C++ Training in Dayton, Ohio
Learn C++ in Dayton, Ohio 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 C++ related training offerings in Dayton, Ohio: C++ Training
C++ Training Catalog
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29 May, 2024 - 30 May, 2024 - DOCKER WITH KUBERNETES ADMINISTRATION
6 May, 2024 - 10 May, 2024 - Linux Fundaments GL120
15 July, 2024 - 19 July, 2024 - Introduction to Spring 5 (2022)
15 July, 2024 - 17 July, 2024 - OpenShift Fundamentals
24 June, 2024 - 26 June, 2024 - See our complete public course listing
Blog Entries publications that: entertain, make you think, offer insight
Disruptive technologies such as hand-held devices, cloud computing and social media are rattling the foundations upon which traditional businesses are built. Enterprise customers have grown smarter at ensuring the latest technological trends work in their favor. Everyone is trying to zero in on their core competencies by employing commodity services to run their business.
Likewise, enterprise application vendors need to zero in on their core competencies and enhance more value to the businesses of their clientele by leveraging standards-based commodity services, such as IaaS and PaaS, provided by leaders in those segments (e.g. Amazon EC2, Google Cloud Platform etc.).
What else enterprises need to do is learn to adopt new and emerging technologies such as cloud, utility and social computing to build on them to penetrate new market avenues.
New small and medium-sized entrants into the market are constantly challenging enterprises given their ability to rapidly turnaround and address the requirements of the customers in a cost-effective manner. Additionally, these new advancements also affect how enterprises create, deploy, and manage solutions and applications. If you take the example of Force.com, for instance, you find that it’s a common war zone for enterprise application vendors to furnish SME markets with their applications, with the new entrants mostly having an edge.
Writing Python in Java syntax is possible with a semi-automatic tool. Programming code translation tools pick up about 75% of dynamically typed language. Conversion of Python to a statically typed language like Java requires some manual translation. The modern Java IDE can be used to infer local variable type definitions for each class attribute and local variable.
Translation of Syntax
Both Python and Java are OO imperative languages with sizable syntax constructs. Python is larger, and more competent for functional programming concepts. Using the source translator tool, parsing of the original Python source language will allow for construction of an Abstract Source Tree (AST), followed by conversion of the AST to Java.
Python will parse itself. This capability is exhibited in the ast module, which includes skeleton classes. The latter can be expanded to parse and source each node of an AST. Extension of the ast.NodeVisitor class enables python syntax constructs to be customized using translate.py and parser.py coding structure.
The Concrete Syntax Tree (CST) for Java is based on visit to the AST. Java string templates can be output at AST nodes with visitor.py code. Comment blocks are not retained by the Python ast Parser. Conversion of Python to multi-line string constructs with the translator reduces time to script.
Scripting Python Type Inference in Java
Programmers using Python source know that the language does not contain type information. The fact that Python is a dynamic type language means object type is determined at run time. Python is also not enforced at compile time, as the source is not specified. Runtime type information of an object can be determined by inspecting the __class__.__name__ attribute.
Python’s inspect module is used for constructing profilers and debugging.
Implementation of def traceit (frame, event, arg) method in Python, and connecting it to the interpreter with sys.settrace (traceit) allows for integration of multiple events during application runtime.
Method call events prompt inspect and indexing of runtime type. Inspection of all method arguments can be conducted. By running the application profiler and exercising the code, captured trace files for each source file can be modified with the translator. Generating method syntax can be done with the translator by search and addition of type information. Results in set or returned variables disseminate the dynamic code in static taxonomy.
The final step in the Python to Java scrip integration is to administer unsupported concepts such as value object creation. There is also the task of porting library client code, for reproduction in Java equivalents. Java API stubs can be created to account for Python APIs. Once converted to Java the final clean-up of the script is far easier.
Related:
What Are The 10 Most Famous Software Programs Written in Python?
I will begin our blog on Java Tutorial with an incredibly important aspect of java development: memory management. The importance of this topic should not be minimized as an application's performance and footprint size are at stake.
From the outset, the Java Virtual Machine (JVM) manages memory via a mechanism known as Garbage Collection (GC). The Garbage collector
- Manages the heap memory. All obects are stored on the heap; therefore, all objects are managed. The keyword, new, allocates the requisite memory to instantiate an object and places the newly allocated memory on the heap. This object is marked as live until it is no longer being reference.
- Deallocates or reclaims those objects that are no longer being referened.
- Traditionally, employs a Mark and Sweep algorithm. In the mark phase, the collector identifies which objects are still alive. The sweep phase identifies objects that are no longer alive.
- Deallocates the memory of objects that are not marked as live.
- Is automatically run by the JVM and not explicitely called by the Java developer. Unlike languages such as C++, the Java developer has no explict control over memory management.
- Does not manage the stack. Local primitive types and local object references are not managed by the GC.
So if the Java developer has no control over memory management, why even worry about the GC? It turns out that memory management is an integral part of an application's performance, all things being equal. The more memory that is required for the application to run, the greater the likelihood that computational efficiency suffers. To that end, the developer has to take into account the amount of memory being allocated when writing code. This translates into the amount of heap memory being consumed.
