Java

The classic Java vs Python debate is almost like the programming world’s version of “tabs vs spaces” or “light mode vs dark mode.” As you step into the world of coding, you will come across passionate discussions and heated arguments about which language reigns supreme in the programming world!

Choosing between Java and Python is like choosing between a structured classroom lecture and an interactive online course; both will teach you a lot, but the experience is completely different. However, the best choice depends on what you want to build, how fast you want to develop, and where you see your career heading.

If you’re a beginner, this decision shapes your learning curve. If you’re a developer, it influences the projects you work on. And if you’re a business owner, it affects the technology driving your product. So, which one should you go for?

In this blog, we will break down the key differences so you can make an informed choice and take the first step toward your programming future. Let’s dive in!

 

 

Overview of Java and Python

Before we dive into the nitty-gritty details, let’s take a step back and get to know our two contenders. Both languages have stood the test of time, but they serve different purposes and cater to different coding styles. Let’s explore what makes each of them unique.

What is Java?

Java came to life in 1995, thanks to James Gosling and his team at Sun Microsystems. Originally intended for interactive television, it quickly found a much bigger role in enterprise applications, backend systems, and Android development.

Over the years, Java has grown and adapted, but its core values – reliability, portability, and security – have stayed rock solid. It is an object-oriented, statically typed, compiled language that requires variable types to be defined upfront, and translates code into an efficient, executable format.

One of Java’s biggest superpowers is its “Write Once, Run Anywhere” (WORA) capability. Since it runs on the Java Virtual Machine (JVM), the same code can work on any device, operating system, or platform without modifications.

What is Python?

Python came into existence in 1991 by Guido van Rossum with a simple goal: to make programming more accessible and enjoyable.

Fun fact: The language is named after the comedy group Monty Python’s Flying Circus and not the snake!

This playful spirit is reflected in Python’s clean, minimalistic syntax, making it one of the easiest languages to learn. It is an interpreted, dynamically typed language that executes the code line by line and does not require you to declare variable types explicitly.

The simplicity and readability of the language truly set it apart. This makes Python a favorite for both beginners getting started and experienced developers who want to move fast.

 

Here’s a list of top Python libraries for data science

 

Compiled vs. Interpreted Languages: How Java and Python Execute Code?

Ever wondered why Java applications tend to run faster than Python scripts? Or why Python lets you test code instantly without compiling? It all comes down to how these languages are executed.

Programming languages generally fall into two categories – compiled and interpreted. This distinction affects everything from performance and debugging to how easily your code runs across different systems. Let’s break it down!

What is a Compiled Language?

A compiled language takes your entire code and converts it into machine code (binary) before running the program. This process is handled by a compiler, which generates an independent executable file (like .exe or .class).

Once compiled, the program can run directly on the computer’s hardware without needing the original source code. Think of it like translating a book where, instead of translating each page as you read, you translate the whole thing first, so you can read it smoothly later. This ensures:

• Faster execution – Since the code is pre-compiled, the program runs much more efficiently
• Optimized performance – The compiler fine-tunes the code before execution, making better use of system resources
• Less flexibility for quick edits – Any changes require a full recompilation, which can slow down development

Common examples of compiled languages include C, C++, and Java. These languages prioritize speed and efficiency, making them ideal for performance-intensive applications.

What is an Interpreted Language?

Unlike compiled languages that translate code all at once, interpreted languages work in real time, executing line by line as the program runs. Instead of a compiler, they rely on an interpreter, which reads and processes each instruction on the fly.

Think of it like a live translator at an international conference where, instead of translating an entire speech beforehand, the interpreter delivers each sentence as it is spoken. This offers:

• Instant execution – No need to compile; just write your code and run it immediately
• Easier debugging – If something breaks, the interpreter stops at that line, making it simpler to track errors
• Slower performance – Since the code is being processed line by line, it runs slower compared to compiled programs

It includes examples like Python, JavaScript, PHP, and Ruby. These languages are all about convenience and quick iteration, making them perfect for developers who want to write, test, and modify code on the go.

 

 

How Java and Python Handle Execution?

Now that we know the difference between compiled and interpreted languages, let’s see where Java and Python fit in.

