.NET Training Classes in Lansing, Michigan
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29 April, 2024 - 2 May, 2024 - Ruby Programming
29 April, 2024 - 1 May, 2024 - RED HAT ENTERPRISE LINUX V7 DIFFERENCES
13 May, 2024 - 15 May, 2024 - VMware vSphere 8.0 with ESXi and vCenter
10 June, 2024 - 14 June, 2024 - Ruby on Rails
2 May, 2024 - 3 May, 2024 - See our complete public course listing
Blog Entries publications that: entertain, make you think, offer insight
Once again theTIOBE Programming Community has calculated the trends in popular programming languages on the web. Evaluating the updates in the index allows developers to assess the direction of certain programming skills that are rising or faltering in their field. According to the November 2013 report, three out of four languages currently ranking in the top twenty are languages defined by Microsoft. These are C#, SQL Server language Transact-SQL and Visual Basic.NET. Not surprising though, the top two languages that remain steady in the number one and two spots are Java and C.
How are the calculations measured? The information is gathered from five major search engines: Google, Bing, Yahoo!, Wikipedia, Amazon, YouTube and Baidu.
Top 20 Programming Languages: as of November 2013
- C
- Java
- Objective-C
- C++
- C#
- PHP
- (Visual) Basic
- Python
- Transact-SQL
- Java Script
- Visual Basic.NET
- Perl
- Ruby
- Pascal
- Lisp
- MATLAB
- Delphi/Object Pascal
- PL/SQL
- COBOL
- Assembly
Although the index is an important itemized guide of what people are searching for on the internet, it’s arguable that certain languages getting recognition is a direct result of early adopters posting tutorials and filling up discussion boards on current trends. Additionally, popular tech blogs pick up on technological shifts and broadcast related versions of the same themes.
When does the popularity of a software language matter?
- If you want marketable skills, knowing what employers are looking for is beneficial. As an example, languages such as Java and Objective C are highly coveted in the smart-phone apps businesses.
- A consistently shrinking language in usage is an indicator not only that employers are apt to pass on those skills but fall in danger of being obsolete.
- Focusing on languages that are compatible with other developers increases your chances to participate on projects that companies are working on.
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
The original article was posted by Michael Veksler on Quora
A very well known fact is that code is written once, but it is read many times. This means that a good developer, in any language, writes understandable code. Writing understandable code is not always easy, and takes practice. The difficult part, is that you read what you have just written and it makes perfect sense to you, but a year later you curse the idiot who wrote that code, without realizing it was you.
The best way to learn how to write readable code, is to collaborate with others. Other people will spot badly written code, faster than the author. There are plenty of open source projects, which you can start working on and learn from more experienced programmers.
Readability is a tricky thing, and involves several aspects:
- Never surprise the reader of your code, even if it will be you a year from now. For example, don’t call a function max() when sometimes it returns the minimum().
- Be consistent, and use the same conventions throughout your code. Not only the same naming conventions, and the same indentation, but also the same semantics. If, for example, most of your functions return a negative value for failure and a positive for success, then avoid writing functions that return false on failure.
- Write short functions, so that they fit your screen. I hate strict rules, since there are always exceptions, but from my experience you can almost always write functions short enough to fit your screen. Throughout my carrier I had only a few cases when writing short function was either impossible, or resulted in much worse code.
- Use descriptive names, unless this is one of those standard names, such as i or it in a loop. Don’t make the name too long, on one hand, but don’t make it cryptic on the other.
- Define function names by what they do, not by what they are used for or how they are implemented. If you name functions by what they do, then code will be much more readable, and much more reusable.
- Avoid global state as much as you can. Global variables, and sometimes attributes in an object, are difficult to reason about. It is difficult to understand why such global state changes, when it does, and requires a lot of debugging.
- As Donald Knuth wrote in one of his papers: “Early optimization is the root of all evil”. Meaning, write for readability first, optimize later.
- The opposite of the previous rule: if you have an alternative which has similar readability, but lower complexity, use it. Also, if you have a polynomial alternative to your exponential algorithm (when N > 10), you should use that.
Use standard library whenever it makes your code shorter; don’t implement everything yourself. External libraries are more problematic, and are both good and bad. With external libraries, such as boost, you can save a lot of work. You should really learn boost, with the added benefit that the c++ standard gets more and more form boost. The negative with boost is that it changes over time, and code that works today may break tomorrow. Also, if you try to combine a third-party library, which uses a specific version of boost, it may break with your current version of boost. This does not happen often, but it may.
Don’t blindly use C++ standard library without understanding what it does - learn it. You look at
documentation at it tells you that its complexity is O(1), amortized. What does that mean? How does it work? What are benefits and what are the costs? Same with std::vector::push_back()
, and with std::map
. Knowing the difference between these two maps, you’d know when to use each one of them.std::unordered_map
Never call
or new
directly, use delete
and [cost c++]std::make_shared[/code] instead. Try to implement std::make_unique
yourself, in order to understand what they actually do. People do dumb things with these types, since they don’t understand what these pointers are.usique_ptr, shared_ptr, weak_ptr
Every time you look at a new class or function, in boost or in std, ask yourself “why is it done this way and not another?”. It will help you understand trade-offs in software development, and will help you use the right tool for your job. Don’t be afraid to peek into the source of boost and the std, and try to understand how it works. It will not be easy, at first, but you will learn a lot.
