Just like the snake it’s named after, Python has wrapped itself around the programming world, becoming a deeply entrenched teaching and practical tool since its 1991 introduction. It’s one of the world’s most used programming languages, with Statista claiming that 48.07% of programmers use it, making it as essential as SQL, C, and even HTML to computer scientists.


This article serves as an introduction to Python programming for beginners. You’ll learn Python basics, such as how to install it and the concepts that underpin the language. Plus, we’ll show you some basic Python code you can use to have a little play around with the language.


Python Basics


It stands to reason that you need to download and install Python onto your system before you can start using it. The latest version of Python is always available at Python.org. Different versions are available for Windows, Linux, macOS, iOS, and several other machines and operating systems.


Installing Python is a universal process across operating systems. Download the installer for your OS from Python.org and open its executable. Follow the instructions and you should have Python up and running, and ready for you to play around with some Python language basics, in no time.


Python IDEs and Text Editors


Before you can start coding in your newly-installed version of Python, you need to install an integrated development environment (IDE) to your system. These applications are like a bridge between the language you write in and the visual representation of that language on your screen. But beyond being solely source code editors, many IDEs serve as debuggers, compilers, and even feature automation that can complete code (or at least offer suggestions) on your behalf.


Some of the best Python IDEs include:


  • Atom
  • Visual Studio
  • Eclipse
  • PyCharm
  • Komodo IDE

But there are plenty more besides. Before choosing an IDE, ask yourself the following questions to determine if the IDE you’re considering is right for your Python project:


  • How much does it cost?
  • Is it easy to use?
  • What are its debugging and compiling features?
  • How fast is the IDE?
  • Does this IDE give me access to the libraries I’ll need for my programs?

Basic Python Concepts


Getting to grips with the Python basics for beginners starts with learning the concepts that underpin the language. Each of these concepts defines actions you can take in the language, meaning they’re essentially for writing even the simplest of programs.


Variables and Data Types


Variables in Python work much like they do for other programming languages – they’re containers in which you store a data value. The difference between Python and other languages is that Python doesn’t have a specific command used to declare a variable. Instead, you create a variable the moment you assign a value to a data type.


As for data types, they’re split into several categories, with most having multiple sub-types you can use to define different variables:


  • String – “str”
  • Numeric – “int,” “complex,” “float”
  • Sequence – “list,” “range,” “tuple”
  • Boolean – “bool”
  • Binary – “memoryview,” “bytes,” “bytearray”

There are more, though the above should be enough for your Python basics notes. Each of these data types serves a different function. For example, on the numerical side, “int” allows you to store signed integers of no defined length, while “float” lets you assign decimals up to 15 points.


Operators


When you have your variables and values, you’ll use operators to perform actions using them. These actions range from the simple (adding and subtracting numbers) to the complex (comparing values to each other). Though there are many types of operators you’ll learn as you venture beyond the Python language basics, the following three are some of the most important for basic programs:


  • Arithmetic operators – These operators allow you to handle most aspects of basic math, including addition, subtraction, division, and multiplication. There are also arithmetic operators for more complex operations, including floor division and exponentiation.
  • Comparison operators – If you want to know which value is bigger, comparison operators are what you use. They take two values, compare them, and give you a result based on the operator’s function.
  • Logical operators – “And,” “Or,” and “Not” are your logical operators and they combine to form conditional statements that give “True” or “False”

Control Structures


As soon as you start introducing different types of inputs into your code, you need control structures to keep everything organized. Think of them as the foundations of your code, directing variables to where they need to go while keeping everything, as the name implies, under control. Two of the most important control structures are:


  • Conditional Statements – “If,” “Else,” and “elif” fall into this category. These statements basically allow you to determine what the code does “if” something is the case (such as a variable equaling a certain number) and what “else” to do if the condition isn’t met.
  • Loops – “For” and “while” are your loop commands, with the former being used to create an iterative sequence, with the latter setting the condition for that sequence to occur.

Functions


You likely don’t want every scrap of code you write to run as soon as you start your program. Some chunks (called functions) should only run when they’re called by other parts of the code. Think of it like giving commands to a dog. A function will only sit, stay, or roll over when another part of the code tells it to do what it does.


You need to define and call functions.


Use the “def” keyword to define a function, as you see in the following example:


def first_function():


print (“This is my first function”)


When you need to call that function, you simply type the function’s name followed by the appropriate parenthesis:


first_function()


That “call” tells your program to print out the words “This is my first function” on the screen whenever you use it.


Interestingly, Python has a collection of built-in functions, which are functions included in the language that anybody can call without having to first define the function. Many relate to the data types discussed earlier, with functions like “str()” and “int()” allowing you to define strings and integers respectively.



Python – Basic Programs


Now that you’ve gotten to grips with some of the Python basics for beginners, let’s look at a few simple programs that almost anybody can run.


