Let’s be honest, today’s competition in business is mainly centered on decisions. The faster and smarter those are, the more successful your company is. Decisions, in turn, are always backed up by data. Traditionally, data science is associated with Python. But Java, too, has a lot to offer in terms of data science and machine learning.
Why Java for Data Science?
The first and perhaps the most obvious question is why choose java for data science? Indeed, Java isn’t the first language that comes to mind when thinking of data analysis. Yet, its characteristics may be pretty beneficial in specific scenarios.
- Performance and Scalability
To begin with, Java’s Just-In-Time (JIT) compiler ensures that high-performance code is generated as needed. This makes this language suitable for large-scale data processing. For example, high-frequency trading systems would normally favor Java because it can handle immense throughput and low-latency processing.
- Robust Ecosystem
Besides, Java is a mature ecosystem. It includes extensive libraries, frameworks, and tools. It’s been in enterprise environments for a while and integrates seamlessly with legacy systems. Needless to say, it’s a huge plus for sectors like banking and telecommunications.
- Security and Reliability
Finally, Java is safe. It places a strong emphasis on security features and a managed memory environment. The latter is of great help in avoiding common bugs and memory leaks. If your business deals with sensitive financial data or health records, this factor is doubly significant.
The Importance of Hiring Dedicated Java Developers for Data Science
Of course, you can fully benefit from what this language has to offer only if you have a professional who knows how to make the most of it. That’s why many businesses today hire dedicated java programmers for data science projects. Such developers know how to integrate complex data systems and mesh new solutions with existing ones. What’s important is that they can do so without disrupting the current operations.
For instance, you can hire Java developers to integrate predictive analytics models into customer relationship management (CRM) systems. When the job is done, both user experience and operational efficiency will improve. And if you hire specialists knowledgeable in handling machine learning with java, they can help you address some unique challenges like predictive maintenance in manufacturing.
Java Libraries for Data Science
Let’s say you are eager to use java data analysis opportunities to the fullest. What resources are available to you or to the professionals you hire in this regard?
Weka
Weka is a library mainly used for data mining. It contains tools for
- data preparation
- classification
- regression
- clustering
- association rules
- visualization.
As you may have already guessed, it is ideal for developing new ML schemes. For example, they now use Weka in agricultural data analysis.
Deeplearning4j (DL4J)
DL4J is another popular library in Java and Scala. It is integrated with Hadoop and Apache Spark and designed to be used in business environments on distributed GPUs and CPUs. It supports all major deep learning models. Its most common use cases include
- image recognition
- fraud detection
- text mining.
Mahout
Apache Mahout’s core focuses are
- collaborative filtering
- clustering
- classification.
This library is widely applicable in systems that recommend products based on user interaction patterns.
Statistical Machine Intelligence and Learning Engine (SMILE)
Smile is a fast and comprehensive ML engine. It provides a wide range of algorithms for data science, including
- classification
- regression
- clustering
- association rule mining,
- visualization.
An example use case of Smile is in health informatics. Here, it analyzes large datasets of patient records to predict health outcomes.
JFreeChart
And then, there is also JFreeChart which is superb for informative customizable visual representations. It supports lots of chart types including
- pie charts
- line charts
- bar charts
- time series charts.
Quite predictably, JFreeChart is extensively used in financial and marketing domains.
Java vs Python for Data Science
The main question of all is, of course, whether Java can be, indeed, preferred to Python for data science or ML purposes. As you’ll see below, it all depends on the project requirements here.
- Performance and Speed
Here, Java typically outperforms Python. It’s mainly thanks to its statically typed nature and the efficiency of the JVM. That’s why they often choose it for large-scale and real-time data processing applications. For instance, LinkedIn’s real-time services are built on Java.
- Ease of Use and Learning Curve
In this area, Python leads. It has a user-friendly syntax that allows writing significantly less code than Java to perform the same functions. That’s why they use Python in data science education and small-scale projects.
- Library Ecosystem
We’ve already reviewed Java’s libraries but Python’s data science ecosystem is still unmatched. Take NumPy for numerical computations, pandas for data manipulation, and TensorFlow for machine learning, for example. Let’s be honest, while Java’s ecosystem is robust, it mainly focuses on large-scale system integration and backend processing rather than on data science.
- Community and Support
Python is the leader again. It boasts a huge data science community and the Python Package Index (PyPI) hosts numerous packages. Java’s community is also large, of course, but it is more geared towards system architecture and enterprise environments.
- Interoperability and Integration
Java is absolutely the best here. The good evidence of this is that they actively use it in finance, healthcare, and manufacturing.
Overall, Python is better for speed and ease of use, while Java is better for scalability, and integration.
Final Thoughts
All in all, Python remains a popular choice for data science. It’s simple and has rich libraries. Yet, as you’ve just seen, Java, too, has some cool things to offer (e.g., speed). As always, your choice depends on your unique requirements. If you work with professional developers, they’d normally advise you which of the two options fits best.