Know what complexity is, and how to calculate it. Avoid exponential and cubic complexity, unless you know your N is very low, and will always stay low.
Learn data-structures and algorithms, and know them. Many people think that it is simply a wasted time, since all data-structures are implemented in standard libraries, but this is not as simple as that. By understanding data-structures, you’d find it easier to pick the right library. Also, believe it or now, after 25 years since I learned data-structures, I still use this knowledge. Half a year ago I had to implemented a hash table, since I needed fast serialization capability which the available libraries did not provide. Now I am writing some sort of interval-btree, since using std::map, for the same purpose, turned up to be very very slow, and the performance bottleneck of my code.
Notice that you can’t just find interval-btree on Wikipedia, or stack-overflow. The closest thing you can find is Interval tree, but it has some performance drawbacks. So how can you implement an interval-btree, unless you know what a btree is and what an interval-tree is? I strongly suggest, again, that you learn and remember data-structures.
These are the most important things, which will make you a better programmer. The other things will follow.
The importance of variables in any programming language can’t be emphasised enough. Even if you are a novice, the chances are good that you will have been using variables for quite a while now.
They are the cornerstone of any language and without them we would not be able to accomplish much of anything. However, most of you up until this point have probably only been working with standard variables, variables which can hold single values such as an integer, a single character, or a string of text.
In this tutorial we are going to take a look at a more special type of variable called an array. Arrays can seem quite daunting at first glance but once you get used to working with them you will wonder how you ever managed to program without them.
The reason arrays are special is because they can hold more than one value. Think about this: say you create a variable which contains a line of text like the code below:
Tech Life in Michigan
Company Name | City | Industry | Secondary Industry |
---|---|---|---|
Lear Corporation | Southfield | Manufacturing | Automobiles, Boats and Motor Vehicles |
TRW Automotive Holdings Corp. | Livonia | Manufacturing | Automobiles, Boats and Motor Vehicles |
Spartan Stores, Inc. | Byron Center | Retail | Grocery and Specialty Food Stores |
Steelcase Inc. | Grand Rapids | Manufacturing | Furniture Manufacturing |
Valassis Communications, Inc. | Livonia | Business Services | Advertising, Marketing and PR |
Autoliv, Inc. | Auburn Hills | Manufacturing | Automobiles, Boats and Motor Vehicles |
Cooper-Standard Automotive Group | Novi | Manufacturing | Automobiles, Boats and Motor Vehicles |
Penske Automotive Group, Inc. | Bloomfield Hills | Retail | Automobile Dealers |
Con-Way Inc. | Ann Arbor | Transportation and Storage | Freight Hauling (Rail and Truck) |
Meritor, Inc. | Troy | Manufacturing | Automobiles, Boats and Motor Vehicles |
Visteon Corporation | Van Buren Twp | Manufacturing | Automobiles, Boats and Motor Vehicles |
Affinia Group, Inc. | Ann Arbor | Manufacturing | Automobiles, Boats and Motor Vehicles |
Perrigo Company | Allegan | Healthcare, Pharmaceuticals and Biotech | Pharmaceuticals |
BorgWarner Inc. | Auburn Hills | Manufacturing | Automobiles, Boats and Motor Vehicles |
Auto-Owners Insurance | Lansing | Financial Services | Insurance and Risk Management |
DTE Energy Company | Detroit | Energy and Utilities | Gas and Electric Utilities |
Whirlpool Corporation | Benton Harbor | Manufacturing | Tools, Hardware and Light Machinery |
Herman Miller, Inc. | Zeeland | Manufacturing | Furniture Manufacturing |
Universal Forest Products | Grand Rapids | Manufacturing | Furniture Manufacturing |
Masco Corporation Inc. | Taylor | Manufacturing | Concrete, Glass, and Building Materials |
PULTEGROUP, INC. | Bloomfield Hills | Real Estate and Construction | Real Estate & Construction Other |
CMS Energy Corporation | Jackson | Energy and Utilities | Energy and Utilities Other |
Stryker Corporation | Portage | Healthcare, Pharmaceuticals and Biotech | Medical Devices |
General Motors Company (GM) | Detroit | Manufacturing | Automobiles, Boats and Motor Vehicles |
Kellogg Company | Battle Creek | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
The Dow Chemical Company | Midland | Manufacturing | Chemicals and Petrochemicals |
Kelly Services, Inc. | Troy | Business Services | HR and Recruiting Services |
Ford Motor Company | Dearborn | Manufacturing | Automobiles, Boats and Motor Vehicles |
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 Michigan 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…