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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.
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:
- Visual Studio
- 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.
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”
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.
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:
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:
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
# 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’):
num1 = float(input(“Enter first number: “))
num2 = float(input(“Enter second number: “))
print(“Invalid input. Please enter a number.”)
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”:
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:
n = random.randint(1, 99)
guess = int(input(“Enter an integer from 1 to 99: “))
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: “))
print (“you guessed it right! Bye!”)
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.
In April 1999, a $433 million Air Force rocket inexplicably malfunctioned almost immediately after liftoff, causing the permanent loss of an $800 million military communications satellite. This $1.2 billion disaster remains one of the costliest accidents in human history.
You might wonder if scientists ever found out what caused this misfiring. They sure did! And the answer is a software bug.
This accident alone is a testament to the importance of software testing.
Although you can probably deduce the software testing definition, let’s also review it together.
So, what is software testing?
Software testing refers to running a software program before putting it on the market to determine whether it behaves as expected and displays no defects.
While testing itself isn’t free, these expenses are cost-effective compared to potential money loss resulting from software failure. And this is just one of the benefits of this process. Others include improving performance, preventing human and equipment loss, and increasing stakeholder confidence.
Now that you understand why software testing is such a big deal, let’s inspect this process in more detail.
Software Testing Fundamentals
We’ll start with the basics – what are the fundamentals of testing in software engineering? In other words, what exactly is its end goal, and which principles underlie it?
Regarding the objectives of software testing, there are three distinct ones aiming to answer crucial questions about the software.
- Verification and validation. Does the software meet all the necessary requirements? And does it satisfy the end customer?
- Defects and errors identification. Does the software have any defects or errors? What is their scope and impact? And did they cause related issues?
- Software quality assurance. Is the software performing at optimal levels? Can the software engineering process be further optimized?
As for principles of software testing, there are seven of them, and they go as follows:
- Testing shows the presence of defects. With everything we’ve written about software testing, this sounds like a given. But this principle emphasizes that testing can only confirm the presence of defects. It can’t confirm their absence. So, even if no flaws are found, it doesn’t mean the system has none.
- Exhaustive testing is impossible. Given how vital software testing is, this process should ideally test all the possible scenarios to confirm the program is defect-free without a shadow of a doubt. Unfortunately, this is impossible to achieve in practice. There’s simply not enough time, money, or space to conduct such testing. Instead, test analysts can only base the testing amount on risk assessment. In other words, they’ll primarily test elements that are most likely to fail.
- Testing should start as early as possible. Catching defects in the early stages of software development makes all the difference for the final product. It also saves lots of money in the process. For this reason, software testing should start from the moment its requirements are defined.
- Most defects are within a small number of modules. This principle, known as defect clustering, follows the Pareto principle or the 80/20 rule. The rule states that approximately 80% of issues can be found in 20% of modules.
- Repetitive software testing is useless. Known as the Pesticide Paradox, this principle warns that conducting the same tests to discover new defects is a losing endeavor. Like insects become resistant to a repeatedly used pesticide mix, the tested software will become “immune” to the same tests.
- Testing is context-dependent. The same set of tests can rarely be used on two separate software programs. You’ll need to switch testing techniques, methodologies, and approaches based on the program’s application.
- The software program isn’t necessarily usable, even without defects. This principle is known as the absence of errors fallacy. Just because a system is error-free doesn’t mean it meets the customer’s business needs. In software testing objectives, software validation is as important as verification.
Types of Software Testing
There are dozens (if not hundreds) types of testing in software engineering. Of course, not all of these tests apply to all systems. Choosing the suitable types of testing in software testing boils down to your project’s nature and scope.
All of these testing types can be broadly classified into three categories.
Functional software testing types examine the system to ensure it performs in accordance with the pre-determined functional requirements. We’ll explain each of these types using e-commerce as an example.
- Unit Testing – Checking whether each software unit (the smallest system component that can be tested) performs as expected. (Does the “Add to Cart” button work?)
- Integration Testing – Ensuring that all software components interact correctly within the system. (Is the product catalog seamlessly integrated with the shopping cart?)
- System Testing – Verifying that a system produces the desired output. (Can you complete a purchase?)
- Acceptance Testing – Ensuring that the entire system meets the end users’ needs. (Is all the information accurate and easy to access?)
Non-functional types of testing in software engineering deal with the general characteristics of a system beyond its functionality. Let’s go through the most common non-functional tests, continuing the e-commerce analogy.
- Performance Testing – Evaluating how a system performs under a specific workload. (Can the e-commerce shop handle a massive spike in traffic without crashing?)
- Usability Testing – Checking the customer’s ability to use the system effectively. (How quickly can you check out?)
- Security Testing – Identifying the system’s security vulnerabilities. (Will sensitive credit card information be stored securely?)
- Compatibility Testing – Verifying if the system can run on different platforms and devices. (Can you complete a purchase using your mobile phone?)
- Localization Testing – Checking the system’s behavior in different locations and regions. (Will time-sensitive discounts take time zones into account?)
Maintenance testing takes place after the system has been produced. It checks whether (or how) the changes made to fix issues or add new features have affected the system.
- Regression Testing – Checking whether the changes have affected the system’s functionality. (Does the e-commerce shop work seamlessly after integrating a new payment gateway?)
- Smoke Testing – Verifying the system’s basic functionality before conducting more extensive (and expensive!) tests. (Can the new product be added to the cart?)
- Sanity Testing – Determining whether the new functionality operates as expected. (Does the new search filter select products adequately?)
Levels of Software Testing
Software testing isn’t done all at once. There are levels to it. Four, to be exact. Each level contains different types of tests, grouped by their position in the software development process.
Read about the four levels of testing in software testing here.
Level 1: Unit Testing
Unit testing helps developers determine whether individual system components (or units) work properly. Since it takes place at the lowest level, this testing sets the tone for the rest of the software development process.
This testing plays a crucial role in test-driven development (TDD). In this methodology, developers perform test cases first and worry about writing the code for software development later.
Level 2: Integration Testing
Integration testing focuses on the software’s inner workings, checking how different units and components interact. After all, you can’t test the system as a whole if it isn’t coherent from the start.
During this phase, testers use two approaches to integration testing: top-down (starting with the highest-level units) and bottom-up (integrating the lowest-level units first).
Level 3: System Testing
After integration testing, the system can now be evaluated as a whole. And that’s exactly what system testing does.
System testing methods are usually classified as white-box or black-box testing. The primary difference is whether the testers are familiar with the system’s internal code structure. In white-box testing, they are.
Level 4: Acceptance Testing
Acceptance testing determines whether the system delivers on its promises. Two groups are usually tasked with acceptance testing: quality assessment experts (alpha testing before the software launches) and a limited number of users (beta testing in a real-time environment).
Software Testing Process
Although some variations might exist, the software testing process typically follows the same pattern.
