Web Services Training Classes in London, Canada

Learn Web Services in London, Canada and surrounding areas via our hands-on, expert led courses. All of our classes either are offered on an onsite, online or public instructor led basis. Here is a list of our current Web Services related training offerings in London, Canada: Web Services Training

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F# is excellent for specialties such as scientific computing and data analysis. It is an excellent choice for enterprise development as well. There are a few great reasons why you should consider using F# for your next project.

Concise

F# is not cluttered up with coding noise;  no pesky semicolons, curly brackets, and so on. You almost never have to specify the kind of object you're referencing because of its powerful type inference system. It usually takes fewer lines of code to solve the same issue.

Convenient

Common programming tasks are much easier in F#. These include generating and using state machines, comparison and equality, list processing, as well as complex type definitions. It is very easy to generate powerful and reusable code because functions are first class objects. This is done by creating functions that have other functions as parameters or that combine existing functions to generate a new functionality.

Correctness

F# has a strong type system, and, therefore, prevents many common errors such as null reference exceptions. Valuables are immutable by default which, too, prevents a huge class of errors. You can also encode business logic by utilizing the type system. When done correctly, it is impossible to mix up units of measure or to write incorrect code thereby decresing the need of unit tests.

Concurrency

F# has number of built-in libraries. These libraries help when more than one thing at a time is occurring. Parallelism and asynchronous programming are very simple. There is also a built-in actor model as well as excellent support for event handling and functional reactive programming. Sharing state and avoiding locks are much easier because data structures are immutable by default.

Completeness

F# also supports other styles that are not 100 percent pure. This makes it easier to interact with the non-pure world of databases, websites, other applications, and so on. It is actually designed as a hybrid functional/OO language. F# is also part of the .NET ecosystem. This gives you seamless access to all the third party .NET tools and libraries. It operates on most platforms. These platforms include Linux and smartphones via mono. Visual Studio is integrates with F# as well. This means you get many plug-ins for unit tests, a debugger, a IDE with IntelliSense support, other development tasks. You can use MonoDevelop IDE on Linux.

Related:

F# - Marching Towards Top 10 Programming Languages

What Are the Advantages of Python Over Ruby?

Top 10 Programming Languages Expected To Be In Demand in 2014

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.

We’re often asked by companies about how they can get the most value from Agile/Scrum practices. More specifically, they want to know if they are being as effective as best they possibly can be by using the Scrum framework for their explicit needs.

The other objective for individuals is determining if it necessary to be certified in order to be effective in the Agile Scrum world?   In short, a good Scrum Master must understand four things: the business they work in, the technology they work with, the Agile and Scrum principles, and, most importantly, people!  Based on these facts, Scrum Master Certification is not enough – real life experience and a bit of soft skills should be part and parcel of their training. For organizations, the main goal is to understand industry best practices when adopting and applying agile principles, to build strong teams, understand and distill business needs into software requirements.

In terms of getting a good grip on training for Agile/Scrum, one can opt to pursue a certification in Scrum (CSM) Certified Scrum Master for personal reasons or for a job requirement. Or, one can simply opt to learn the benefits and pitfalls of the methodology and decide the best approach for them.

There are different ways to get started with Agile training. Below are two of the most common paths to Agile our students take.

data dictionary workThe 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.data dictionary

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.

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A successful career as a software developer or other IT professional requires a solid understanding of software development processes, design patterns, enterprise application architectures, web services, security, networking and much more. The progression from novice to expert can be a daunting endeavor; this is especially true when traversing the learning curve without expert guidance. A common experience is that too much time and money is wasted on a career plan or application due to misinformation.

The Hartmann Software Group understands these issues and addresses them and others during any training engagement. Although no IT educational institution can guarantee career or application development success, HSG can get you closer to your goals at a far faster rate than self paced learning and, arguably, than the competition. Here are the reasons why we are so successful at teaching:

  • Learn from the experts.
    1. We have provided software development and other IT related training to many major corporations in Canada since 2002.
    2. Our educators have years of consulting and training experience; moreover, we require each trainer to have cross-discipline expertise i.e. be Java and .NET experts so that you get a broad understanding of how industry wide experts work and think.
  • Discover tips and tricks about Web Services programming
  • Get your questions answered by easy to follow, organized Web Services experts
  • Get up to speed with vital Web Services programming tools
  • Save on travel expenses by learning right from your desk or home office. Enroll in an online instructor led class. Nearly all of our classes are offered in this way.
  • Prepare to hit the ground running for a new job or a new position
  • See the big picture and have the instructor fill in the gaps
  • We teach with sophisticated learning tools and provide excellent supporting course material
  • Books and course material are provided in advance
  • Get a book of your choice from the HSG Store as a gift from us when you register for a class
  • Gain a lot of practical skills in a short amount of time
  • We teach what we know…software
  • We care…
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Interesting Reads Take a class with us and receive a book of your choosing for 50% off MSRP.