Top 10 Statistics Tools to Get Better Data Insights

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Top 10 Statistics Tools to Get Better Data
Insights
WWW.STATANALYTICA.COM
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Overview
Statistics tools are playing a crucial role in
running our life. We performed various tests on
data to get valuable results from the data. We
can get more information from that data if we do
the proper experiment on the data. Every analysis
depends on appropriate planning and execution. To
win the data experiments battle, we need to make
sure that we are analyzing the data in the right
ways that will help us find critical insights of
data. Analyzing the data is crucial in many
aspects of our life. There are plenty of
statistics tools in the world that are designed
for the statistical analysis and make this
process super smooth and easy for the users. Here
we are going to discuss the topmost statistics
tools useful in many different aspects of our
lives.
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Statistics tools for data analysis
SPSS (IBM)
SPSS is the number 1 statistics tool for human
behavior research. As the name suggests, it
stands for Statistical Package for the Social
Sciences. That is why the statisticians used it
widely for data analysis purposes. Apart from
that, you can also use the GUI for the graphical
depictions of results. You can also create a
customized script for automatic analysis. It is
one of the most potent statistics tools for
advanced statistical processing. SPSS Inc
introduced it, but later on, it was acquired by
IBM in the year 2009. Therefore the SPSS is known
as IBM SPSS.
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R
R is one of the best open source statistics tools
for data analytics. It is use by statisticians
for research purposes. It offers excellent
toolboxes that are available for a vast range of
applications. R is an open-source programming
language, and it has a severe learning curve. It
is not for beginners, and you need to have good
command over coding to get started with R. It was
developed at Bell Laboratory by John Chambers and
colleagues. R offers a wide range of statistics
and graphical techniques i.e., linear, nonlinear
modeling, classical statistics test, time series
analysis, and many more.
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Microsoft Excel
MS Excel is one of the best statistical tools for
data analysis. It offers cutting edge solutions
to data analytics professionals. You can use it
for data visualization as well as simple
statistics. It is the best statistical tool for
those who want to perform basic data analysis
techniques on their data.
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Tableau
Tableau is one of the most powerful data
visualization software. The data visualization
technique is widely used in data analytics. Now
it is the part of Salesforce that is known for
its high-end CRMs in the world. You can create
the best data representation of the massive
amount of data in Tableau within a few minutes.
Therefore it helps the data analyst to make
quicker decision making. It has an enormous
amount of online analytical processing cubes,
cloud databases, spreadsheets, and many more. It
also offers the drag and drop interface to the
users. Thus the user needs to drag and drop the
data set sheet in Tableau and set the filters as
per their requirements.
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Minitab
It is not the most popular data analysis
statistics tool. But you can do the basic as well
as some advanced statistical techniques using
Minitab. You can also execute commands in MATLAB
with the help of GUI as well as scripted
commands. It was developed in the year 1972 at
Pennsylvania State University. The researchers
Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian
L. Joiners are the creators of this tool. In
earlier days, it was known as OMNITAB. In this
way, it will enable you to find a solid answer to
the most challenging questions.
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Statistics Tools For Data Science
Apache Hadoop
When it comes to data science, then Apache Hadoop
is considered as the best and most reliable
statistics tool. It is open-source software that
is used for scalable computing. Apache Hadoop is
part of the Apache software foundation under
Apache license 2.0 and can solve the most complex
computational problems. It is also best at
data-intensive tasks. Hadoop has the best
functionality that it doesnt send the larger
files directly to the node. It splits the large
files into small chunks and then sends it over to
the node with different instructions.
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SAS
SAS is one best statistics tool for data science.
It is also playing a crucial role in the data
science industry. You can use it either as the
GUI or create your script for the advanced level
statistics analysis in data science. It can
produce the best graphs and charts. You can also
extend the functionality of SAS using the coding
feature.
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RapidMiner
RapidMiner is yet another best tool for data
science. It offers a platform that is quite
helpful in data preparation, machine learning,
and predictive model deployment. You can create
the data model from the initial stage to the last
step easily with RapidMiner. It offers a complete
data science package. It is best for machine
learning, deep learning, text mining, as well as
predictive analytics.
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Python
Python is one of the best programming languages
in the world. I have mentioned it in this blog
because it can work seamlessly with the
statistics. It is the most straightforward
programming language and offers lots of packages
and models for statistics and data science.
Python is a high-level, general-purpose
programming language. It was created by Guido Van
Rossum and released in 1991. It is the best
statistics tool for data science. You can fulfill
all your statistics requirements by using Python
for data science.
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MATLAB (The Mathworks)
MATLAB is the best statistics analysis tool and
the best statistics programming language in the
world. It offers a variety of tools in its
toolbox that makes it quite easy to use
programming languages. You can do the most
sophisticated statistical analysis with MATLAB
i.e., EEG data analysis. You can extend MATLAB
functionality with the help of add ons for
toolboxes. It offers a multi-paradigm numerical
computing environment. It means that you can do
the procedural as well as object-oriented
programming with MATLAB. Math Works developed it.
It is best for matrix manipulation data function
plotting, algorithms implementations, user
interface creations, and many more. You can also
run the program of other programming languages
with MATLAB all because of its interfacing
function.
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Conclusion
We have seen that there are plenty of statistics
tools for data analysis, as well as data science.
Even there are some more statics tools available
in the world that can fulfill your requirements
for data analysis and data science. Even some of
the statistics tools online are the alternative
to these statistics tools that I have mentioned
above. But all these tools are best in their
class. You can use any one of these tools without
having the second opinion.
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