Step 1: Planning the Test
This step entails developing the following:
- Test strategy for outlining testing approaches
- Test plan for detailing testing objectives, priorities, and processes
- Test estimation for calculating the time and resources needed to complete the testing process
Step 2: Designing the Test
In the design phase, testers create the following:
- Test scenarios (hypothetical situations used to test the system)
- Test cases (instructions on how the system should be tested)
- Test data (set of values used to test the system)
Step 3: Executing the Test
Text execution refers to performing (and monitoring) the planned and designed tests. This phase begins with setting up the test environment and ends with writing detailed reports on the findings.
Step 4: Closing the Test
After completing the testing, testers generate relevant metrics and create a summary report on their efforts. At this point, they have enough information to determine whether the tested software is ready to be released.
High-Quality Testing for High-Quality Software
Think of different types of software testing as individual pieces of a puzzle that come together to form a beautiful picture. Performing software testing hierarchically (from Level 1 to Level 4) ensures no stone is left unturned, and the tested software won’t let anyone down.
With this in mind, it’s easy to conclude that you should only attempt software development projects if you implement effective software testing practices first.
The term “big data” is self-explanatory: it’s a large collection of data. However, to be classified as “big,” data needs to meet specific criteria. Big data is huge in volume, gets even bigger over time, arrives with ever-higher velocity, and is so complex that no traditional tools can handle it.
Big data analytics is the (complex) process of analyzing these huge chunks of data to discover different information. The process is especially important for small companies that use the uncovered information to design marketing strategies, conduct market research, and follow the latest industry trends.
In this introduction to big data analytics, we’ll dig deep into big data and uncover ways to analyze it. We’ll also explore its (relatively short) history and evolution and present its advantages and drawbacks.
History and Evolution of Big Data
We’ll start this introduction to big data with a short history lesson. After all, we can’t fully answer the “what is big data?” question if we don’t know its origins.
Let’s turn on our time machine and go back to the 1960s. That’s when the first major change that marked the beginning of the big data era took place. The advanced development of data centers, databases, and innovative processing methods facilitated the rise of big data.
Relational databases (storing and offering access to interconnected data points) have become increasingly popular. While people had ways to store data much earlier, experts consider that this decade set the foundations for the development of big data.
The next major milestone was the emergence of the internet and the exponential growth of data. This incredible invention made handling and analyzing large chunks of information possible. As the internet developed, big data technologies and tools became more advanced.
This leads us to the final destination of short time travel: the development of big data analytics, i.e., processes that allow us to “digest” big data. Since we’re witnessing exceptional technological developments, the big data journey is yet to continue. We can only expect the industry to advance further and offer more options.
Big Data Technologies and Tools
What tools and technologies are used to decipher big data and offer value?
Data Storage and Management
Data storage and management tools are like virtual warehouses where you can pack up your big data safely and work with it as needed. These tools feature a powerful infrastructure that lets you access and fetch the desired information quickly and easily.
Data Processing and Analytics Framework
Processing and analyzing huge amounts of data are no walk in the park. But they can be, thanks to specific tools and technologies. These valuable allies can clean and transform large piles of information into data you can use to pursue your goals.
Machine Learning and Artificial Intelligence Platforms
Machine learning and artificial intelligence platforms “eat” big data and perform a wide array of functions based on the discoveries. These technologies can come in handy with testing hypotheses and making important decisions. Best of all, they require minimal human input; you can relax while AI works its magic.
Data Visualization Tools
Making sense of large amounts of data and presenting it to investors, stakeholders, and team members can feel like a nightmare. Fortunately, you can turn this nightmare into a dream come true with big data visualization tools. Thanks to the tools, creating stunning graphs, dashboards, charts, and tables and impressing your coworkers and superiors has never been easier.
Big Data Analytics Techniques and Methods
What techniques and methods are used in big data analytics? Let’s find the answer.
Descriptive analytics is like a magic wand that turns raw data into something people can read and understand. Whether you want to generate reports, present data on a company’s revenue, or analyze social media metrics, descriptive analytics is the way to go.
It’s mostly used for:
- Data summarization and aggregation
- Data visualization
Have a problem and want to get detailed insight into it? Diagnostic analytics can help. It identifies the root of an issue, helping you figure out your next move.
Some methods used in diagnostic analytics are:
- Data mining
- Root cause analysis
Predictive analytics is like a psychic that looks into the future to predict different trends.
Predictive analytics often uses:
- Regression analysis
- Time series analysis
Prescriptive analytics is an almighty problem-solver. It usually joins forces with descriptive and predictive analytics to offer an ideal solution to a particular problem.
Some methods prescriptive analytics uses are:
- Optimization techniques
- Simulation and modeling
Applications of Big Data Analytics
Big data analytics has found its home in many industries. It’s like the not-so-secret ingredient that can make the most of any niche and lead to desired results.
Business and Finance
How do business and finance benefit from big data analytics? These industries can flourish through better decision-making, investment planning, fraud detection and prevention, and customer segmentation and targeting.
Healthcare is another industry that benefits from big data analytics. In healthcare, big data is used to create patient databases, personal treatment plans, and electronic health records. This data also serves as an excellent foundation for accurate statistics about treatments, diseases, patient backgrounds, risk factors, etc.
Government and Public Sector
Big data analytics has an important role in government and the public sector. Analyzing different data improves efficiency in terms of costs, innovation, crime prediction and prevention, and workforce. Multiple government parts often need to work together to get the best results.
As technology advances, big data analytics has found another major use in the government and public sector: smart cities and infrastructure. With precise and thorough analysis, it’s possible to bring innovation and progress and implement the latest features and digital solutions.
Sports and Entertainment
Sports and entertainment are all about analyzing the past to predict the future and improve performance. Whether it’s analyzing players to create winning strategies or attracting the audience and freshening up the content, big data analytics is like a valuable player everyone wants on their team.
Challenges and Ethical Considerations in Big Data Analytics
Big data analytics represent doors to new worlds of information. But opening these doors often comes with certain challenges and ethical considerations.
Data Privacy and Security
One of the major challenges (and the reason some people aren’t fans of big data analytics) is data privacy and security. The mere fact that personal information can be used in big data analytics can make individuals feel exploited. Since data breaches and identity thefts are, unfortunately, becoming more common, it’s no surprise some people feel this way.
Fortunately, laws like GDPR and CCPA give individuals more control over the information others can collect from them.
Data Quality and Accuracy
Big data analytics can sometimes be a dead end. If the material wasn’t handled correctly, or the data was incomplete to start with, the results themselves won’t be adequate.
Algorithmic Bias and Fairness
Big data analytics is based on algorithms, which are designed by humans. Hence, it’s not unusual to assume that these algorithms can be biased (or unfair) due to human prejudices.
Ethical Use of Big Data Analytics
The ethical use of big data analytics concerns the “right” and “wrong” in terms of data usage. Can big data’s potential be exploited to the fullest without affecting people’s right to privacy?