Hello, World! Program


The starting point for any new coder in almost any new language is to get the screen to print out the words “Hello, World!”. This one is as simple as you can get, as you’ll use the print command to get a piece of text to appear on screen:


print(‘Hello, World! ‘)


Click what “Run” button in your IDE of choice and you’ll see the words in your print command pop up on your monitor. Though this is all simple enough, make sure you make note of the use of the apostrophes/speech mark around the text. If you don’t have them, your message doesn’t print.


Basic Calculator Program


Let’s step things up with one of the Python basic programs for beginners that helps you to get to grips with functions. You can create a basic calculator using the language by defining functions for each of your arithmetic operators and using conditional statements to tell the calculator what to do when presented with different options.


The following example comes from Programiz.com:


# This function adds two numbers


def add(x, y):


return x + y


# This function subtracts two numbers


def subtract(x, y):


return x – y


# This function multiplies two numbers


def multiply(x, y):


return x * y


# This function divides two numbers


def divide(x, y):


return x / y


print(“Select operation.”)


print(“1.Add”)


print(“2.Subtract”)


print(“3.Multiply”)


print(“4.Divide”)


while True:


# Take input from the user


choice = input(“Enter choice(1/2/3/4): “)


# Check if choice is one of the four options


if choice in (‘1’, ‘2’, ‘3’, ‘4’):


try:


num1 = float(input(“Enter first number: “))


num2 = float(input(“Enter second number: “))


except ValueError:


print(“Invalid input. Please enter a number.”)


continue


if choice == ‘1’:


print(num1, “+”, num2, “=”, add(num1, num2))


elif choice == ‘2’:


print(num1, “-“, num2, “=”, subtract(num1, num2))


elif choice == ‘3’:


print(num1, “*”, num2, “=”, multiply(num1, num2))


elif choice == ‘4’:


print(num1, “/”, num2, “=”, divide(num1, num2))


# Check if user wants another calculation


# Break the while loop if answer is no


next_calculation = input(“Let’s do next calculation? (yes/no): “)


if next_calculation == “no”:


break


else:


print(“Invalid Input”)


When you run this code, your executable asks you to choose a number between 1 and 4, with your choice denoting which mathematical operator you wish to use. Then, you enter your values for “x” and “y”, with the program running a calculation between those two values based on the operation choice. There’s even a clever piece at the end that asks you if you want to run another calculation or cancel out of the program.


Simple Number Guessing Game


Next up is a simple guessing game that takes advantage of the “random” module built into Python. You use this module to generate a number between 1 and 99, with the program asking you to guess which number it’s chosen. But unlike when you play this game with your sibling, the number doesn’t keep changing whenever you guess the right answer.


This code comes from Python for Beginners:


import random


n = random.randint(1, 99)


guess = int(input(“Enter an integer from 1 to 99: “))


while True:


if guess < n:


print (“guess is low”)


guess = int(input(“Enter an integer from 1 to 99: “))


elif guess > n:


print (“guess is high”)


guess = int(input(“Enter an integer from 1 to 99: “))


else:


print (“you guessed it right! Bye!”)


break


Upon running the code, your program uses the imported “random” module to pick its number and then asks you to enter an integer (i.e., a whole number) between 1 and 99. You keep guessing until you get it right and the program delivers a “Bye” message.


Python Libraries and Modules


As you move beyond the basic Python language introduction and start to develop more complex code, you’ll find your program getting a bit on the heavy side. That’s where modules come in. You can save chunks of your code into a module, which is a file with the “.py” extension, allowing you to call that module into another piece of code.


Typically, these modules contain functions, variables, and classes that you want to use at multiple points in your main program. Retyping those things at every instance where they’re called takes too much time and leaves you with code that’s bogged down in repeated processes.


Libraries take things a step further by offering you a collection of modules that you can call from as needed, similar to how you can borrow any book from a physical library. Examples include the “Mayplotlib” library, which features a bunch of modules for data visualization, and “Beautiful Soup,” which allows you to extract data from XML and HTML files.



Best Practices and Tips for Basic Python Programs for Beginners


Though we’ve focused primarily on the code aspect of the language in these Python basic notes so far, there are a few tips that will help you create better programs that aren’t directly related to learning the language:


  • Write clean code – Imagine that you’re trying to find something you need in a messy and cluttered room. It’s a nightmare to find what you’re looking for because you’re constantly tripping over stuff you don’t need. That’s what happens in a Python program if you create bloated code or repeat functions constantly. Keep it clean and your code is easier to use.
  • Debugging and error handling – Buggy code is frustrating to users, especially if that code just dumps them out of a program when it hits an error. Beyond debugging (which everybody should do as standard) you must build error responses into your Python code to let users know what’s happening when something goes wrong.
  • Use online communities and resources – Python is one of the most established programming languages in the world, and there’s a massive community built up around it. Take advantage of those resources. Try your hand at a program first, then take it to the community to see if they can point you in the right direction.