Java: A Hybrid Approach

Java takes a middle-ground approach that is not fully compiled like C++, nor fully interpreted like Python. Instead, it follows a two-step execution process:

1. Compiles to Bytecode – Java code is first converted into an intermediate form called bytecode
2. Runs on the Java Virtual Machine (JVM) – The bytecode is not executed directly by the computer but runs on the JVM, making Java platform-independent

To boost performance, Java also uses Just-In-Time (JIT) compilation, which converts bytecode into native machine code at runtime, improving speed without losing flexibility.

Python: Fully Interpreted

Python, on the other hand, sticks to a purely interpreted approach. Key steps of Python execution include:

1. Compiling to Bytecode: Java code is first compiled into an intermediate form called bytecode (.class files)
2. Running on the JVM: This bytecode is not executed directly by the system but runs on the Java Virtual Machine (JVM), making Java platform-independent
3. JIT Compilation for Speed: Java uses Just-In-Time (JIT) compilation, which converts bytecode into native machine code at runtime, optimizing performance
4. Python Interpreter: It reads and executes code line by line, skipping the need for compilation

This makes Python slower in execution compared to Java, but much faster for development and debugging, since you do not need to compile every change.

 

Explore the NLP techniques and tasks to implement using Python

 

While understanding how Java and Python execute code gives us a solid foundation, there is more to this debate than just compilation vs. interpretation. These two languages have key differences that shape how developers use them. Let’s dive deeper into the major differences between Java and Python and see which one fits your needs best!

Java vs Python: Key Differences Every Developer Should Know

Now that we’ve explored how Java and Python execute code, let’s dive into the key differences that set them apart. Whether you’re choosing a language for your next project or just curious about how they compare, understanding these aspects will help you make an informed decision.

 

 

1. Syntax & Readability

One of the biggest differences between Java and Python is their syntax. Let’s understand this difference with an example of printing “Hello, World!” in both languages.

Python is known for its clean, simple, and English-like syntax. It focuses on readability, reducing the need for extra symbols like semicolons or curly braces. As a result, Python code is often shorter and easier to write, making it a great choice for beginners.

You can print “Hello, World!” in Python using the following code:

 

Java, on the other hand, is more structured and verbose. It follows a strict syntax that requires explicit declarations, semicolons, and curly braces. While this adds some complexity, it also enforces consistency, which is beneficial for large-scale applications.

In Java, the same output can be printed using the code below:

 

As you can see, Python gets straight to the point, while Java requires more structure.

2. Speed & Performance

Performance is another key factor when comparing Java vs Python.

Java is generally faster because it uses Just-In-Time (JIT) compilation, which compiles bytecode into native machine code at runtime, improving execution speed. Java is often used for high-performance applications like enterprise software, banking systems, and Android apps.

Python is slower since it executes code line by line. However, performance can be improved with optimized implementations like PyPy or by using external libraries written in C (e.g., NumPy for numerical computations). Python is still fast enough for most applications, especially in AI, data science, and web development.

 

Here’s a list of top Python packages you must explore

 

3. Typing System (Static vs. Dynamic)

Both programming languages also differ in ways they handle data types. This difference can be highlighted in the way a variable is declared in both languages.

Java is statically typed – You must declare variable types before using them. This helps catch errors early and makes the code more predictable, but requires extra effort when coding. This static typing makes it more reliable, helps prevent errors, but requires more code. For instance:

 

Python is dynamically typed – Variables do not require explicit type declarations, making development faster. While this can lead to unexpected errors at runtime, it also makes the language faster to write and more flexible. Such a variable declaration in Python will look like:

 

4. Memory Management & Garbage Collection

Both Java and Python automatically manage memory, but they do it differently. Let’s take a closer look at how each programming language gets it done.

Java uses automatic garbage collection via the Java Virtual Machine (JVM), which efficiently handles memory allocation and cleanup. Its garbage collector runs in the background, optimizing performance without manual intervention. Hence, it is more optimized to handle large-scale applications.

Python also has garbage collection, but it mainly relies on reference counting. When an object’s reference count drops to zero, it is removed from memory. However, Python’s memory management can sometimes lead to inefficiencies, especially in large applications.

5. Concurrency & Multithreading

Similarly, when it comes to multithreading and parallel execution, both Java and Python handle it differently.