Future Trends and Opportunities in Big Data Analytics
Although it has proven useful in many industries, big data analytics is still relatively young and unexplored.
Integration of Big Data Analytics With Emerging Technologies
It seems that new technologies appear in the blink of an eye. Our reality today (in a technological sense) looks much different than just two or three years ago. Big data analytics is now intertwined with emerging technologies that give it extra power, accuracy, and quality.
Cloud computing, advanced databases, the Internet of Things (IoT), and blockchain are only some of the technologies that shape big data analytics and turn it into a powerful giant.
Advancements in Machine Learning and Artificial Intelligence
Machines may not replace us (at least not yet), but it’s impossible to deny their potential in many industries, including big data analytics. Machine learning and artificial intelligence allow for analyzing huge amounts of data in a short timeframe.
Machines can “learn” from their own experience and use this knowledge to make more accurate predictions. They can pinpoint unique patterns in piles of information and estimate what will happen next.
New Applications and Industries Adopting Big Data Analytics
One of the best characteristics of big data analytics is its versatility and flexibility. Accordingly, many industries use big data analytics to improve their processes and achieve goals using reliable information.
Every day, big data analytics finds “new homes” in different branches and niches. From entertainment and medicine to gambling and architecture, it’s impossible to ignore the importance of big data and the insights it can offer.
These days, we recognize the rise of big data analytics in education (personalized learning) and agriculture (environmental monitoring).
Workforce Development and Education in Big Data Analytics
Analyzing big data is impossible without the workforce capable of “translating” the results and adopting emerging technologies. As big data analytics continues to develop, it’s vital not to forget about the cog in the wheel that holds everything together: trained personnel. As technology evolves, specialists need to continue their education (through training and certification programs) to stay current and reap the many benefits of big data analytics.
Turn Data to Your Advantage
Whatever industry you’re in, you probably have goals you want to achieve. Naturally, you want to achieve them as soon as possible and enjoy the best results. Instead of spending hours and hours going through piles of information, you can use big data analytics as a shortcut. Different types of big data technologies can help you improve efficiency, analyze risks, create targeted promotions, attract an audience, and, ultimately, increase revenue.
While big data offers many benefits, it’s also important to be aware of the potential risks, including privacy concerns and data quality.
Since the industry is changing (faster than many anticipated), you should stay informed and engaged if you want to enjoy its advantages.
Books represent gateways to new worlds, allowing us to gain valuable knowledge on virtually any topic. Those interested in exploring computer science books face two challenges. First, just like you can’t build a good house without a proper foundation, you can’t expand your knowledge if you don’t understand basic concepts. Secondly, technology is always evolving, so besides understanding how things work, you need to stay current with the latest trends.
Finding books that help you build a good foundation and follow innovations isn’t easy. Fortunately, you don’t have to go through hundreds of titles to find the good ones. Here, we’ll introduce you to the best BSc Computer Science books that will set you up for success.
Top BSc Computer Science Books
These BSc Computer Science books can “program” your mind and help you absorb knowledge.
Introduction to Computer Science
Many people are eager to learn how to program and immerse themselves in the IT world. But the first step toward that is adopting fundamentals. Before jumping into the IT industry, you need to learn more about computer science and the basic concepts behind it.
Computer Science Illuminated by Nell Dale and John Lewis
This student-friendly book sheds light on computer science. It explores operating systems, hardware, software, and networks from “neutral ground” (without focusing on particular programming languages). Therefore, if you don’t “speak” programming languages just yet, this book will be your best friend.
Intro to Python for Computer Science and Data Science: Learning to Program With AI, Big Data, and the Cloud by Paul Deitel and Harvey Deitel
If you want to be a programming expert, you may need to speak Python, a universal language with a wide array of applications. This book teaches you how to use Python in computer science and offers the perfect balance between theoretical and practical knowledge. It transforms complex information into comprehensive and engaging data.
Data Structures and Algorithms
Finding the best BSc Computer Science book on data structures and algorithms can feel like trying to find a needle in a haystack. We found the needle for you and offer the best options.
Data Structures and Algorithms Made Easy by Narasimha Karumanchi
This book is a winner in the data structures and algorithms game. It’s the perfect option for beginners interested in learning the topic from scratch and building a solid foundation for more advanced levels. It covers basic concepts and moves on to more complex stuff without overwhelming the readers.
Data Structures and Algorithms in Java by Robert Lafore
If you’re familiar with Java and want to start with data structures and algorithms, this book is the gold standard. It will guide you on a journey from basic Arrays and Strings to advanced structures like Hash-Tables and Graphs.
Computer networks are grids through which computing devices “talk to” each other and share data. Here are the books you can use to improve your knowledge and get ahead in your career.
Computer Networks by Andrew S. Tanenbaum
If you want to understand the nitty-gritty behind computer networks, this book is the way to go. Hop on a journey through email, the world wide web, video conferencing, and much more, to understand how the networks work and how to use them to your advantage.
Every chapter follows the same, easy-to-follow structure containing basic principles and real-life examples.
Computer Networking: A Top-Down Approach by James F. Kurose and Keith W. Ross
This beginner-friendly book takes a somewhat unusual approach. It first introduces students to applications and uses them to explain fundamental concepts. That way, students are exposed to the “real world” early on and can understand how networking works with ease.
An operating system for a computer is like oxygen for a human; it can’t live without it. Operating systems are interfaces that support everything computers do. Here are the best books about them.
Operating Systems: Three Easy Pieces by Remzi Arpaci-Dusseau and Andrea Arpaci-Dusseau
How do operating systems work? What are the three basic concepts hiding behind every OS? Find the answers to these questions and learn everything OS-related in this book. While beginner-friendly, this amazing study can be combined with more advanced materials and offer a deeper understanding of modern OSs.
Guide to Operating Systems by Greg Tomsho
This book represents a detailed guide on installing, updating, maintaining, and configuring operating systems and everything related to them. Besides offering general info, the book explores specific OSs and allows you to peek into this world without feeling overwhelmed.
Database systems are like virtual warehouses where you can keep your data secure. They’re the ones we can “thank” for easy information retrieval, browsing, and organization. If you want to learn the ins and outs of database systems, these books can help.
Database Systems: The Complete Book by Hector Garcia-Molina, Jeffrey D. Ullman, and Jennifer Widom
This book is the holy grail for many computer science students. It offers a comprehensive approach and detailed explanations of everything related to database system design, use, and implementation. The book is extensive, but it’s written in an engaging way, so reading through it is a breeze.
Database Systems: Design, Implementation, & Management by Carlos Colonel and Steven Morris
Building your virtual warehouses for storing data may seem impossible. But it can become your reality thanks to this excellent book. It contains clear and comprehensive instructions on building database systems, offers concrete examples, but also focuses on the bigger picture and latest industry trends.
