Project Management Training in Minneapolis, Minnesota
Learn Project Management in Minneapolis, Minnesota 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 Project Management related training offerings in Minneapolis, Minnesota: Project Management Training
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7 July, 2025 - 8 July, 2025 - RED HAT ENTERPRISE LINUX SYSTEMS ADMIN I
19 May, 2025 - 23 May, 2025 - RED HAT ENTERPRISE LINUX SYSTEMS ADMIN II
18 August, 2025 - 21 August, 2025 - Fast Track to Java 17 and OO Development
18 August, 2025 - 22 August, 2025 - OpenShift Fundamentals
9 June, 2025 - 11 June, 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
Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.
A Matter of Personality...
That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.
Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.
For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:
a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]
first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:
a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}
but there are a number of obvious alternatives, such as:
a = (10..19).collect do |x|
x ** 3
end
b = a.find_all do |y|
y % 2 == 0
end
It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.
And Somewhat One of Performance
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.
It is hard not to wonder how current technology would have altered the events surrounding the tragic death of John F. Kennedy. On the afternoon of November 22, 1963, shots rang out in Dallas, TX, taking the life of JFK, one of the most beloved Americans. Given the same circumstances today, surely the advances in IT alone, would have drastically changed the outcome of that horrible day. Would the government have recognized that there was a viable threat looming over JFK? Would local and government agencies have been more prepared for a possible assassination attempt? Would the assortment of everyday communication devices assisted in the prevention of the assassination, not to mention, provided greater resources into the investigation? With all that the IT world has to offer today, how would it have altered the JFK tragedy?
As many conspiracy theories have rocked the foundation of the official story presented by government agencies, realization of the expansive nature of technology provides equal consideration as to how the event would have been changed had this technology been available during the time of the shooting. There were T.V. cameras, home 8mm recorders, even single shot-hand held cameras snapping away as the car caravan approached. Yet, there remains little documentation of the shooting and even less information pertaining to the precautions taken by officials prior to JFK's arrival. Theorists consider these possibilities along with how the world would have turned out had the great John F. Kennedynever been assassinated on that day.
Tech Life in Minnesota
Company Name | City | Industry | Secondary Industry |
---|---|---|---|
The Affluent Traveler | Saint Paul | Travel, Recreation and Leisure | Travel, Recreation, and Leisure Other |
Xcel Energy Inc. | Minneapolis | Energy and Utilities | Gas and Electric Utilities |
Thrivent Financial for Lutherans | Minneapolis | Financial Services | Personal Financial Planning and Private Banking |
CHS Inc. | Inver Grove Heights | Agriculture and Mining | Agriculture and Mining Other |
Hormel Foods Corporation | Austin | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
St. Jude Medical, Inc. | Saint Paul | Healthcare, Pharmaceuticals and Biotech | Medical Devices |
The Mosaic Company | Minneapolis | Agriculture and Mining | Mining and Quarrying |
Ecolab Inc. | Saint Paul | Manufacturing | Chemicals and Petrochemicals |
Donaldson Company, Inc. | Minneapolis | Manufacturing | Tools, Hardware and Light Machinery |
Michael Foods, Inc. | Minnetonka | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
Regis Corporation | Minneapolis | Retail | Retail Other |
Fastenal Company | Winona | Wholesale and Distribution | Wholesale and Distribution Other |
Securian Financial | Saint Paul | Financial Services | Insurance and Risk Management |
UnitedHealth Group | Minnetonka | Financial Services | Insurance and Risk Management |
The Travelers Companies, Inc. | Saint Paul | Financial Services | Insurance and Risk Management |
Imation Corp. | Saint Paul | Computers and Electronics | Networking Equipment and Systems |
C.H. Robinson Worldwide, Inc. | Eden Prairie | Transportation and Storage | Warehousing and Storage |
Ameriprise Financial, Inc. | Minneapolis | Financial Services | Securities Agents and Brokers |
Best Buy Co. Inc. | Minneapolis | Retail | Retail Other |
Nash Finch Company | Minneapolis | Wholesale and Distribution | Grocery and Food Wholesalers |
Medtronic, Inc. | Minneapolis | Healthcare, Pharmaceuticals and Biotech | Medical Devices |
LAND O'LAKES, INC. | Saint Paul | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
General Mills, Inc. | Minneapolis | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
Pentair, Inc. | Minneapolis | Manufacturing | Manufacturing Other |
Supervalu Inc. | Eden Prairie | Retail | Grocery and Specialty Food Stores |
U.S. Bancorp | Minneapolis | Financial Services | Banks |
Target Corporation, Inc. | Minneapolis | Retail | Department Stores |
3M Company | Saint Paul | Manufacturing | Chemicals and Petrochemicals |
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 Minnesota 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 Project Management programming
- Get your questions answered by easy to follow, organized Project Management experts
- Get up to speed with vital Project Management 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…