strings In Python

Working With Lists In Python

Conditional Programming In Python

In this tutorial I am going to give you a gentle introduction to network programming in Python. If you are new to programming or new to Python then that may seem like a daunting thought. But read on and you will be pleasantly surprised how easy it is.

Like most modern programming languages, Python was designed for networking from the very beginning, and thanks to that, a lot of the networking tasks you would want to accomplish with the language are made a whole lot easier.

Network communication is a large topic, but if it is something that interests you then read on because in this tutorial I will show you how to download a web page. I will show you how easy Python makes tasks like this.

Take a look at the following code:

import urllib
	
con = urllib.urlopen("http://hartmannsoftware.com")
page = con.read()
con.close()
print page