Designing and constructing software is no walk in the park. If you’re interested in this industry, you need to build your skills meticulously. Books that can help you on this exciting (and sometimes frustrating) journey are reviewed below.
Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
In this book, Robert C. Martin, a software engineering legend, discusses the seemingly insignificant differences between bad and poorly-written codes. He explains which “symptoms” bad codes manifest and how to clean them.
Code Complete: A Practical Handbook of Software Construction by Steve McConnell
One of the first (and smartest) steps toward building quality code is getting this book. Here, the author summarized everything there is to know about constructing software. Since the book contains both the basics and the more advanced construction practices, everyone finds it useful, both beginners and pros.
Additional Resources for BSc Computer Science Students
BSc Computer Science books aren’t the only spring you should drink water from if you’re thirsty for knowledge on the subject.
Online Platforms and Courses
Online platforms and courses are great resources for those who want to expand their knowledge and learn how to cash it in. The internet is overflowing with great courses focusing on various aspects of computer science. Here are a few ideas to get you started:
- Open Institute of Technology (OPIT) – The institute offers a comprehensive online BSc in Computer Science. Throughout the program, students get acquainted with everything computer science-related. After completing their studies, they’ll be able to land high-paying jobs.
- Udemy and Coursera – Although not “official” institutes and universities, these platforms deserve a seat at the table. Both Udemy and Coursera offer quality computer science courses held by some of the most respected names in the industry.
Coding Practice Websites
You’ve read books, attended courses, and feel like you know everything there is to know about the theoretical part. But is there a way to put this theory into practice and see whether your codes work? The answer is yes! Practice makes perfect, and coding practice websites will become your best friends and help you conquer programming.
- Coderbyte – Solve real-life coding issues and drive your skills to perfection. With over a dozen available programming languages, you can try out as many ideas as you’d like.
- HackerRank – HackerRank is home to hundreds of coding challenges. Plus, it has leaderboards, so you can see how you compare to other coders. It’s also home to useful tutorials, and since the website is popular, you may even be able to land your dream job.
Computer Science Forums and Communities
Is there a better place for like-minded people to meet and discuss the topics they’re passionate about? Computer science forums and communities should be an important stop on your way to becoming an expert on the subject.
Tips for Success in BSc Computer Science
Success doesn’t happen overnight (at least for most people). If computer science is your true passion, here’s how to master it:
- Focus on the basics to create a good foundation.
- Put your thinking cap on and practice problem-solving and critical thinking skills.
- Participate in group projects and collaborations (teamwork makes the dream work).
- Keep up with the latest industry trends.
- Gain valuable hands-on experience through internships.
Acquire Computer Science Knowledge Effectively
Although books don’t offer practical knowledge, they can be invaluable allies in setting a great theoretical foundation. By carefully choosing the best books and putting effort into developing your skills, you’ll become a pro in a jiff.
Computers are already ubiquitous in the workplace, with the constantly-evolving concept of cloud computing becoming so popular that Tech Jury says 90% of businesses were in the cloud (in some form) in 2022. All of those systems need maintenance and software, requiring people who are dab-hands with keyboards at their fingertips to build networks, analyze data, and develop software.
Enter computer scientists.
By studying computer science, you open yourself up to a branching career path that could take you into almost any sort of business. But before that, you need to know the answer to a simple question – “Is BSc Computer Science a good course?”
Understanding BSc Computer Science
Think of a BSc in Computer Science as though it’s a buffet, with every topic covered being a different dish. You’ll get a taste of everything that’s on offer in the computing field, with your later educational (and career) decisions being based on the dish (i.e., the topic) that you like best. Among those topics and study areas are the following:
- Networks and Computer Systems – Taking a more hardware-oriented focus (though software plays a part), this topic covers how to connect computers so they can interact with one another.
- Programming – The language of computers is one you’ll need to learn how to speak if you want to develop software or websites. You’ll discover that there are a lot of languages to choose from, each with its own specific uses.
- Artificial Intelligence (AI) – As one of the fastest-growing fields in computing (Statista anticipates growth from $100 billion in 2021 to almost $2 trillion by 2030), AI is already becoming essential in business. You’ll learn the concepts that govern AI, such as machine learning and neural networks.
- Network Security – Every advancement in computer science brings with it malicious parties who wish to use (or subvert) that advancement to their own ends. Computer science courses teach the foundational aspects of network security, setting the stage for later specialization.
Moving beyond what you study (and the above isn’t an exhaustive list of topics), how long you spend on earning your BSc in Computer Science is another key deciding factor. Most traditional universities offer three-year courses, extending to four years if you take an internship or in-course work. The newer breed of online universities offer more flexibility, with some fast-track courses taking as little as two years, while others offer a more free-form version of study that lets you move at your own pace. With the latter, you could take several more years to complete your degree, though you’ll be able to fit your studies around work and family more easily than you would with a full-time course.
Benefits of BSc Computer Science
Assuming you’re willing to place the time (and monetary) investment into a BSc in Computer Science, there are three core benefits you’ll get from the course.
1 – Acquire In-Demand Skills and Knowledge
The basics you learn are in demand in most companies, with many offering additional training and tuition to help you build beyond the basics to become a specialist. Key areas of interest for employers include:
- Programming – Those who can speak the language that lies behind software are always in demand, with programmers earning an average hourly rate of $33.10, according to Indeed. Salary expectations climb as you move through the ranks, with senior software engineers capable of earning in the early six figures.
- Data Structures and Algorithms – Problem solvers are popular in any business. The knowledge of algorithms you develop when studying computer science allows you to create code (almost like a set of steps) that’s designed to solve problems. The same applies to data structures, which focus on the locations and methods used to keep data organized.
- Computer Networks and Security – Even a small office has a network of computers, laptops, smart devices, printers, and servers that all need to communicate with one another. Computer scientists enable that communication, and keep the “conversations” machines have with each other shielded from intruding eyes.
2 – Versatility and Adaptability in the Job Market
Computer science graduates are like the chameleons of the job market. They have so much foundational knowledge in an array of subjects that they’re well-placed to be “Jacks of all trades” as general computer experts. Plus, the base they have can be built from, setting the stage for them to specialize in specific areas of computing based on their preferences.
We’ll dig into some specific roles you could take (along with their salaries) in the next section of the article.
3 – Opportunities for Further Education and Specialization
You’re already part way down the road to computer science mastery once you have your BSc, so why stop there? The opportunity exists for further education and specialization, which could open the door to further career opportunities:
- Masters and Ph.D. Programs – A Master’s degree in computer science (or a related subject) is the next logical educational step once you have your BSc. You’ll build on what you’ve already learned, in addition to having a chance to specialize in your thesis. PhD programs aren’t immediately open (you’ll need your Master’s first) but they give you a chance to delve into subject-specific research and could set you up for a career in teaching computer science.