Memory is split into two types: stack and heap. Stack memory is memory set aside for a thread of execution e.g. a function. When a function is called, a block of memory is reserved for those variables local to the function, provided that they are either a type of Java primitive or an object reference. Upon runtime completion of the function call, the reserved memory block is now available for the next thread of execution. Heap memory, on the otherhand, is dynamically allocated. That is, there is no set pattern for allocating or deallocating this memory. Therefore, keeping track or managing this type of memory is a complicated process. In Java, such memory is allocated when instantiating an object:
String s = new String(); // new operator being employed String m = "A String"; /* object instantiated by the JVM and then being set to a value. The JVM calls the new operator */
The interpreted programming language Python has surged in popularity in recent years. Long beloved by system administrators and others who had good use for the way it made routine tasks easy to automate, it has gained traction in other sectors as well. In particular, it has become one of the most-used tools in the discipline of numerical computing and analysis. Being put to use for such heavy lifting has endowed the language with a great selection of powerful libraries and other tools that make it even more flexible. One upshot of this development has been that sophisticated business analysts have also come to see the language as a valuable tool for those own data analysis needs.
Greatly appreciated for its simplicity and elegance of syntax, Python makes an excellent first programming language for previously non-technical people. Many business analysts, in fact, have had success growing their skill sets in this way thanks to the language's tractability. Long beloved by specialized data scientists, the iPython interactive computing environment has also attracted great attention within the business analyst’s community. Its instant feedback and visualization options have made it easy for many analysts to become skilled Python programmers while doing valuable work along the way.
Using iPython and appropriate notebooks for it, for example, business analysts can easily make interactive use of such tools as cohort analysis and pivot tables. iPython makes it easy to benefit from real-time, interactive researches which produce immediately visible results, including charts and graphs suitable for use in other contexts. Through becoming familiar with this powerful interactive application, business analysts are also exposing themselves in a natural and productive way to the Python programming language itself.
Gaining proficiency with this language opens up further possibilities. While interactive analytic techniques are of great use to many business analysts, being able to create fully functioning, independent programs is of similar value. Becoming comfortable with Python allows analysts to tackle and plumb even larger data sets than would be possible through an interactive approach, as results can be allowed to accumulate over hours and days of processing time.
This ability can sometime allow business analysts to address the so-called "Big Data" questions that can otherwise seem the sole province of specialized data scientists. More important than this higher level of independence, perhaps, is the fact that this increased facility with data analysis and handling allows analysts to communicate more effectively with such stakeholders. Through learning a programming language which allows them to begin making independent inroads into such areas, business analysts gain a better perspective on these specialized domains, and this allows them to function as even more effective intermediaries.
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Tech Life in Ohio
Company Name | City | Industry | Secondary Industry |
---|---|---|---|
Nationwide Insurance Company | Columbus | Financial Services | Insurance and Risk Management |
Owens Corning | Toledo | Manufacturing | Concrete, Glass, and Building Materials |
FirstEnergy Corp | Akron | Energy and Utilities | Gas and Electric Utilities |
The Lubrizol Corporation | Wickliffe | Manufacturing | Chemicals and Petrochemicals |
Sherwin-Williams | Cleveland | Retail | Hardware and Building Material Dealers |
Key Bank | Cleveland | Financial Services | Banks |
TravelCenters of America, Inc. | Westlake | Retail | Gasoline Stations |
Dana Holding Company | Maumee | Manufacturing | Automobiles, Boats and Motor Vehicles |
O-I (Owens Illinois), Inc. | Perrysburg | Manufacturing | Concrete, Glass, and Building Materials |
Big Lots Stores, Inc. | Columbus | Retail | Department Stores |
Limited Brands, Inc. | Columbus | Retail | Clothing and Shoes Stores |
Cardinal Health | Dublin | Healthcare, Pharmaceuticals and Biotech | Healthcare, Pharmaceuticals, and Biotech Other |
Progressive Corporation | Cleveland | Financial Services | Insurance and Risk Management |
Parker Hannifin Corporation | Cleveland | Manufacturing | Manufacturing Other |
American Financial Group, Inc. | Cincinnati | Financial Services | Insurance and Risk Management |
American Electric Power Company, Inc | Columbus | Energy and Utilities | Gas and Electric Utilities |
Fifth Third Bancorp | Cincinnati | Financial Services | Banks |
Macy's, Inc. | Cincinnati | Retail | Department Stores |
Goodyear Tire and Rubber Co. | Akron | Manufacturing | Plastics and Rubber Manufacturing |
The Kroger Co. | Cincinnati | Retail | Grocery and Specialty Food Stores |
Omnicare, Inc. | Cincinnati | Healthcare, Pharmaceuticals and Biotech | Pharmaceuticals |
The Procter and Gamble Company | Cincinnati | Consumer Services | Personal Care |
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 Ohio 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 C++ programming
- Get your questions answered by easy to follow, organized C++ experts
- Get up to speed with vital C++ 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…