Hello creative guys, scientists and developers! Here I will be talking about the notions of math, programming and their combinatorics not in terms of showcasing advanced tools and expertise but in terms of the algorithmic thinking, the logic behind it and the piles of voluntary work required to make them work. But why in the first place some people cope better with math and programming and some others not? Let me dig way back into my past and give you my experiences since when I was a teenager studying math, physics and chemistry to enter the university…! The logic applies in computer science specializations as well.
To be good at math or even better, excel at it, you need a strong mind that can solve problems at the targeted areas being examined in the end. Furthermore, you need a passion for it that is different from studying history or composition. There have been classes where with the sole prerequisite of hard work, you are guaranteed to excel even though natural sciences are a little bit more challenging. You need to play on your own with the tools as well. I remember myself studying at the 1st and 2nd year of high school in Greece and being curious about the grand questions of cosmology, math Olympiads for I was awarded a prize back then from the Greek Mathematical Company or buying advanced and university level books on math and quantum physics to get excited with the material.
I was doing it on my own in other words, programming is no different. You need to have freedom of time to devote it to the commands, play with them, practice them in the appropriate environment and make sure you’ve spent as much time as requested so that your brain assimilates all the information. At the exact time of building foundations, if you hurry or if you’re not diligent you will fail, whatever is implied with that… The same rule applies to e.g. studying Python.
High school calculus and university calculus will unlock the access for numerous impressive studies later on – in the era of democratization of education -…! My expertise in specializations of computing science and technology industries spans from AI, digital marketing, programming in Python, quantum computing and applied quantum physics as well with a keen interest to skyrocket progress in the future. For this reason, we should all be familiar with the fact that foundations of programming as well as theoretical informatics lie on the grounds of variables and mathematical functions, math in other words, contrary to enough scientists that will not favor scientific engineering…
Take for example my classes in AI and my AI assignment in The University of Athens. Universities are obliged to teach you the science and the theories in terms of a … “huge catalog” of applications and interdisciplinary fields. Each one of those applications is an independent science in the working world and we need “objectivity” in programming. In my AI assignment for example I dealt with the theories of AI and how these apply to the fields of my digital marketing expertise with examples from Big Data, Sentiment Analysis, Large Language Models, MarTech, Google applications, Deep Learning, Genetic Algorithms, mathematics and examples with pseudocode, optimization problems, notions of graphs and theoretical informatics, advertising as an issue of artificial intelligence and furthermore, interesting encyclopedic and scientific knowledge in the era of digital transformation.
Technology from technology differs. Language from language differs. Application from application differs. Machine from machine differs. Science from science differs… Human from human differs… e.g. pay attention to examples such as Facebook (PHP), Web Development, Data Science Languages, Software Applications. A university degree is no longer relative to the assumption that a computer scientist knows everything in informatics, as is the case with AI where we study the science and the theories behind one field and let’s say 1-2 languages. There are dozens of languages in the market each one with different utility purposes, hybrid visual development tools depending on different utilities and plenty of applications with different key terms from the arena of applied mathematics.
In programming we also get used to the idea of invisible math, meaning, models defined by structured code using the simple technique of naming them as well as importing the appropriate libraries instead of programming formulas on our own. Pay also attention to the fact that a simple equation in Optimal Kalman Filtering for example from a book of 400 pages, requires an enormous big and difficult to conceive piece in a programming language such as MATLAB or Python. That’s for those who merely wish of advanced and higher math without knowing how to apply them in codified problem-solving situations.
Take for example my knowledge from studying Python programming and getting used with arrays, variables, lists, functions, methods, lists of lists, NumPy, matplotlib, dictionaries, pandas data-frames, functions such as int(), str(), bool(), float(), len(), round(), print(), type(), del(), sorted(), methods such as index(), count(), append() using empty lists and loops, remove(), reverse(), comparison operators, Boolean operators, array equivalents of Boolean operators, conditionals, filtering Pandas, loop data structures, Supervised Learning with Python, as well as lambda functions, list comprehensions, coding functions, data science toolboxes, etc, etc.
Even though default programming of the above is far from creating independent backend environments using Python in software development whereas targeting users and not ourselves demands error handling etc, default programming in Python works perfect for data science, scientific programming and AI in cloud environments such as Anaconda and Jupyter Notebooks. Scientists should get disillusioned of reaching a super-perfect expertise on Python such as this of ChatGPT and start structuring simple problems with the art and science of reverse engineering – decomposing a mammoth problem to get to design information and rules of work – as this is the case of this project as well, one scientist embarks on a journey to measure the world.