- Professional Certifications – If you prefer the less formal educational route, professional certifications enable you to study at your own pace and give you handy pieces of paper you can use to prove your skills. Great examples include Cisco’s CCIE program and CompTIA’s range of certifications.
Job Prospects and Career Opportunities
Building on the previous mention about your chameleon-like ability to get jobs in multiple fields, you need to know is BSc in Computer Science good for the career-focused student. These are the roles you can get (with salary data from Indeed).
Software Development and Engineering
Rather than being the person who uses software, you can be the person who forms and puts together the building blocks that make the software tick. Software developers and engineers use their coding skills to create the next great apps, websites, computer games, and anything else that needs a computer or mobile device to run.
Average Salary – $114,470
Data Analysis and Data Science
Data, data everywhere, and not a drop to drink. That little spin on the classic “lost at sea” phrase tells you everything you need to know about how many companies feel in the Big Data world. They’re collecting tons of data but don’t know how to organize what they have or extract useful information from it. Data analysts and scientists solve that problem.
Average Salary (Data Analyst) – $74,570
Average Salary (Data Scientist) – $129,574
Cybersecurity and Network Administration
There’s a never-ending battle being waged between network administrators and hackers, with each trying to stay one step ahead of the other. Cyberattacks are on the rise, with Security Magazine pointing out that attacks around the globe increased by 38% in 2022. That means there’s always demand for cybersecurity specialists.
Average Salary – $107,063
Research and Academia
Rather than using your skills to benefit private enterprises, you could be responsible for the next generation of computer scientists. The academic path is a noble one, though not always the most profitable, and it affords you the chance to research the subjects you’re passionate about. The level you reach in academia depends on your own academic accomplishments, with a BSc usually being enough for school-level teaching. You’ll need a Master’s or Ph.D. to go into further education or complex research.
Average Salary (Computing Teacher) – $26.79 per hour
Entrepreneurship and Freelance Opportunities
Why restrict yourself to a single company when you could build your own or spread your scientific seeds wide by becoming a freelancer? More control over your destiny is the biggest benefit of this career path, though there’s a more “sink or swim” mentality. Those who hit it big with a great business idea can hit it really big, but there are plenty of failed computing businesses on the entrepreneurial road.
Average Salary – It all depends on what you do and how well you do it
Factors to Consider When Evaluating the Worth of BSc Computer Science
If you’re still asking “Is BSc Computer Science a good course?” the answer is a definite “yes.” But there are some factors to consider before you commit to several years of computing studies:
- Personal Interests and Aptitude – Success in any area of study requires a passion for your subject and a certain amount of talent in the field. If you’re missing one (or both) of these for computer science then a BSc may not be for you.
- Job Market Trends – It’s very possible to make a six-figure salary as a computer scientist, though specialization is often needed to hit the highest figures. Still, it’s worth keeping an eye on what’s happening with the job market to ensure you’re studying toward a future role.
- Return on Investment – Undergraduate programs can cost anywhere between $15,000 and $85,000, so you need to feel confident that a computer science course is the right one for your future career. Otherwise, you’re left with a massive hole in your bank balance that you need to fill with student loan repayments.
- Job Satisfaction – Working yourself into the ground is never a good thing. You need to feel confident that you’ll achieve the appropriate balance between your work, personal, and family lives.
Comparing BSc Computer Science With Other Courses
A BSc in Computer Science is far from your only choice if you’re interested in delving into computers. Here are three alternatives to consider.
BSc Information Technology
Though an IT degree covers some of the same ground as a computer science one (especially when it comes to computer networks), you’ll trade theoretical knowledge for practical application. Expect to do a lot of work with databases and basic software, with some coding along the way.
BSc Data Science
As a more specialized course, a BSc in Data Science sees you delving deeper into the math and statistics behind computational systems. You’ll learn how to analyze data and may get a better grip on emerging tech, such as machine learning, than you would with a computer science degree.
Bachelor of Engineering (Computer Science)
A bachelor of engineering takes a more hardware-centric focus than a BSc, with this course teaching more about the principles of electrical engineering and how our computing devices actually work. There are still software components, and you’ll touch on similar subjects to a BSc, but you’ll get more practical experience with this course.
Is a BSc in Computer Science Good for You?
The most important question to ask isn’t “Is BSc Computer Science a good course,” but rather is it the right course for you? Your career goals, coupled with your desire (or lack thereof) to invest your time and money into the degree, may be the main deciding factors.
As with any course, ask yourself what the ultimate benefit is to you and weigh up your options (remembering that there are several types of computing degrees) to make the right choice.
Few computer science concepts have been as popular as artificial intelligence and machine learning. Traditionally reserved for sci-fi and fantasy, these disciplines have entered the real world and been eagerly welcomed by the public. Of course, tech companies and businesses across all industries were also quick to reap the benefits of AI and ML.
Today, the job market is full of offers for experts in the two fields. More importantly, plenty of those job listings come from leading companies, representing prime career opportunities. But tech giants want genuine experts – people thoroughly educated in the field.
Getting an MSc in AI and machine learning is an excellent way to gain the knowledge, experience, and proper credentials to land some of the most profitable and exciting jobs in the industry. The possibilities here are almost unlimited: You can enroll at a university for live classes or obtain your master’s degree in AI and machine learning online.
We’ve compiled a list of the best programs to get your masters in AI and ML. Let’s look at what the top educational institutions have to offer.
Factors to Consider when Choosing a Masters Program in AI and ML
Picking the best masters in machine learning and artificial intelligence isn’t a straightforward choice. Many institutions offer courses on the subject, but not all of them are of equal quality. Here are the essential criteria to consider when deciding which course to take:
- University reputation and ranking: The first factor to look at is whether the university is well-regarded among current and former students, as well as internationally. A reputable institution will usually meet other quality criteria as well.
- Curriculum and course offerings: Every masters in AI and ML program will be slightly different. You should examine the curriculum closely to find out if the classes match your educational and professional goals.
- Research opportunities and faculty expertise: There’s plenty of theory in AI and ML, but the core value of these disciplines lies in practical application. That’s why you’ll want to pick a program with ample research and hands-on opportunities. On a similar note, the faculty members should be industry experts who can explain and show real-life uses of the skills taught.
- Job placement and industry connections: Besides the knowledge, top MSc in AI and machine learning programs will provide access to industry networks and the relevant job market. This will be one of the greatest advantages of enrollment. You’ll get the chance to enter the AI and MS professional landscape upon graduation or, in some cases, during the program.
- Tuition fees and financial aid: Studying at top universities can be costly and may impact your budget severely. However, that doesn’t mean you can’t get quality education without breaking the bank. You can find reasonably priced offers or financial aid methods to help you along the way.
Top 5 Masters Programs in AI and ML
1. Imperial College London – MSc in Artificial Intelligence
The Imperial College in London offers intensive AI and programming training in this MSc program. During your studies, you’ll gain the essential and advanced technical skills, as well as experience in practical AI application.
