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Software & Tools For Data Science uk,uae,australia, (1)

Software & Tools For Data Science
Dr. Nancy Agnes, Head, Technical Operations, Tutorsindia info@ tutorsindia.com
Data Science is the analytical field that vitally
depends upon the large amount of data, such as Big
Data, to analyze the business problem and provide
the accurate solution for the problem. But handling
the huge amount of data is not the easy task. To
avoid manual errors, the automatic computational
and logical processes are enhanced via tools and
Software. Using that Software and tools, the
problem can be solved with a minimum amount of
time with high accuracy. [1]
Several tools and Software with high flexibility and
features with good extracting and visualizing
effects provide more accuracy even when the data
is large. Many of the tools and Software provides
high-efficiency and accurate results. [4]
1. Tableau: Tableau is the complete data
visualization tool. It supports all kinds of
worksheets and structured form of data for data
processing, exploratory data analysis, and database
compatibility. It is not an open-source platform. It
is dependent upon the organization necessity. The
visualization format is very admiring and good
The organization may possess a huge amount of
business revenue annually, the vast amount of
turnovers and losses, employee strength according
to productivity, to understand the current market
values and strategies can be estimated to forecast
the organization strength. For instance, the Netflix
viewers may increase/decrease according to the
consecutive shows cast in a certain period. Many of
the viewers may withdraw their accounts due to the
poor quality of the streaming. Netflix analyzes the
root cause for their withdrawals. The analytics
process will be done to predict the cause for the
withdrawal. Based on the analytics report, further
modifications and other recommendations will be
published and cast. [2]
2. Jupyter Notebook: Jupyter Notebook is a peak in
the data science market because of its compatibility
in both the statistical analytical languages Python
and R. Jupyter supports coding flexibility Python
and R language. Basically, it is a web-based
application which supports all kind of worksheets
and spreadsheets for data extraction and data
4. Python: In recent years, many data scientists
plant their roots in the Python language, which
provide more flexible packages for statistical and
mathematical analyses. Python has the feature to
connect the other similar tools like Scipy, Dask,
HPAT, Cython to provide more flexibility and
By using Software and tools, the accuracy of
results for a large number of business datasets can
be obtained efficiently. Tools and Software also
help transform the data into a visualized format
existing in the structured or semi-structured form of
data. Every Software and tools have a unique way
of representing the data in the graphical format.
The Software and tools generate the exact results
and outcomes based on the report imported into it.
The purpose of the data science tools and Software
is to extract, manipulate, and process the data. On
the other hand, converting the structured data
doesn't convey any information and convert those
data into useful information. [3]
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3. MATPLOTLIB: Matplotlib developed especially
for Python language to provide more plotting and
visualization features. Matplotlib provides more
modules, especially for visualization. For instance,
Pyplot provides more modules for graphs and plots.
5. R and R Studio: As same as Python, R Studio
designed especially for statistical and mathematical
analytics. R Studio is the open-source platform.
The console port of the R Studio supports more
library packages and analytical functions. [6]
6. BigML: BigML is completely based on machine
learning algorithm for data science and data
analytics. It provides more flexible packages with
automation regression, linear regression analysis,
cluster analysis, anomaly detection, and forecasting
of time series data. The BigML has the feature of
online assessment from the source website –
visualization and analysis using R, RStudio, and
RMarkdown." Journal of Statistics Education 25.2
(2017): 60-67.
1. As the data generating everywhere around the
world, handling and manipulating the large volume
of data will be the tedious process. So the need for
data scientists is vast, and the processing of large
amounts of data using automation tools provides
better results.
2. The errors in manual computations will lead to
recomputation which is time consumption process.
To ignore those manual errors, tools and Software
with high efficiency and accurate results even for
forecasting and predictive analysis.
3. The minimal time of the process is enough for
the Software and tools comparatively manual
computations even for a small number of datasets.
The automation tools exactly predict and provide
the outcome based on the trained data set.
The world is full of data everywhere, and those
data can be stored either physically or virtually. But
handling the entire data is not the single-day
process. It a routine for the data scientists to
compute the tedious data and produce the output
for the data. The dataset can be efficiently
manipulated through recent technology-based tools
such as Artificial Intelligence, Machine Learning,
Cloud computing algorithms.
1. Zhang, Amy X., Michael Muller, and Dakuo Wang.
"How do data science workers collaborate? roles,
workflows, and tools." Proceedings of the ACM on
Human-Computer Interaction 4.CSCW1 (2020): 1-23.
2. Saling, Kristin C., and Michael D. Do. "Leveraging
People Analytics for an Adaptive Complex Talent
Management System." Procedia Computer Science 168
(2020): 105-111.
3. Bloice, Marcus D., and Andreas Holzinger. "A tutorial
on machine learning and data science tools with
python." Machine
Informatics (2016): 435-480.
4. Van Der Aalst, Wil. "Data science in action." Process
mining. Springer, Berlin, Heidelberg, 2016. 3-23.
5. Ari, Niyazi, and Makhamadsulton Ustazhanov.
"Matplotlib in python." 2014 11th International
Conference on Electronics, Computer and Computation
(ICECCO). IEEE, 2014.
6. Stander, Julian, and Luciana Dalla Valle. "On
enthusing students about big data and social media
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