Linux Unix Training Classes in Eau Claire, Wisconsin

Learn Linux Unix in Eau Claire, Wisconsin 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 Linux Unix related training offerings in Eau Claire, Wisconsin: Linux Unix Training

We offer private customized training for groups of 3 or more attendees.
Eau-Claire  Upcoming Instructor Led Online and Public Linux Unix Training Classes
Enterprise Linux System Administration Training/Class 10 June, 2024 - 14 June, 2024 $2190
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration
Linux Fundaments GL120 Training/Class 15 April, 2024 - 19 April, 2024 $2090
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration
LINUX SHELL SCRIPTING Training/Class 29 May, 2024 - 30 May, 2024 $990
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration
OpenShift Fundamentals Training/Class 22 April, 2024 - 24 April, 2024 $2090
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration
RED HAT ENTERPRISE LINUX AUTOMATION WITH ANSIBLE Training/Class 3 June, 2024 - 6 June, 2024 $2735
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration
RED HAT ENTERPRISE LINUX V7 DIFFERENCES Training/Class 13 May, 2024 - 15 May, 2024 $1690
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration
RHCSA EXAM PREP Training/Class 17 June, 2024 - 21 June, 2024 $2090
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration
Docker Training/Class 29 April, 2024 - 1 May, 2024 $1690
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration
DOCKER WITH KUBERNETES ADMINISTRATION Training/Class 6 May, 2024 - 10 May, 2024 $2490
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration
RED HAT SATELLITE V6 (FOREMAN/KATELLO) ADMINISTRATION Training/Class 1 April, 2024 - 4 April, 2024 $2590
HSG Training Center instructor led online
Eau-Claire, Wisconsin 54701
Hartmann Software Group Training Registration

View all Scheduled Linux Unix Training Classes

Linux Unix Training Catalog

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DevOps Classes

cost: $ 1690length: 3 day(s)
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Foundations of Web Design & Web Authoring Classes

cost: $ 790length: 2 day(s)
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Course Directory [training on all levels]

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Blog Entries publications that: entertain, make you think, offer insight

Recently, the new iOS update had added Reminders to the iPhone. If you ever found yourself setting notes on your iPhone to remember to do things, such as buying milk while at the grocery store, this process has become leagues upon leagues simpler, and faster. On your iPhone is an application named “Reminders”. Tap on this application and experience the new world of To-Do lists.

 

Right away, you are greeted by a screen that looks similar to a notepad, where you would be scribbling down reminders for this, and for that. To start off, tap on the plus button, and you are able to input the reminder you want. Say you want to be reminded to “Buy Milk.” Just type that into the application and you’re good to go.

But wait, there’s more. What this new application brings to the table that is extremely useful is the fact that your iPhone can remind you to do that task at a certain location, which, in this case, is buying milk. If you had saved your regular grocery store in your Maps application as a favorite location, you are able to do so. (To save a favorite location, go into your Maps application, search for your nearest grocery store that you regularly shop at, tap on the pin, tap on the blue arrow to get more information, and “Add to Bookmarks.”) In order to remind you to buy milk at your favorite grocery store, slide the “Off” to “On” and you are now able to set where you would like to be reminded at, and at what point in time. Now, you will never leave the grocery store without buying milk!

Here is a list of the organizations that use Python. This list is periodically updated by HSG’s software fans as well as the community at large.
 

Web Development

1.       Yahoo Maps
Yahoo acquired Four11, whose address and mapping lookup services were implemented in Python. Yahoo Maps still uses Python today, as can be seen by examining its URLs.
 

2.       Yahoo Groups
A comprehensive public archive of Internet mailing lists that was originally implemented in pure Python. At one point Scott Hassan, one of the founders of Findmail/eGroups (the company that was later acquired by Yahoo), reported that they had 180,000 lines of Python underlying everything from their 100% dynamic website to all email delivery, pumping out 200 messages/second on a single 400 MHz Pentium.

 

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.

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

Tech Life in Wisconsin

Fun Facts and stats: ? Wisconsin?s nickname is the Badger State. ? In 1882 the first hydroelectric plant in the United States was built at Fox River. ? The first practical typewriter was designed in Milwaukee in 1867. ? The nation's first kindergarten was established in Watertown in 1856. Its first students were local German-speaking youngsters. ? The Republican Party was founded in Ripon in 1854.
The purpose of learning is growth, and our minds, unlike our bodies, can continue growing as we continue to live.  ~Mortimer Adler
other Learning Options
Software developers near Eau Claire have ample opportunities to meet like minded techie individuals, collaborate and expend their career choices by participating in Meet-Up Groups. The following is a list of Technology Groups in the area.
Fortune 500 and 1000 companies in Wisconsin that offer opportunities for Linux Unix developers
Company Name City Industry Secondary Industry
We Energies Milwaukee Energy and Utilities Gas and Electric Utilities
Bemis Company, Inc. Neenah Manufacturing Plastics and Rubber Manufacturing
Regal Beloit Corporation Beloit Manufacturing Tools, Hardware and Light Machinery
Manitowoc Company, Inc Manitowoc Manufacturing Heavy Machinery
Briggs and Stratton Corporation Milwaukee Manufacturing Tools, Hardware and Light Machinery
Mortgage Guaranty Insurance Corporation (MGIC) Milwaukee Financial Services Lending and Mortgage
A.O. Smith Corporation Milwaukee Manufacturing Tools, Hardware and Light Machinery
Sentry Insurance Stevens Point Financial Services Insurance and Risk Management
Rockwell Automation, Inc. Milwaukee Manufacturing Tools, Hardware and Light Machinery
Bucyrus International, Inc. South Milwaukee Manufacturing Heavy Machinery
Diversey, Inc. Sturtevant Manufacturing Chemicals and Petrochemicals
Alliant Energy Corporation Madison Energy and Utilities Gas and Electric Utilities
Plexus Corp. Neenah Manufacturing Manufacturing Other
Spectrum Brands Holdings, Inc. Madison Manufacturing Tools, Hardware and Light Machinery
Kohl's Corporation Menomonee Falls Retail Department Stores
Snap-on Tools, Inc. Kenosha Manufacturing Tools, Hardware and Light Machinery
Fiserv, Inc. Brookfield Software and Internet Data Analytics, Management and Storage
CUNA Mutual Group Madison Financial Services Insurance and Risk Management
Oshkosh Corporation Oshkosh Manufacturing Heavy Machinery
Modine Manufacturing Company Racine Manufacturing Manufacturing Other
Northwestern Mutual Life Insurance Company Milwaukee Financial Services Insurance and Risk Management
Joy Global Inc. Milwaukee Manufacturing Heavy Machinery
Harley-Davidson, Inc. Milwaukee Manufacturing Automobiles, Boats and Motor Vehicles
American Family Insurance Madison Financial Services Insurance and Risk Management
Johnson Controls, Inc. Milwaukee Manufacturing Heavy Machinery
ManpowerGroup Milwaukee Business Services HR and Recruiting Services

training details locations, tags and why hsg

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 Wisconsin 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 Linux Unix programming
  • Get your questions answered by easy to follow, organized Linux Unix experts
  • Get up to speed with vital Linux Unix 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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