This program lasts for one year and includes full-time studying on site in South Kensington. The total fee, expressed in British Pounds, is £21,000 for UK students and £39,400 for learners from abroad. To enroll, you’ll need to meet the minimum requirements of a degree in engineering, physics, mathematics, or similar fields.
In terms of the curriculum, this program’s core modules include Introduction to Machine Learning, Introduction to Symbolic Artificial Intelligence, and Python Programming. You’ll participate in individual and group projects and have access to state-of-the-art computing labs.
Certain projects are done in collaboration with leading AI companies, representing an excellent opportunity to get in touch with acclaimed tech professionals. As a result, graduates from this program have improved chances of finding high-level work in the industry.
2. University of Tuebingen – International Master’s Program in Machine Learning
The master’s in machine learning from the University of Tuebingen is a flexible program with particular emphasis on statistical ML and deep learning. The institution ensures the lectures follow the latest trends in the ever-developing machine learning field.
You can finish the studies during the four semesters of the program or take an extra semester. In that case, you’ll be eligible for a note of distinction, depending on the quality of your thesis. Non-EU students will need to pay a fee of €1,500 per semester along with a €160 semester fee. Students from the EU and others eligible for fee exceptions will only have to cover the semester fees.
As mentioned, the curriculum is exceptionally flexible. The program features only three mandatory lectures: Probabilistic Inference and Learning, Statistical Machine Learning, and Deep Learning. All other lectures are elective, so you can tailor the program to fit your needs and goals precisely.
The lecturers at Tuebingen University, all renowned machine learning researchers, will work with you actively during the program. Owing to the institution’s interdisciplinary approach, you’ll be able to work on your thesis under the supervision of any computer science professor, regardless of their particular field of expertise.
As a partner of the Max Planck Institute, this university regularly collaborates with world-class tech professionals and innovators. And as a student of the University of Tuebingen, you’ll have the chance to meet and work with those authorities. You can even write your thesis during an apprenticeship with a leading tech company.
3. University of Amsterdam – Master in Artificial Intelligence
The artificial intelligence MSc at the University of Amsterdam is among the most comprehensive programs worldwide. It’s designed to provide students with a broad scope of knowledge about AI and its practical application.
This is a full-time, regular program that lasts for two years and takes place in the university’s Science Park. The tuition fee for Dutch, Swiss, Surinamese, or EU students is €2,314, while other learners will need to pay €16,500. It’s worth mentioning that scholarships are available for all students.
For the first year, the curriculum includes seven core courses meant to establish a strong foundation in machine learning, computer vision, and NLP. The second year consists entirely of electives, both restricted and free-choice. Of course, you’ll wrap up the program with an AI thesis.
This artificial intelligence MSc program offers excellent career prospects. Many alumni have found work in distinguished positions at leading tech or tech-adjacent companies like Google, Eagle Vision, Airbnb, and Volvo.
4. Johns Hopkins University – Artificial Intelligence Master’s Program Online
As one of the leading educational centers in the world, Johns Hopkins University provides exceptional programs and courses in numerous areas. This online AI master’s program is no different. It will give you a solid understanding of the subject in theory and practice.
To earn this degree, you’ll need to pass 10 courses in the total period of five years. Since Johns Hopkins is a U.S. university, the tuition fees are expressed in dollars. The standard fee per course is $6,290. However, this program is a part of the university’s Engineering for Professionals division, and all courses in that division are subject to a special dean’s discount. The actual price you’ll pay, therefore, will be $5,090 per course or $50,900 in total.
The core courses you’ll take will include Introduction to Algorithms or Algorithms for Data Science, Applied Machine Learning, Artificial Intelligence, and Creating AI-Enabled Systems. The rest of the curriculum will consist of six electives – you’ll have 26 to choose from.
The faculty consists of acclaimed experts, and the university has close ties with industry-leading companies. Both of which will help you build your network and connect with professionals who may help advance your career.
5. KTH Sweden – MSc Machine Learning
Housed at the university’s campus in Stockholm, this MSc in machine learning program is a part of the KTHs School of Electrical Engineering and Computer Science. The program examines different facets of machine learning and how they apply to problem-solving in the real world.
The program is broken down into four semesters and lasts for two years total, if completed regularly. Swiss and EU students need not pay fees for program application or tuition. For other learners, the tuition fee for the whole program will be SEK 310,000, while the application fee is SEK 900.
The curriculum consists of mandatory and elective classes, with the electives being conditioned. For example, you’ll need to choose a minimum of six courses from the two groups of Theory and Application Domain.
KTH has an impressive percentage of graduates who found employment – 97%. Of those, half have assumed leadership positions, and one in 10 works in a managerial role. In fact, more than half of KHTs students start working in their respective industries before getting the degree. This serves as proof of the stellar reputation that KHT enjoys nation- and worldwide.
Become an Expert in the Leading Computer Science Disciplines
Getting a masters in AI and ML can help you find your place in these highly competitive industries. Of course, it will be necessary to find a program that suits you to maximize your chances of success.
Whichever program you choose, one thing is certain: Machine learning and artificial intelligence will continue to grow in importance. With a proper education, you’ll be able to keep up the pace and may find yourself among the experts leading the progress in these disciplines.
According to Data USA, degrees in the business field are among the most popular in the United States, with 840,116 degrees in this field alone being awarded in 2020. You went down the commerce route (meaning you have a grasp of business administration, accounting, and applied economics) and now you’re interested in practical applications of your knowledge.
With your commerce degree firmly under your belt, you may feel like a ship without a rudder – aimless and having no idea what direction to go. Happily, the tech field is ready and waiting for you, as a career in computer sciences may await. Here, we ask, “can a commerce student do BSc Computer Science?” The answer may surprise you, especially if you’re worried that a computer science degree’s eligibility requirements are outside the scope of what you learned in your commerce studies.
Background on Commerce and Computer Science
On the surface, commerce and computer science may seem like they go together as well as peanut butter and granite. But if you dig a little deeper into the scope of each subject, you start to realize that there’s more crossover than there first appears:
- Commerce – A degree in commerce gives you a firm grasp of the numbers that lie behind the scenes in a business, with banking, economics, and accounting all falling under your developing areas of expertise. Analytics is also a key part of these courses (especially in the research and data analyst fields), which is where we see some crossover with computer science.
- Computer Science – If commerce is all about the behind-the-scenes numbers in business, computer science handles what goes on under the hood in computing. Software development, data modeling, and analysis all fall under the computer science graduate’s remit, with the ability to pore through data to come to conclusions being essential to this technical subject.
It’s in the analysis that we start to see similarities between commerce and computer science emerge. Yes, commerce focuses more on the numbers behind businesses (and wider economic trends), but the ability to understand the data presented and report on what you see has applications in the computer science field. There’s not a direct crossover, as computer science will require you to learn the “language” in which computers speak, but they are many soft skills you develop in a commerce degree that apply to computer science.
