.NET Training Classes in Schenectady, New York
Learn .NET in Schenectady, NewYork 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 .NET related training offerings in Schenectady, New York: .NET Training
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- RED HAT ENTERPRISE LINUX SYSTEMS ADMIN II
8 December, 2025 - 11 December, 2025 - Linux Fundaments GL120
22 September, 2025 - 26 September, 2025 - Fast Track to Java 17 and OO Development
8 December, 2025 - 12 December, 2025 - Python for Scientists
8 December, 2025 - 12 December, 2025 - OpenShift Fundamentals
6 October, 2025 - 8 October, 2025 - See our complete public course listing
Blog Entries publications that: entertain, make you think, offer insight
A business rule is the basic unit of rule processing in a Business Rule Management System (BRMS) and, as such, requires a fundamental understanding. Rules consist of a set of actions and a set of conditions whereby actions are the consequences of each condition statement being satisfied or true. With rare exception, conditions test the property values of objects taken from an object model which itself is gleaned from a Data Dictionary and UML diagrams. See my article on Data Dictionaries for a better understanding on this subject matter.
A simple rule takes the form:
if condition(s)
then actions.
An alternative form includes an else statement where alternate actions are executed in the event that the conditions in the if statement are not satisfied:
if condition(s)
then actions
else alternate_actions
It is not considered a best prectice to write rules via nested if-then-else statements as they tend to be difficult to understand, hard to maintain and even harder to extend as the depth of these statements increases; in other words, adding if statements within a then clause makes it especially hard to determine which if statement was executed when looking at a bucket of rules. Moreoever, how can we determine whether the if or the else statement was satisfied without having to read the rule itself. Rules such as these are often organized into simple rule statements and provided with a name so that when reviewing rule execution logs one can determine which rule fired and not worry about whether the if or else statement was satisfied. Another limitation of this type of rule processing is that it does not take full advantage of rule inferencing and may have a negative performance impact on the Rete engine execution. Take a class with HSG and find out why.
Rule Conditions
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.”
When you think about the black market, I’m sure the majority of you will think of prohibition days. When alcohol was made illegal, it did two things: It made the bad guys more money, and it put the average joe in a dangerous position while trying to acquire it. Bring in the 21stcentury. Sure, there still is a black market… but come on, who is afraid of mobsters anymore? Today, we have a gaming black market. It has been around for years, but will it survive? With more and more games moving towards auction houses, could game companies “tame” the gaming black market?
In the old days of gaming on the internet, we spent most of our online time playing hearts, spades… whatever we could do while connected to the internet. As the years went by, better and better games came about. Then, suddenly, interactive multiplayer games came into the picture. These interactive games, mainly MMORPGS, allowed for characters to pick up and keep randomly generated objects known as “loot”. This evolution of gaming created the black market.
In the eyes of the software companies, the game is only being leased/rented by the end user. You don’t actually have any rights to the game. This is where the market becomes black. The software companies don’t want you making money of “virtual” goods that are housed on the software or servers of the game you are playing on. The software companies, at this point, started to get smarter.
Where there is a demand…
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.
Tech Life in New York
Company Name | City | Industry | Secondary Industry |
---|---|---|---|
NYSE Euronext, Inc. | New York | Financial Services | Securities Agents and Brokers |
Anderson Instrument Company Inc. | Fultonville | Manufacturing | Tools, Hardware and Light Machinery |
News Corporation | New York | Media and Entertainment | Radio and Television Broadcasting |
Philip Morris International Inc | New York | Manufacturing | Manufacturing Other |
Loews Corporation | New York | Travel, Recreation and Leisure | Hotels, Motels and Lodging |
The Guardian Life Insurance Company of America | New York | Financial Services | Insurance and Risk Management |
Jarden Corporation | Rye | Manufacturing | Manufacturing Other |
Ralph Lauren Corporation | New York | Retail | Clothing and Shoes Stores |
Icahn Enterprises, LP | New York | Financial Services | Investment Banking and Venture Capital |
Viacom Inc. | New York | Media and Entertainment | Media and Entertainment Other |
Omnicom Group Inc. | New York | Business Services | Advertising, Marketing and PR |
Henry Schein, Inc. | Melville | Healthcare, Pharmaceuticals and Biotech | Medical Supplies and Equipment |
Pfizer Incorporated | New York | Healthcare, Pharmaceuticals and Biotech | Pharmaceuticals |
Eastman Kodak Company | Rochester | Computers and Electronics | Audio, Video and Photography |
Assurant Inc. | New York | Business Services | Data and Records Management |
PepsiCo, Inc. | Purchase | Manufacturing | Nonalcoholic Beverages |
Foot Locker, Inc. | New York | Retail | Department Stores |
Barnes and Noble, Inc. | New York | Retail | Sporting Goods, Hobby, Book, and Music Stores |
Alcoa | New York | Manufacturing | Metals Manufacturing |
The Estee Lauder Companies Inc. | New York | Healthcare, Pharmaceuticals and Biotech | Personal Health Care Products |
Avon Products, Inc. | New York | Healthcare, Pharmaceuticals and Biotech | Personal Health Care Products |
The Bank of New York Mellon Corporation | New York | Financial Services | Banks |
Marsh and McLennan Companies | New York | Financial Services | Insurance and Risk Management |
Corning Incorporated | Corning | Manufacturing | Concrete, Glass, and Building Materials |
CBS Corporation | New York | Media and Entertainment | Radio and Television Broadcasting |
Bristol Myers Squibb Company | New York | Healthcare, Pharmaceuticals and Biotech | Biotechnology |
Citigroup Incorporated | New York | Financial Services | Banks |
Goldman Sachs | New York | Financial Services | Personal Financial Planning and Private Banking |
American International Group (AIG) | New York | Financial Services | Insurance and Risk Management |
Interpublic Group of Companies, Inc. | New York | Business Services | Advertising, Marketing and PR |
BlackRock, Inc. | New York | Financial Services | Securities Agents and Brokers |
MetLife Inc. | New York | Financial Services | Insurance and Risk Management |
Consolidated Edison Company Of New York, Inc. | New York | Energy and Utilities | Gas and Electric Utilities |
Time Warner Cable | New York | Telecommunications | Cable Television Providers |
Morgan Stanley | New York | Financial Services | Investment Banking and Venture Capital |
American Express Company | New York | Financial Services | Credit Cards and Related Services |
International Business Machines Corporation | Armonk | Computers and Electronics | Computers, Parts and Repair |
TIAA-CREF | New York | Financial Services | Securities Agents and Brokers |
JPMorgan Chase and Co. | New York | Financial Services | Investment Banking and Venture Capital |
The McGraw-Hill Companies, Inc. | New York | Media and Entertainment | Newspapers, Books and Periodicals |
L-3 Communications Inc. | New York | Manufacturing | Aerospace and Defense |
Colgate-Palmolive Company | New York | Consumer Services | Personal Care |
New York Life Insurance Company | New York | Financial Services | Insurance and Risk Management |
Time Warner Inc. | New York | Media and Entertainment | Media and Entertainment Other |
Cablevision Systems Corp. | Bethpage | Media and Entertainment | Radio and Television Broadcasting |
CA Technologies, Inc. | Islandia | Software and Internet | Software |
Verizon Communications Inc. | New York | Telecommunications | Telephone Service Providers and Carriers |
Hess Corporation | New York | Energy and Utilities | Gasoline and Oil Refineries |
training details locations, tags and why hsg
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.
- We have provided software development and other IT related training to many major corporations in New York since 2002.
- 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 .NET programming
- Get your questions answered by easy to follow, organized .NET experts
- Get up to speed with vital .NET 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…