Machine Learning and Data Science are undoubtedly the most sought after career options in the last 5 years. Not only because of its popularity but also because the technical knowledge that leads to the above mentioned career courses is something which is easily available. Yes, you read that right. There are thousands of pieces of information available online that not only teach but also encourage the practical establishment of one's computer science skills. This continuous practice can be done from your comfortable home chair and can lead to a bright career. However, it is very important to know that machine learning and data science are not just about computers or computer science. It has a similar contribution to mathematics which provides the logic for the execution of a certain program.
Referring to the program, in this blog, we are here to guide
you about the various programming languages (or as they like to call it
'tools' in the tech world) which are essentially the tools of any data science.
There are tools. Or studies related to machine learning. A very important
question always comes up: "What language to use for data science?".
Trust me as we say, there is a lot of debate between Python and R and which of
them is more popular for data science. While we believe that both the languages
are equally important for any data scientist, there are other programming
languages which are important in data science and can be used as per the
situation.
In this article, we aim to provide all the information about
the most popular tools/programming languages for Machine Learning and Data
Science. Let's start with data science.
1. Python
Python is one of the best programming languages for data
science because of its potential for statistical analysis, data modeling and
easy readability. Its extensive library support for data science and analysis
is one of the reasons for Python's enormous success in data science. There are
many Python libraries that contain a wide variety of functions, tools, and
methods for managing and analyzing data. Each of these libraries has a specific
focus with some libraries handling image and text data, data mining, neural
networks, data visualization, etc. For example, Pandas is a free Python
software library for data analysis and data handling, NumPy for numerical
computing, SciPy for scientific computing, Matplotlib for data visualization,
etc.
2. R
When talking about Data Science, it is impossible not to
talk about R. In fact, R is one of the best languages for data science
because it was developed by statisticians, for statisticians. It is also very
popular with an active community and many state-of-the-art libraries currently
available. In fact, there are many R libraries that contain many functions,
tools, and methods for managing and analyzing data. Each of these libraries has
a special focus on managing image and text data, data manipulation, data
visualization, web crawling, machine learning, etc., with a few libraries. For
example, dplyr is a very popular data manipulation library, ggplot2 is a data
visualization library, etc.
3. SQL
SQL or Structured Query Language is a language specially
created to manage and retrieve data stored in relational database management
systems. This language is extremely important for data science as it mainly
deals with data. The main role of data scientists is to convert the data into
actionable insights and hence they need SQL to retrieve the data from the
database as and when required. There are many popular SQL databases that data
scientists can use such as SQLite, MySQL, Postgres, Oracle and Microsoft SQL
Server. BigQuery, specifically, is a data warehouse that can manage data
analysis over petabytes of data and enables super fat SQL queries.
4. Matlab
MATLAB is a programming language for mathematical operations
which automatically makes it important for data science. It allows mathematical
modeling, image processing and data analysis. With a lot of mathematical
functions useful in data science for linear algebra, statistics, optimization,
Fourier analysis, filtering, differential equations, numerical integration,
etc., MATLAB is one of the most popular languages. Apart from all this, MATLAB
also has built-in graphics that can be used to create data visualizations with
different types of plots.
Java is one of the oldest programming languages and it is
also very important in data science. Most of the big data and data science
tools are written in Java such as Hive, Spark and Hadoop. Since Hadoop runs on
the Java Virtual Machine, it is important to fully understand Java in order to
use Hadoop. In addition, there are many data science libraries and tools that
are also in Java such as weka, mllib, java-ml, deeplearning4j, etc.
Machine learning is an amazing technology. It's tempting to
build a machine that behaves largely like humans. Mastering Machine Learning Tools Let You Play Wisely
1. Knime
Knime is an open-source machine learning tool based on GUI.
The best thing about Knime is that it does not require any programming
knowledge. One can still avail the facilities provided by NIME. It is generally
used for data-relevant purposes. For example, data manipulation, data mining,
etc. Furthermore, it processes the data by creating various different workflows
and then executes them. It comes with repositories that are filled with various
nodes which are then brought into the NIME portal. And finally, the workflow of
nodes is created and executed.
2. Accord.net
Accord.net is a computational machine learning framework. It
comes with an image as well as audio package. Such packages help to train
models and build interactive applications. For example, auditions, computer
vision, etc. Since .net exists in the name of the tool, the base library of
this framework is the C# language. Accord libraries are very useful in testing
as well as manipulating audio files.
3. Jupiter Notebook Note
Jupyter Notebook is one of the most widely used machine
learning tools. Not only its a very fast processing language but it is also an
efficient platform. Moreover, it supports three languages viz. Julia, R,
Python. Thus the name Jupiter is formed from the combination of these three
programming languages. Jupyter Notebook allows the user to store and share live
code as a notebook. One can also access it through GUI. For example, Win Python
Navigator, Anaconda Navigator, etc.
4. Tensor Flow
TensorFlow is an open-source framework that also comes in
handy for large scale numerical ML. It is a blender of machine learning as well
as neural network models. Also it is a good friend of Python. The most
prominent feature of TensorFlow is that it runs on CPU and GPU as well. Natural
language processing, image classification are the ones that implement this
tool.
5. Pytorch
Pytorch is a deep learning framework. It is very quick to
use as well as flexible. This is because Pytorch has a good grasp on the GPU.
It is one of the most important tools of machine learning as it is used in the
most important aspects of ML, including Deep Neural Networks and Tensor
Computation. Pytorch is completely based on Python. Along with this, it is the
best alternative to NumPy.
Now that you know the top programming languages for data
science, it's time to go ahead and practice them. Ivy Professional School
offers a plethora of certifications to choose from that not only enrich your
knowledge of data science and machine learning, but also enhance your practical
experience with the above tools. You can use Python for data analysis and SQL
data management. It is up to you to choose the right language for each
individual project based on your objectives and preferences. And always
remember, whatever your choice, it will only expand your skills and help you
grow as a data scientist!
So, these were some of the most popular and widely used
machine learning tools. All these show how advanced machine learning is. All
these tools use and run different programming languages. For example, some of
them run on Python, some on C++ and some on Java.
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