Get to Grips With the Basic Concepts of Python


With these Python introduction notes, you have everything you need to understand some of the more basic aspects of the language, as well as run a few programs. Experimentation is your friend, so try taking what you’ve learned here and writing a few other simple programs for yourself. Remember – the Python community (along with stacks of online resources) are available to help you when you’re struggling.

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Il Sole 24 Ore: Integrating Artificial Intelligence into the Enterprise – Challenges and Opportunities for CEOs and Management
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 14, 2025 6 min read

Source:


Expert Pierluigi Casale analyzes the adoption of AI by companies, the ethical and regulatory challenges and the differentiated approach between large companies and SMEs

By Gianni Rusconi

Easier said than done: to paraphrase the well-known proverb, and to place it in the increasingly large collection of critical issues and opportunities related to artificial intelligence, the task that CEOs and management have to adequately integrate this technology into the company is indeed difficult. Pierluigi Casale, professor at OPIT (Open Institute of Technology, an academic institution founded two years ago and specialized in the field of Computer Science) and technical consultant to the European Parliament for the implementation and regulation of AI, is among those who contributed to the definition of the AI ​​Act, providing advice on aspects of safety and civil liability. His task, in short, is to ensure that the adoption of artificial intelligence (primarily within the parliamentary committees operating in Brussels) is not only efficient, but also ethical and compliant with regulations. And, obviously, his is not an easy task.

The experience gained over the last 15 years in the field of machine learning and the role played in organizations such as Europol and in leading technology companies are the requirements that Casale brings to the table to balance the needs of EU bodies with the pressure exerted by American Big Tech and to preserve an independent approach to the regulation of artificial intelligence. A technology, it is worth remembering, that implies broad and diversified knowledge, ranging from the regulatory/application spectrum to geopolitical issues, from computational limitations (common to European companies and public institutions) to the challenges related to training large-format language models.

CEOs and AI

When we specifically asked how CEOs and C-suites are “digesting” AI in terms of ethics, safety and responsibility, Casale did not shy away, framing the topic based on his own professional career. “I have noticed two trends in particular: the first concerns companies that started using artificial intelligence before the AI ​​Act and that today have the need, as well as the obligation, to adapt to the new ethical framework to be compliant and avoid sanctions; the second concerns companies, like the Italian ones, that are only now approaching this topic, often in terms of experimental and incomplete projects (the expression used literally is “proof of concept”, ed.) and without these having produced value. In this case, the ethical and regulatory component is integrated into the adoption process.”

In general, according to Casale, there is still a lot to do even from a purely regulatory perspective, due to the fact that there is not a total coherence of vision among the different countries and there is not the same speed in implementing the indications. Spain, in this regard, is setting an example, having established (with a royal decree of 8 November 2023) a dedicated “sandbox”, i.e. a regulatory experimentation space for artificial intelligence through the creation of a controlled test environment in the development and pre-marketing phase of some artificial intelligence systems, in order to verify compliance with the requirements and obligations set out in the AI ​​Act and to guide companies towards a path of regulated adoption of the technology.

Read the full article below (in Italian):

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The Lucky Future: How AI Aims to Change Everything
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 10, 2025 7 min read

There is no question that the spread of artificial intelligence (AI) is having a profound impact on nearly every aspect of our lives.

But is an AI-powered future one to be feared, or does AI offer the promise of a “lucky future.”

That “lucky future” prediction comes from Zorina Alliata, principal AI Strategist at Amazon and AI faculty member at Georgetown University and the Open Institute of Technology (OPIT), in her recent webinar “The Lucky Future: How AI Aims to Change Everything” (February 18, 2025).

However, according to Alliata, such a future depends on how the technology develops and whether strategies can be implemented to mitigate the risks.

How AI Aims to Change Everything

For many people, AI is already changing the way they work. However, more broadly, AI has profoundly impacted how we consume information.

From the curation of a social media feed and the summary answer to a search query from Gemini at the top of your Google results page to the AI-powered chatbot that resolves your customer service issues, AI has quickly and quietly infiltrated nearly every aspect of our lives in the past few years.

While there have been significant concerns recently about the possibly negative impact of AI, Alliata’s “lucky future” prediction takes these fears into account. As she detailed in her webinar, a future with AI will have to take into consideration:

  • Where we are currently with AI and future trajectories
  • The impact AI is having on the job landscape
  • Sustainability concerns and ethical dilemmas
  • The fundamental risks associated with current AI technology

According to Alliata, by addressing these risks, we can craft a future in which AI helps individuals better align their needs with potential opportunities and limitations of the new technology.