Eligibility for BSc Computer Science
The key questions to ask when considering the issue of whether can commerce student do BSc Computer Science split into two categories:
- The general eligibility requirements to study a BSc in computer science
- Specific requirements that apply to commerce students
Eligibility Criteria for BSc Computer Science
BSc Computer Science degrees don’t require a great deal of computer know-how (though it helps), instead focusing on your grasp of mathematics. Requirements include the following:
- A high school diploma (or your country’s equivalent) that shows solid performance in mathematical subjects.
- Some degrees require you to achieve a specific Grade Point Average (GPA), though the specific GPA varies depending on where you apply.
- A high level of English proficiency, which can be measured using one (or both) of the following tests:
- IELTS – Get a minimum score between 6.0 and 7.0
- TOEFL – Get a minimum score between 90 and 100
Beyond these educational requirements, international students may need to submit copies of their passport and Visa, alongside certified academic transcripts to show they’ve achieved their country’s equivalents of the above grades. Not all courses require this of international students, with some online universities focusing more on your academic skills and less on your country of origin.
In terms of entrance exams, some colleges enforce computer science-specific exams (such as the CUET or CUCET), while others use NPATS or similar, more general exams, to determine proficiency.
Eligibility Criteria for Commerce Students
You may be standing at the starting line of your educational journey, meaning you’ve not yet applied to start your degree in commerce. First, congratulations on thinking so far ahead that you’re wondering “Can a commerce student do BSc Computer Science?” And second, you need to know what high school subjects help you get onto this degree path.
Commerce is a form of business degree, meaning any high school subjects that apply to the economic world help. Subjects like math, finance, economics, and foreign languages are obvious choices. The likes of marketing and computer applications also help (with the latter also laying some groundwork for your later computer science studies.
Much like computer science, you’ll likely have to take an entrance exam when applying to study commerce at most universities. The CSEET, CUET, and SET are common choices, with the first of these exams focusing specifically on those who study commerce to work as company secretaries.
The Possibility of Flexible Eligibility Criteria
Not all colleges require you to take entrance exams, with some even using broader strokes for their eligibility requirements to the point where they provide flexibility for both commerce and computer science students.
Colleges with open curriculums (such as Brown University and Hamilton College) offer more freedom in terms of what you study, with their entry requirements being more flexible as a result. Online institutions, such as the Open Institute of Technology (OPIT) may also offer more flexible entry criteria, sometimes allowing you to transfer credit from one course to another. That type of credit transfer may be ideal for you if you start a degree in commerce only to later decide to go down the computer science route.
Career Prospects for Commerce Students in Computer Science
When it comes to careers for those who hold computer science degrees, the obvious heavy-hitters are software and web development, IT management, and systems architecture. There are also exciting careers in the emerging AI fields that take full advantage of the technical skills you’ll develop as part of a BSc in computer science.
As for the career crossover between commerce and computer science, the key is to think about the skills that a commerce degree gives you that can apply in the computing field. Such skills include the following:
- Analytical Skills – Much like computer science, commerce is all about analyzing the data presented so you can report (and leverage) it for other purposes. Your ability to sit down and pore through the numbers will take you a long way in a computer-related role.
- Problem-Solving Skills – Closely linked to analytical skills, the ability to solve problems requires you to see the data at hand and come up with solutions while accounting for any restrictions presented. In creating commerce models, those restrictions may relate to budget and competencies, while computer science asks you to solve problems while taking system capabilities and limitations into account.
- Communication and Teamwork – Though often considered soft skills (as opposed to the “hard” technical skills you learn in a commerce degree), communication and teamwork are vital. If you need proof, try to work alone in any technical career and you’ll see why it’s so crucial to have these skills.
Potential Career Paths for Commerce Students with a BSc in Computer Science
With so much crossover potential between commerce and computer science, it’s clear that the answer to the question can a commerce student do BSc Computer Science is a resounding “yes.” And once you’ve completed your studies, several career paths await:
- Data Analyst – Reviewing data to find insights (be that into businesses or computer systems) are part of the remit for a data analyst. This role is all about problem-solving, which is a skill you’ll develop in abundance as a commerce and computer science student.
- Business Analyst – Take the ability to gather insights that is required of a data analyst and apply it specifically to areas of improvement in a business to become a business analyst. You’ll combine technical knowledge of a company’s inner workings with complex financial (and computational) models.
- IT Consultant – More computer science-centric than commerce-focused, IT consultants deal with the hows and whys of the computer networks businesses build. Your commerce skills will still come into play though, particularly when explaining how IT benefits businesses financially.
- Financial Technology Specialist – Combining the best of both worlds, this role combines the accounting skills you develop studying commerce with the technical ability needed to understand software and its functions.
Challenges and Considerations for Commerce Students
Though it’s possible for a commerce student to study (and succeed in) computer science, there are some challenges to consider.
The Technical Nature of Computer Science
As you learn the language of numbers in a commerce degree, so must you learn the language of machines when studying computer science. Getting to grips with the lingo (not to mention coding) can present a challenge to more business-minded students.
Balancing Your Workload
There’s an old saying that goes “Don’t burn the candle at both ends,” which is a warning not to pack too much onto your work plate. If you study commerce and computer science simultaneously, there’s a risk you may push yourself too far. Avoiding burnout requires finding the balance between your studies and personal time.
Networking and Practical Experience
As a commerce student, you understand that the world of business is as much about who you know as what you know. Finding the right people to take a chance on you, thus giving you practical experience, can be tough. But when armed with a pair of degrees in subjects that complement one another, you’re in a better position to build connections with people who can help you go far.
From Commerce to Computing – Is It Right for You?
So, can a commerce student do BSc Computer Science?
The answer isn’t just “yes,” but that it’s actually a great direction to go. Where a commerce degree equips you with a nice mix of technical knowledge and soft skills, a computer science course gives you even more practical knowledge that allows you to enter more specialized fields. However, your interest in each subject plays a role, as your ability (and passion) for studying hinges on your desire to dig into the more technical world of computing.
Assuming you have a genuine interest (and meet the appropriate eligibility criteria), supplementing your commerce studies with computer science can open up many career paths.
AI and machine learning are like an unstoppable tidal wave in today’s world. We’ve already seen the crest of that wave appear over the horizon with increased automation in businesses and the emergence of apps like ChatGPT. But in the coming years, the wave will engulf the world, making AI big business.
That’s supported by statistics from Statista, too, with reports that the AI market that was worth $200 billion (approx. €185 billion) in 2022 will be worth a staggering $2 trillion (approx. €1.85 trillion) in 2030. The point is that massive growth is coming in AI, and the right Master’s in AI is the key for you to be a part of that growth rather than getting stuck in an industry that gets consumed by it.