Java excels in multithreading. Thanks to its built-in support for threads, Java allows true parallel execution, making it ideal for applications requiring high-performance processing, like gaming engines or financial software.

Python, on the other hand, faces limitations due to the Global Interpreter Lock (GIL). The GIL prevents multiple threads from executing Python bytecode simultaneously, which limits true parallelism. However, it supports multiprocessing, helping bypass the GIL for CPU-intensive tasks.

 

You can also learn to build a recommendation system using Python

 

Thus, when it comes to Java vs Python, there is no one-size-fits-all answer. If you need speed, performance, and scalability, Java is the way to go. If you prioritize simplicity, rapid development, and flexibility, Python is your best bet.

Java vs Python: Which One to Use for Your Next Project?

Now that we’ve explored the key differences between Java and Python, the next big question is: Which one should you use for your next project?

To answer this question, you must understand where each of these language excel. While both languages have carved out their own niches in the tech world, let’s break it down further for better understanding.

 

 

Where to Use Java?

Java’s reliability, speed, and scalability make it a top choice for several critical applications. A few key ones are discussed below:

• Enterprise Applications (Banking, Finance, and More)

Java has long been the backbone of banking and financial applications, as they need secure, fast, and highly scalable systems. Many large enterprises rely on Java frameworks like Spring and Hibernate to build and maintain their financial software. For instance, global banks like Citibank and JPMorgan Chase use Java for their core banking applications.

• Android Development

While Kotlin has gained traction in recent years, Java is still widely used for Android app development. Since Android apps run on the Dalvik Virtual Machine (DVM), which is similar to the Java Virtual Machine (JVM), Java remains a go-to language for Android developers. Popular Android apps built using Java include Spotify and Twitter.

• Large-Scale Backend Systems

Java’s robust ecosystem makes it ideal for handling complex backend systems. Frameworks like Spring Boot and Hibernate help developers build secure, scalable, and high-performance backend services. Even today, E-commerce giants like Amazon and eBay rely on Java for their backend operations.

• High-Performance Applications

Java is a compiled language with Just-In-Time (JIT) compilation, performing better in compute-intensive applications compared to interpreted languages like Python. This makes it ideal for applications that require fast execution, low latency, and high reliability, like stock trading platforms and high-frequency trading (HFT) systems.

When to Choose Python?

Meanwhile, Python’s flexibility, simplicity, and powerful libraries make it the preferred choice for data-driven applications, web development, and automation. Let’s look closer at the preferred use cases for the programming language.

• Data Science, AI, and Machine Learning

Python has become the best choice for AI and machine learning. With libraries like TensorFlow, PyTorch, NumPy, and Pandas, Python makes it incredibly easy to develop and deploy data science and AI models. Google, Netflix, and Tesla use Python for AI-driven recommendations, data analytics, and self-driving car software.

 

Learn to build AI-based chatbots using Python

 

• Web Development (Django, Flask)

Python’s simplicity and rapid development capabilities make it suitable for web development. Frameworks like Django and Flask allow developers to build secure, scalable web applications quickly. For instance, websites like Instagram and Pinterest are built using Python and Django.

• Automation and Scripting

Automation is one of the strengths of Python, making it a top choice for data scraping, server management, or workflow automation. Python can save hours of manual work with just a few lines of code. Its common use is in companies like Reddit and NASA for automating tasks like data analysis and infrastructure management.

• Cybersecurity and Penetration Testing

Python is widely used in ethical hacking and cybersecurity due to its ability to automate security testing, develop scripts for vulnerability scanning, and perform network penetration testing. Security professionals use Python to identify system weaknesses and secure networks. Popular security tools like Metasploit and Scapy are built using Python.

 

You can also learn about Python in data science.

 

To sum it up:

• Java for large-scale enterprise applications, Android development, or performance-heavy systems
• Python for AI, data science, web development, or automation

And if you still cannot make up your mind, you can always learn both languages!

 

 

Java or Python? Making the Right Choice for Your Future

Both languages are in high demand, with Python leading in AI and automation and Java dominating enterprise and backend systems. No matter which one you choose, you’ll be investing in a skill that opens doors to exciting career opportunities in the ever-evolving tech world.

The best language for you depends on where you want to take your career. Since both are the best choices in their domains, whether you choose Python’s flexibility or Java’s robustness, you will be setting yourself up for a thriving tech career!

