Python Programming Training Classes in Frankfort, Kentucky
Training Suggestions from the Experts
An Experienced Python developer must have
... an understanding of the following topics: Map, Reduce and Filter, Numpy, Pandas, MatplotLib, File handling and Database integration. All of these requirements assume a solid grasp of Python Idioms that include iterators, enumerators, generators and list comprehensions.
To quickly get up to speed, we suggest you enroll in the following classes: Beginning Python and Advanced Python 3
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Learn Python Programming in Frankfort, Kentucky 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 Python Programming related training offerings in Frankfort, Kentucky: Python Programming Training
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- Object-Oriented Programming in C# Rev. 6.1
17 November, 2025 - 21 November, 2025 - RHCSA EXAM PREP
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8 December, 2025 - 11 December, 2025 - See our complete public course listing
Blog Entries publications that: entertain, make you think, offer insight
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.
Smart Project Management –Best Practices of Good Managers
Project management could be one of the easiest jobs on the planet, and could also be the worst nightmare. The difference between the two extremes depends on smart management of a project. According to the project management institute, there are five phases in project management - Initiating, Planning, Executing, Monitoring & Controlling, and Closing.
Every manager has his own style of project management. But there are a lot of contributing factors that result in a successfully managed project. These factors vary from project to project, but they all contain some common elements.
1. Setting SMART Goals
In Python, we can create three types of methods in a class: instance or regular method, classmethod and staticmethod. Instance methods are associated, as the name infers, with an instance or object of the class and take self as the first parameter. Classmethods take a reference to the class, cls, as the first parameter of the class. Staticmethods, for the most part, are convenience methods that could be declared as functions since they really do not have much to do with the class itself. They were probably added at some time after the advent of Python in order to make the language more object oriented i.e. minimize the number of free floating functions.
Refer the our article static, class and regular methods in Python for a detailed explanation on this subject.
The Zen of Python, by Tim Peters has been adopted by many as a model summary manual of python's philosophy. Though these statements should be considered more as guideline and not mandatory rules, developers worldwide find the poem to be on a solid guiding ground.
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
Tech Life in Kentucky
| Company Name | City | Industry | Secondary Industry |
|---|---|---|---|
| Brown-Forman Beverages Worldwide | Louisville | Manufacturing | Alcoholic Beverages |
| General Cable Corporation | Newport | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| PharMerica Corporation | Louisville | Software and Internet | Data Analytics, Management and Storage |
| Humana Inc. | Louisville | Financial Services | Insurance and Risk Management |
| Lexmark International, Inc. | Lexington | Computers and Electronics | Peripherals Manufacturing |
| YUM! Brands, Inc. | Louisville | Retail | Restaurants and Bars |
| ResCare, Inc. | Louisville | Healthcare, Pharmaceuticals and Biotech | Doctors and Health Care Practitioners |
| Kindred Healthcare, Inc. | Louisville | Healthcare, Pharmaceuticals and Biotech | Residential and Long-Term Care Facilities |
| Ashland Inc | Covington | 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 Kentucky 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 Python Programming programming
- Get your questions answered by easy to follow, organized Python Programming experts
- Get up to speed with vital Python Programming 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
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