The document discusses the significance of software development in data science, emphasizing the importance of choosing between Java and Python for this purpose. It compares both languages across various factors like syntax, performance, and available libraries, highlighting that while Java excels in performance, Python offers more flexibility and ease of learning. Both programming languages have robust libraries suited for data science, making either a viable option depending on specific business needs.

1. Why would anyone ever need to develop software in the data science category? If truth be
told, data science is a large field of technology. It deals with processing raw data that
renders valuable information.
The need for data science software is undeniable. But the question, of whether you
should enlist Java software development services or Python software development
services for big data remains valid.
What Is Data Science?
Data science simply is the extraction of valuable data using scientific means. This involves
using algorithms, processes, and methods, to process structured or unstructured data.
This raw data would otherwise be difficult to sort through without such software.
For every business, data science is an important activity. This filtration of data can be
beneficial for business operations in many ways.
Benefits of data science for a business are:
Enhances business predictability
Aids the marketing and sales departments
Improves security of data
Facilitates conversion of complex data
Assists in decision-making
Now that we have highlighted the importance of this software, let’s help you decide if you
should hire Java programmers.
Java vs Python
Picking out the most ideal technology for the development of applications significant for
enterprise establishments is not a light decision! Choosing the wrong one will render your
application useless. Not all programming languages come with suitable libraries and
features!
Two options that tend to stand out from the plethora of technologies are Java and Python.
Immediately a question comes to mind. Java vs Python, which is better? What about these
two programming languages catches the attention of businesses who need data science
solutions?
Let’s take a deeper look at how exactly Java and Python can be used in data science and
more particularly which is better Java or Python. We will start by comparing a few points
of both technologies.
Differences Between Java and Python
Java and Python have been around for a long time. Extensive efforts by the developing
community have fortified both of them as reliable software development languages.
The truth is, the differences between Java and Python are numerous! Here is a
comparison between Java and Python based on just three main factors.
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2. Syntax
Java has a defined syntax. At the time of coding, the developer must define the data type
of variables. Throughout the program’s lifetime, this data type cannot be changed at all! It
is this feature that differs in Python development.
In Python development, the developer does not have to manually define any data types as
they are automatically assigned. Plus, throughout the process, these data types can be
changed. This makes Python a dynamic programming language and much more flexible
for businesses.
That said, Java follows syntax rules strictly. A missing semicolon or bracket anywhere in
the code will result in an error! But in Python, such rules do not apply. Naturally, this
makes Python much easier to learn and code with.
If you hire Java developers with much experience in Java coding, there won’t be any
problems with syntax errors.
Performance
When it comes to speed, there is a notable difference in the performance of both
technologies.
Java actually performs tasks much faster and exhibits excellent performance. The Java
technology executes code faster and executes multiple requests simultaneously. This is
probably why Java application development services are in high demand.
On the other hand, Python’s performance is underwhelming. Python executes code line-
by-line! This as you can imagine will lead to a great decrease in the speed of Python
applications.
Frameworks and Tools
Both Java and Python are technologies that can be used to develop applications that sort
through and retrieve important data. Based on the data found, it could be used for
analytical purposes or machine learning.
If a Python web development company needs to develop applications for data science
purposes, it would use these libraries.
Data science libraries in Python :
Pandas
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3. The Pandas library is a well-known, if not most popular library in the Python language. It is
a valuable library because it is useful in the process of data munging or wrangling. Data
analysis is possible by using series and data frames. A major bit of Python web
application development for data science makes use of the Pandas library.
SciPy
A short-form for Scientific Python, SciPy deals with applications that relate to Science,
Mathematics, and Engineering. With this library, it is possible to solve problems that
involve linear algebra, statistics, and optimization.
NumPy
NumPy or Numerical Python is for enterprises whose job is to deal with high-level
mathematics. It is the base for all Python libraries with use in mathematical applications.
TensorFlow
Developed by the Google brain team, TensorFlow is an open-source library that aids in
the implementation of deep-learning applications.
We know the scientific libraries of Python. Time to see what libraries every Java web
application development company needs to know!
Data science libraries in Java:
WEKA 3
WEKA 3 stands for Waikato Environment for Knowledge Analysis. It is an open-source
library that facilitates the development of applications that are used for data modeling,
analysis, and data mining.
Java ML
Also known as Java Machine Learning. This library comes with the ability to develop
calculative applications. These applications are capable of data processing, classification,
and analysis. A Java development company will be able to master this library as
machine learning is an expanding field.
Apache Spark
Apache Spark may be the most important library in Java for data science. With its
capability of processing large data subsets, many other libraries are based-off on Apache
Spark. Various built-in modules come with Apache spark, these include Spark MLli, Spark
SQL, and Spark Streaming.
Deeplearning4j
The name suggests the use of Deeplearning4j, deep learning. It is most applicable in Java
app development involving machine learning.
Conclusion
Java vs Python poses a difficult battle between two very useful technologies.
When assessing the strengths of both Java and Python, Java wins performance-wise but
Python wins syntax-wise. This leaves it all up to the libraries available in each technology
and how effective they are at their purpose.
Both have efficient libraries and it is very difficult to separate them in terms of library
numbers and effectiveness. Either one you choose will be highly effective for data science
software development!
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