Data science tools are becoming increasingly popular as the demand for data scientists increases. However, with so many different tools, knowing which ones to learn can be challenging

In this blog post, we will discuss the top 7 data science tools that you must learn. These tools will help you analyze and understand data better, which is essential for any data scientist.

So, without further ado, let’s get started!

List of 7 data science tools 

There are many tools a data scientist must learn, but these are the top 7:

• Python
• R Programming
• SQL
• Java
• Apache Spark
• Tensorflow
• Git

And now, let me share about each of them in greater detail!

1. Python

Python is a popular programming language that is widely used in data science. It is easy to learn and has many libraries that can be used to analyze data, machine learning, and deep learning.

It has many features that make it attractive for data science: An intuitive syntax, rich libraries, and an active community.

Python is also one of the most popular languages on GitHub, a platform where developers share their code.

Therefore, if you want to learn data science, you must learn Python!

There are several ways you can learn Python:

• Take an online course: There are many online courses that you can take to learn Python. I recommend taking several introductory courses to familiarize yourself with the basic concepts.

 

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your deep learning skills.

 

• Read a book: You can also pick up a guidebook to learning data science. They’re usually highly condensed with all the information you need to get started with Python programming.
• Join a Boot Camp: Boot camps are intense, immersive programs that will teach you Python in a short amount of time.

 

Whichever way you learn Python, make sure you make an effort to master the language. It will be one of the essential tools for your data science career.

2. R Programming

R is another popular programming language that is highly used among statisticians and data scientists. They typically use R for statistical analysis, data visualization, and machine learning.

R has many features that make it attractive for data science:

• A wide range of packages
• An active community
• Great tools for data visualization (ggplot2)

These features make it perfect for scientific research!

In my experience with using R as a healthcare data analyst and data scientist, I enjoyed using packages like ggplot2 and tidyverse to work on healthcare and biological data too!

If you’re going to learn data science with a strong focus on statistics, then you need to learn R.

To learn R, consider working on a data mining project or taking a certificate in data analytics.

 

3. SQL

SQL (Structured Query Language) is a database query language used to store, manipulate, and retrieve data from data sources. It is an essential tool for data scientists because it allows them to work with databases.

SQL has many features that make it attractive for data science: it is easy to learn, can be used to query large databases, and is widely used in industry.

If you want to learn data science involving big data sets, then you need to learn SQL. SQL is also commonly used among data analysts if that’s a career you’re also considering exploring.

There are several ways you can learn SQL:

• Take an online course: There are plenty of SQL courses online. I’d pick one or two of them to start with
• Work on a simple SQL project
• Watch YouTube tutorials
• Do SQL coding questions

 

4. Java

Java is another programming language to learn as a data scientist. Java can be used for data processing, analysis, and NLP (Natural Language Processing).

Java has many features that make it attractive for data science: it is easy to learn, can be used to develop scalable applications, and has a wide range of frameworks commonly used in data science. Some popular frameworks include Hadoop and Kafka.

There are several ways you can learn Java:

• Work on a project
• Practice using programming exercises

 

5. Apache Spark

Apache Spark is a powerful big data processing tool that is used for data analysis, machine learning, and streaming. It is an open-source project that was originally developed at UC Berkeley’s AMPLab.

Apache Spark is known for its uses in large-scale data analytics, where data scientists can run machine learning on single-node clusters and machines.

Spark has many features made for data science:

• It can process large datasets quickly
• It supports multiple programming languages
• It has high scalability
• It has a wide range of libraries

If you want to learn big data science, then Apache Spark is a must-learn. Consider taking an online course or watching a webinar on big data to get started.

 

6. Tensorflow

TensorFlow is a powerful toolkit for machine learning developed by Google. It allows you to build and train complex models quickly.

Some ways TensorFlow is useful for data science:

• Provides a platform for data automation
• Model monitoring
• Model training

Many data scientists use TensorFlow with Python to develop machine learning models. TensorFlow helps them to build complex models quickly and easily.

If you’re interested to learn TensorFlow, do consider these ways:

• Read the official documentation
• Complete online courses
• Attend a TensorFlow meetup

However, to learn and practice your Tensorflow skills, you’ll need to pick up decent deep learning hardware to support the running of your algorithms.