Industry Applications of AI

While AI has been in development for decades, Alliata describes a period known as the “AI winter” during which educators like herself studied AI technology, but hadn’t arrived at a point of practical applications. Contributing to this period of uncertainty were concerns over how to make AI profitable as well.

That all changed about 10-15 years ago when machine learning (ML) improved significantly. This development led to a surge in the creation of business applications for AI. Beginning with automation and robotics for repetitive tasks, the technology progressed to data analysis – taking a deep dive into data and finding not only new information but new opportunities as well.

This further developed into generative AI capable of completing creative tasks. Generative AI now produces around one billion words per day, compared to the one trillion produced by humans.

We are now at the stage where AI can complete complex tasks involving multiple steps. In her webinar, Alliata gave the example of a team creating storyboards and user pathways for a new app they wanted to develop. Using photos and rough images, they were able to use AI to generate the code for the app, saving hundreds of hours of manpower.

The next step in AI evolution is Artificial General Intelligence (AGI), an extremely autonomous level of AI that can replicate or in some cases exceed human intelligence. While the benefits of such technology may readily be obvious to some, the industry itself is divided as to not only whether this form of AI is close at hand or simply unachievable with current tools and technology, but also whether it should be developed at all.

This unpredictability, according to Alliata, represents both the excitement and the concerns about AI.

The AI Revolution and the Job Market

According to Alliata, the job market is the next area where the AI revolution can profoundly impact our lives.

To date, the AI revolution has not resulted in widespread layoffs as initially feared. Instead of making employees redundant, many jobs have evolved to allow them to work alongside AI. In fact, AI has also created new jobs such as AI prompt writer.

However, the prediction is that as AI becomes more sophisticated, it will need less human support, resulting in a greater job churn. Alliata shared statistics from various studies predicting as many as 27% of all jobs being at high risk of becoming redundant from AI and 40% of working hours being impacted by language learning models (LLMs) like Chat GPT.

Furthermore, AI may impact some roles and industries more than others. For example, one study suggests that in high-income countries, 8.5% of jobs held by women were likely to be impacted by potential automation, compared to just 3.9% of jobs held by men.

Is AI Sustainable?

While Alliata shared the many ways in which AI can potentially save businesses time and money, she also highlighted that it is an expensive technology in terms of sustainability.

Conducting AI training and processing puts a heavy strain on central processing units (CPUs), requiring a great deal of energy. According to estimates, Chat GPT 3 alone uses as much electricity per day as 121 U.S. households in an entire year. Gartner predicts that by 2030, AI could consume 3.5% of the world’s electricity.

To reduce the energy requirements, Alliata highlighted potential paths forward in terms of hardware optimization, such as more energy-efficient chips, greater use of renewable energy sources, and algorithm optimization. For example, models that can be applied to a variety of uses based on prompt engineering and parameter-efficient tuning are more energy-efficient than training models from scratch.

Risks of Using Generative AI

While Alliata is clearly an advocate for the benefits of AI, she also highlighted the risks associated with using generative AI, particularly LLMs.

  • Uncertainty – While we rely on AI for answers, we aren’t always sure that the answers provided are accurate.
  • Hallucinations – Technology designed to answer questions can make up facts when it does not know the answer.
  • Copyright – The training of LLMs often uses copyrighted data for training without permission from the creator.
  • Bias – Biased data often trains LLMs, and that bias becomes part of the LLM’s programming and production.
  • Vulnerability – Users can bypass the original functionality of an LLM and use it for a different purpose.
  • Ethical Risks – AI applications pose significant ethical risks, including the creation of deepfakes, the erosion of human creativity, and the aforementioned risks of unemployment.

Mitigating these risks relies on pillars of responsibility for using AI, including value alignment of the application, accountability, transparency, and explainability.

The last one, according to Alliata, is vital on a human level. Imagine you work for a bank using AI to assess loan applications. If a loan is denied, the explanation you give to the customer can’t simply be “Because the AI said so.” There needs to be firm and explainable data behind the reasoning.

OPIT’s Masters in Responsible Artificial Intelligence explores the risks and responsibilities inherent in AI, as well as others.

A Lucky Future

Despite the potential risks, Alliata concludes that AI presents even more opportunities and solutions in the future.

Information overload and decision fatigue are major challenges today. Imagine you want to buy a new car. You have a dozen features you desire, alongside hundreds of options, as well as thousands of websites containing the relevant information. AI can help you cut through the noise and narrow the information down to what you need based on your specific requirements.

Alliata also shared how AI is changing healthcare, allowing patients to understand their health data, make informed choices, and find healthcare professionals who meet their needs.

It is this functionality that can lead to the “lucky future.” Personalized guidance based on an analysis of vast amounts of data means that each person is more likely to make the right decision with the right information at the right time.

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