Top European Programs for Masters in AI and ML
In choosing the MSc artificial intelligence programs that appear on this list, we looked at factors ranging from the quality (and variety) of course content to who provides the degree. The three courses highlighted here are among Europe’s best to offer to European and overseas students.
Master in Artificial Intelligence (Universita di Bologna)
Though it’s held in Italy, this Master’s program is delivered in English as part of Universita di Bologna’s computer science program. It’s an on-campus course, meaning you’ll have to move to Bologna to attend.
The course provides a solid grounding in the foundations of AI over two years. You’ll get to grips with topics like machine learning and natural language processing, in addition to touching on the ethical and social issues that the rise of AI brings to the table.
The course is welcoming to international students, as it currently has a 77% ratio of international students who don’t come from Bologna. To apply, you must complete an application on the Studenti Online program, along with a mandatory form. Failure to follow this procedure leads to your application being discarded. Applicants don’t necessarily need to hold a Bachelor’s degree, though they must demonstrate a transcript of record that shows they have earned at least 150 ECTS or CFU credits in majors like computer science, mathematics, statistics, and physics.
The course page boasts that 90.5% of its 2021 graduates were happy with their degrees. It’s natural to assume most of these graduates leveraged their Master’s in artificial intelligence to move into careers in the field.
Master in Applied Data Science & AI (OPIT)
If you want to master artificial intelligence with a sprinkling of applying that mastery to the data science industry, OPIT’s course is right for you. It’s an 18-month course (though a 12-month fast-track version is available) that is fully online and delivers 90 ECTS credits. The first term covers the foundational aspects of AI, including subjects like machine learning and data science. But the second term stands out as it moves study from the theoretical to the practical by challenging you to solve real-world problems with your knowledge.
As an online program, it’s available to anybody anywhere, with entry requirements also being flexible. You’ll need a BSc degree, even one from a non-technical field, and should demonstrate English proficiency up to the B2 level with appropriate certification. Don’t worry if you don’t have an English language certification because OPIT offers its own that you can take before registering for the course.
Career-wise, the course is a good option because it occupies an interesting middle-ground between theory and practicality. The second term, in particular, equips you with skills that you can apply directly in fields as varied as IT business analysis, business intelligence, and data science.
MSc in Advanced Computer Science (University of Oxford)
Though it’s not marketed directly as a Master’s in machine learning and artificial intelligence, the University of Oxford’s program gives you excellent qualifications in both. It’s also delivered by an institution that EduRank names as the best for AI in the UK, and sixth-best in the world. The course examines advanced machine learning and computer security techniques, focusing on computational models and the algorithms behind them.
It’s a full-time program demanding 35 hours of weekly study, 15 of which you’ll spend on campus with the other 20 dedicated to self-study. It’s also a tough nut to crack for applicants, as the University of Oxford has a low 18% acceptance rate. You’ll need a first-class undergraduate degree with honors (or an equivalent) in mathematics or computer science to stand a chance of getting into one of the UK’s most prestigious universities.
Those tough entry requirements pay off later on, though, as the words “University of Oxford” on a CV immediately make employers stand up and pay attention. The wide-ranging approach of the program also means you’re not focusing solely on AI, opening up career opportunities in other fields related to math and statistical analysis.
Data Science Master – Europe’s Best Options
Data science is an industry that requires you to translate your understanding of algorithmic theory to transform complex data sets into actionable insights. It’s also an industry that’s making increasingly heavy use of AI tools, making a Master’s in data science a great companion (or alternative) to the best artificial intelligence Master in Europe. As you noticed above, OPIT’s MSc AI program includes elements of data science, though the two programs here (covered in brief) are excellent choices as standalone programs.
MSc Data Sciences and Business Analytics (Essec Business School)
This hybrid course lasts for either one or two years, depending on your background, and focuses on the application of data sciences in a business context. It’s also ranked as the fourth-best Master’s in business analytics in the world by QS World University Rankings.
That high ranking is backed up by the university’s own statistics, which state that over half of its students get jobs before they even complete the course. Essec has a 100% career success rate for graduates in less than six months from completion of the Master’s, making this a great choice for career-focused students. Google, Amazon, JP Morgan Chase, and PwC count as some of the top recruiters that keep their eye on graduates from this program.
Admission requires a degree in a related technical subject, such as engineering, science, or business, from a leading university. That degree also impacts the version of the program you take, as a three-year BSc means you take the two-year Master’s, while those who have a four-year BSc under their belts take the one-year version, assuming they meet other requirements.
Data Science, Technology, and Innovation (University of Edinburgh)
With over 13,000 international students, the University of Edinburgh welcomes overseas students who want to expand their knowledge. Its MSc data science program is no different, buoyed by the fact that it’s an online course that doesn’t require you to move to the less-than-sunny climate of Edinburgh.
It’s a part-time program that relies on self-study, though it provides you with plenty of interactive resources to help along the way. The program is something of an umbrella course as it focuses on equipping students with the knowledge they need to enter the data science field across industries as diverse as medicine, science, and even the arts.
You’ll need the equivalent of an Upper Second-Class Honors degree that has elements of programming before applying. Ideally, you’ll also have evidence of mathematical skill, either through taking math classes in your undergraduate studies or by demonstrating the equivalent of an English A-Level in math through other qualifications.
Factors to Consider When Choosing an Artificial Intelligence Master’s
The five programs highlighted here all help you master artificial intelligence, with many also providing a practical grounding that puts you in good stead for your future career. But if you want to do more research (and that’s always a good idea), the following factors should be on your mind when checking other programs:
- Course Curriculum – The content of your course impacts what you can do once you have your MSc under your belt. Focus on programs that teach tangible skills applicable to the field you wish to enter.
- Faculty – Always check the credentials of the program’s creators and administrators, particularly in terms of industry experience, to confirm they have the relevant tools.
- Tuition and Financial Aid – Master’s programs aren’t cheap (you’ll pay several thousand euros for even an online course), so check you can budget accordingly for the program. Many universities offer financial aid options, from scholarships to student loans, that can help in this area.
- Location – The location isn’t really an issue if you take an online course, but it impacts your decision if you decide to study on-campus. Remember that you’ll spend at least a year of your life on the course (often two years) so you need to gel well with the place in which you’ll live.
- Networking and Industry – Does the course provider have connections to major industry players? Does it offer career advice, ideally via a specialized office or program? These are the types of questions to ask when assessing a university’s capacity for networking and career advancement.
Become a Master in Artificial Intelligence
A Master’s degree in artificial intelligence is your entry point into a growing industry that’s already on the verge of taking the world by storm. That is, assuming you choose the right program. The five highlighted here all land in the “right program” category by virtue of the tuition you receive, the reputation of the institution, and their accessibility to European and overseas students.
Research each program (and any others you consider) extensively before making a choice. Remember that it’s not always about the course or its reputation – it’s about how the course helps you achieve the specific learning goals you need to achieve to get ahead in your chosen career.