 

7. Git

Git is a version control system used to track code changes. It is an essential tool for data scientists because it allows them to work on projects collaboratively and keep track of their work.

Git is useful in data science for:

• Tracking changes in code
• Allowing collaboration on coding projects
• Keeping track of work

If you’re planning to enter data science, Git is a must-know tool! Since you’ll be coding a lot in Python/R/Java, you’ll want to master Git to work with your team well in a collaborative coding environment.

Git is also an essential part of using GitHub, a code repository platform used by many data scientists.

To learn Git, I’d recommend just watching simple tutorials on YouTube.

Final thoughts

And these are the top seven data science tools that you must learn!

The most important thing is to get started and keep upskilling yourself! There is no one-size-fits-all solution in data science, so find the tools that work best for you and your team and start learning.

I hope this blog post has been helpful in your journey to becoming a data scientist. Happy learning!

 

Written by Austin Chia

GitHub repositories are great for finding new solutions and keeping up with open source projects. Take a look at some of the most popular repositories.

If you didn’t watch the video because you’re in a public setting and don’t have headphones, the video essentially says GitHub is an open source software development community for teams of and individual developers to work on projects. GitHub gives you a platform to copy projects, track changes, and so much more.

What brings me to share the trending GitHub repositories for this month,  and the months to come, are the ideas a community of people has. Like the introduction video says, an idea/implementation for one product can bring up new solutions for another. We should always be aware of products and solutions in the community because they might help us in the future.

Trending GitHub repositories

With that, here are the top trending GitHub repositories users are most excited about for October.

1) iptv-org/ iptv

This project allows you to watch IPTV (Internal Protocol Television) channels from around the world. There are currently a collection of 8,000+ channels available to the public.

2) bloc97/ Anime4K

Anime4K is used as a “real-time upscaling for anime video”. It’s completely open-sourced, and far from perfect. Currently, it claims to be the “fastest at achieving reasonable quality” for your anime 4K upscaling. The project is looking at taking a hybrid approach with machine learning to improve results.

3) axi0mX/ ipwndfu

IPWNDFU is a jail-breaking tool for many iOS devices. “Jail-breaking” is the term used when someone wants to free their device (typically mobile) of restrictions given by the operator or manufacturer. It allows the user to download software previously unauthorized. This project is BETA software so you will need to be careful if you use it.

4) vlang/ v

Version 1.0 of the V Programming Language hasn’t been released yet, but I imagine it will be a popular repository to follow for the next few months. Expected in December 2019, V is a simple language for developing maintainable software. The language can reportedly compile itself in less than 1 second with zero dependencies.

5) geek computer/ Python

The Python project is a list of Python examples created by the project managers to share their experience with the programming language. If you want to advance your skills with Python, take a look at this repository.

6) TheAlgorithms/ Java

TheAlgorithms makes open source resources for learning data structures and algorithms with their implementation in any programming language. Java is their project for implementing all algorithms in Java.

Check out the post I co-authored covering 101 machine learning algorithms.

7) home-assistant/ home-assistant

“Home Assistant is a home automation platform running on Python 3. It can track and control all devices at home and offer a platform for automating control.”

8) robbyrussell/ oh-my-zsh

Zsh is a powerful scripting tool to help make you feel like a 10x developer. In this community of 1300+ contributors, you can have a framework for managing your zsh configuration.

9) mitesh77/ Best-Flutter-UI-Templates

Flutter Dart is an open-source software used to develop applications. In this instance, it’s being used to make available templates to build your app.

10) gatsbyjs/ gatsby

Like Flutter, Gatsby is used to helping developers “build blazing fast websites and apps”. Gatsby allows you to create dynamic pages, pull data from just about any data source, and automates many activities such as lazy-loading, image optimization, and inlining critical styles.

Over the years, the popularity of different programming languages has been increasing. This blog lists down some of the top & most useful programming languages for you to learn.

The use of programming languages ​​remains a popular way of earning money and the main tool for creating modern technologies. Even if you start studying some of these programming languages right now, you can be sure of high wages and rapid career growth.

The more programming languages ​​you know and use, the higher your status will be. For the development of one application/technology, several of them can be used at once. Since the inception of the first computers, more than 8,000 programming languages have been invented. There are basic ones that are used everywhere. It is impossible to single out the best one, each has advantages and disadvantages.

