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!

Java still has its place in the world of software development, but is it quickly becoming obsolete by the more dynamically enabled Python programming language? The issue is hotly contested by both sides of the debate. Java experts point out that Java is still being developed with more programmer friendly updates. Python users swear that Java can take up to ten times longer to develop. Managers that need to make the best decision for a company need concrete information so that an informed and rational decision can be made.

First, Java is a static typed language while Python is dynamically typed. Static typed languages require that each variable name must be tied to both a type and an object. Dynamically typed languages only require that a variable name only gets bound to an object. Immediately, this puts Python ahead of the game in terms of productivity since a static typed language requires several elements and can make errors in coding more likely.

Python uses a concise language while Java uses verbose language. Concise language, as the name suggests, gets straight to the point without extra words. Removing additional syntax can greatly reduce the amount of time required to program.  A simple call in Java, such as the ever notorious "Hello, World" requires three several lines of coding while Python requires a single sentence. Java requires the use of checked exceptions. If the exceptions are not caught or thrown out then the code fails to compile. In terms of language, Python certainly has surpassed Java in terms of brevity.

Additionally, while Java's string handling capabilities have improved they haven't yet matched the sophistication of Python's. Web applications rely upon fast load times and extraneous code can increase user wait time. Python optimizes code in ways that Java doesn't, and this can make Python a more efficient language. However, Java does run faster than Python and this can be a significant advantage for programmers using Java. When you factor in the need for a compiler for Java applications the speed factor cancels itself out leaving Python and Java at an impasse.

While a programmer will continue to argue for the language that makes it easiest based on the programmer's current level of knowledge, new software compiled with Python takes less time and provides a simplified coding language that reduces the chance for errors. When things go right, Java works well and there are no problems. However, when errors get introduced into the code, it can become extremely time consuming to locate and correct those errors. Python generally uses less code to begin with and makes it easier and more efficient to work with.

Ultimately, both languages have their own strengths and weaknesses. For creating simple applications, Python provides a simpler and more effective application. Larger applications can benefit from Java and the verbosity of the code actually makes it more compatible with future versions. Python code has been known to break with new releases. Ultimately, Python works best as a type of connecting language to conduct quick and dirty work that would be too intensive when using Java alone. In this sense, Java is a low-level implementation language. While both languages are continuing to develop, it's unlikely that one language will surpass the other for all programming needs in the near future.

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.

Information Technology (IT) tools are here to support your business in the global market. Effective communication is key for IT and business experts to collaborate effectively in search of solutions. Consulting, reaching out for help to a third-party, can bridge the gap between your business marketing experts and IT operations experts, especially with the emergence of big data analytics and its implication on the global market. Having the right consultants equipped with business knowledge and data technology expertise can make a difference.

Your marketing organization is probably familiar with digital tools and conducting global research. Its results can uncover the journey customers take to purchase your products or use your services. It can highlight the pain points and frictions that prevent their experiences with you to be delightful and amazing. Armed with this knowledge and beautiful compelling presentations, marketing executives expect that IT operations leaders will translate these insights into actions.

But people in IT operations are too involved in meeting key performance indicators that have nothing to do with the end customers. Meeting requirements of faster and cheaper don't translate very well into customer satisfaction. A classic breakdown in communication is described in a Harvard Business Review article, “A Technique to Bridge the Gap Between Marketing and IT.” The author goes on to describe how a new CIO at a bank found IT to be focused on the internal organization as their customers, rather than the real end customer. Moreover, no one was looking at the incident reports which clearly showed that incidents were increasing. And nobody looked at what these incidents were doing to the bank’s customers. The startling and scary numbers of incidents were caught and addressed and brought down from 1,000 to 600 or (40%) and later to 450 per week.

Surprisingly, these type of seemingly isolated scenarios are still being discovered within organizations presently, sometimes internally, and through third party insights such as consultants.  By engaging consultants to provide a perspective based on what they’ve experienced before, they can often bring new and innovative ideas or possible challenges to the table that an internal processes probably wouldn’t have been able to see on their own.  Often, third party input can help to provide the translation needed to go from marketing research results into actions that IT operations can understand and make sense in their high-performance culture. When companies understand and use this knowledge to reassess how to improve their customer experiences, they work backward from what customers want to achieve significantly higher improvements. 

IT and business management are more and more being asked to move away from their traditional roles, such as IT being the "technology infrastructure gatekeeper", and instead become enablers across the enterprise of effective collaboration, big data consumers, and key players in driving desired business outcomes. Marketing leaders look to technology as a way to facilitate the customer's journey and his positive experience of it, bring more clients, and meet increasingly higher loyalty goals. They rely on IT projects to enable big data-based behavioral targeting anywhere in the global market. This means projects to analyze search engine results, improve website personalization and optimization, and building of mobile applications for a more personal experience. All these are projects that consultants with their communication, consulting and technical expertise are well prepared to help in order to bridge the expectation gap between IT and other business organizations.

In order to meet these 21st-century business challenges, Information Technology organizations have been transitioning from waterfall stage-gate project management approaches to agile development. The stage-gate method applies a step-by-step approach where waiting, reviewing and approving are required before moving to the next step in the project. Agile management emphasizes collaboration, no decision hierarchies, and few people roles for making quick, customer-focused small changes over time to deliver solutions that delight and amaze customers. Agile development has allowed many businesses to respond quickly to changing customer desires and expectations. But moving to continuous delivery is a struggle requiring focused, dedicated teams that are not well suited to the traditional matrix organization where people are resources whose time must be "chopped" into many pieces and shared among many projects. Agile teams meet frequently as often as daily but never waiting more than a week to do so.

Marketing people are externally focused. IT people are internally focused. The first works with customer emotions. The second works to increase efficiency. Big data analytic tools are used by the first and supported by the second. Consultants can be the glue that helps both come together in effective collaborations that deliver positive business outcomes in both global and local markets.