Have a great time folks!
To be good at math or even better, excel at it, you need a strong mind that can solve problems at the targeted areas being examined in the end. Furthermore, you need a passion for it that is different from studying history or composition. There have been classes where with the sole prerequisite of hard work, you are guaranteed to excel even though natural sciences are a little bit more challenging. You need to play on your own with the tools as well. I remember myself studying at the 1st and 2nd year of high school in Greece and being curious about the grand questions of cosmology, math Olympiads for I was awarded a prize back then from the Greek Mathematical Company or buying advanced and university level books on math and quantum physics to get excited with the material.
I was doing it on my own in other words, programming is no different. You need to have freedom of time to devote it to the commands, play with them, practice them in the appropriate environment and make sure you’ve spent as much time as requested so that your brain assimilates all the information. At the exact time of building foundations, if you hurry or if you’re not diligent you will fail, whatever is implied with that… The same rule applies to e.g. studying Python.
High school calculus and university calculus will unlock the access for numerous impressive studies later on – in the era of democratization of education -…! My expertise in specializations of computing science and technology industries spans from AI, digital marketing, programming in Python, quantum computing and applied quantum physics as well with a keen interest to skyrocket progress in the future. For this reason, we should all be familiar with the fact that foundations of programming as well as theoretical informatics lie on the grounds of variables and mathematical functions, math in other words, contrary to enough scientists that will not favor scientific engineering…
Take for example my classes in AI and my AI assignment in The University of Athens. Universities are obliged to teach you the science and the theories in terms of a … “huge catalog” of applications and interdisciplinary fields. Each one of those applications is an independent science in the working world and we need “objectivity” in programming. In my AI assignment for example I dealt with the theories of AI and how these apply to the fields of my digital marketing expertise with examples from Big Data, Sentiment Analysis, Large Language Models, MarTech, Google applications, Deep Learning, Genetic Algorithms, mathematics and examples with pseudocode, optimization problems, notions of graphs and theoretical informatics, advertising as an issue of artificial intelligence and furthermore, interesting encyclopedic and scientific knowledge in the era of digital transformation.
Technology from technology differs. Language from language differs. Application from application differs. Machine from machine differs. Science from science differs… Human from human differs… e.g. pay attention to examples such as Facebook (PHP), Web Development, Data Science Languages, Software Applications. A university degree is no longer relative to the assumption that a computer scientist knows everything in informatics, as is the case with AI where we study the science and the theories behind one field and let’s say 1-2 languages. There are dozens of languages in the market each one with different utility purposes, hybrid visual development tools depending on different utilities and plenty of applications with different key terms from the arena of applied mathematics.
In programming we also get used to the idea of invisible math, meaning, models defined by structured code using the simple technique of naming them as well as importing the appropriate libraries instead of programming formulas on our own. Pay also attention to the fact that a simple equation in Optimal Kalman Filtering for example from a book of 400 pages, requires an enormous big and difficult to conceive piece in a programming language such as MATLAB or Python. That’s for those who merely wish of advanced and higher math without knowing how to apply them in codified problem-solving situations.
Take for example my knowledge from studying Python programming and getting used with arrays, variables, lists, functions, methods, lists of lists, NumPy, matplotlib, dictionaries, pandas data-frames, functions such as int(), str(), bool(), float(), len(), round(), print(), type(), del(), sorted(), methods such as index(), count(), append() using empty lists and loops, remove(), reverse(), comparison operators, Boolean operators, array equivalents of Boolean operators, conditionals, filtering Pandas, loop data structures, Supervised Learning with Python, as well as lambda functions, list comprehensions, coding functions, data science toolboxes, etc, etc.
Even though default programming of the above is far from creating independent backend environments using Python in software development whereas targeting users and not ourselves demands error handling etc, default programming in Python works perfect for data science, scientific programming and AI in cloud environments such as Anaconda and Jupyter Notebooks. Scientists should get disillusioned of reaching a super-perfect expertise on Python such as this of ChatGPT and start structuring simple problems with the art and science of reverse engineering – decomposing a mammoth problem to get to design information and rules of work – as this is the case of this project as well, one scientist embarks on a journey to measure the world.
Have a great time folks!
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