Below is the list to get you acquainted with the best programming languages ​​and find out which one to start with:

Python 

Python is a simple programming language that is suitable for beginners and will be a relatively easy way to get into a new profession. A clear code, a large library of tools, and a minimum of tricks allow you to quickly get the hang of it, making the language the most popular in education and also helping you to learn data science. Although learning Python for Data Science is not an easy task, there is a lot of training out there that can help one get started.  It is not for nothing called “language with batteries included”, it itself provides methods for solving basic problems. It is easy to integrate with C and C ++ languages.

Python’s performance is inferior to other languages, but because of this, it does not lose its relevance. Scientists all over the world use it for machine learning. Plus, it’s ideal for web services; backend, and sysadmin.

C & C++

The C language appeared in 1975, and its more multifaceted extension C ++, in 1985. They are the progenitors of most programming languages. Every 3 years the C ++ language is updated, and today there is already the 20th ISO standard. Initially, the C language was developed for less powerful computers, was economical, and more tied to the hardware. This binding remains today, which allows you to “squeeze” the maximum out of productivity. Now the language is used both for game development and for machines with a low-power processor.

These complex languages ​​are not the most fun places to learn programming. When studying, you can quickly burn out and say goodbye to the profession. However, it is C ++ that will help to fully probe the “brain” of the computer, which is extremely important for the programmer. This hard start is suitable for those who want to understand the basics. C ++ does not support validation at the time of writing the code, which also complicates the development work.

That is why these specialists are in great demand.

.NET

.NET is a framework from Microsoft that allows you to use the same namespaces, libraries, and APIs for different languages. .NET supports several languages: along with C#, it also includes VB.NET, C ++, F #, as well as various dialects of other languages ​​tied to .NET.

.NET is fairly widespread in the development of in-house software products, but it is still relatively rare in web development, like other software products from Microsoft. Therefore, finding .NET developers for a web project can be quite difficult. The use of .NET usually  “pulls” the purchase of other software from Microsoft. However, if you are looking for a promising direction, this is a great option.

JavaScript

Created in 1995, JavaScript is the dominant frontend language around the world. Its relevance is not lost even for a minute, it can be used to create interactive websites. It is also easy to learn, but today it is not enough for development, and the number and quality of frameworks can become difficult. That is why you should not start with it, because constant retraining for the changing frontend, by virtue of already active programmers.

Thanks to a large number of add-ons, the functionality of JavaScript is limitless. The main disadvantage is that due to the fact that the language is used to encode pop-ups, we often have to deal with malicious content.

Java

Java was also introduced in 1995 but has nothing to do with JavaScript. It is in demand in the backend and occupies a good position in it. Death is predicted for the language every year, but it seems that this will not happen soon. Behind the “boring and verbose” structure, many find the perfect solution to many problems. For example, it is used by banking structures to write mobile applications for large companies.

The main advantage is that the developed application will run on any platform that supports Java due to the weak link system. That is, after the initial creation, there is no need to modify the application specifically for each server.

The disadvantages of the language include additional payment for the licensed version of the Java Development Kit, and it is not suitable for applications in the cloud.

Learning Java first is not worth it, it is the perfect complement to other more fundamental languages.

Swift

Created by Apple in 2014, Swift has grown exponentially in popularity. The creators positioned it as a replacement for Objective-C and the beginning of a “new era” of programming. But so far it is in demand for only iOS applications.

The language is perfectly adapted for both custom and server-side development. The syntax is easy to read and the code runs quickly.

It’s only worth learning if you’re going to develop apps for Apple products.

But even for iOS, it does not always work. Since the language is new, it is used to write applications for at least the seventh generation of iOS. In addition, Swift still has many shortcomings, it is unstable and has a small number of third-party resources to work with.

Conclusion

A number of programmers build their careers with professional knowledge in only one programming language, but they are more proficient in several of them at once, which significantly increases their chances of succeeding in their careers. It is difficult to say which of them should be studied. For example, if you want to work in a large company, it is better to learn C and Java, Python and JavaScript are suitable for participation in web startups, and for iOS mobile applications, it is enough to have knowledge of Swift.