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Software & Tools For Data Science

Dr. Nancy Agnes, Head, Technical Operations, Tutorsindia info@ tutorsindia.com

I. INTRODUCTION

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]

II. NEED FOR SOFTWARE AND TOOLS

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]

III. EFFICIENCY OF SOFTWARE AND TOOLS

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]

IV. RECENT TOOLS AND SOFTWARE IN

DATA SCIENCE

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

looking.

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

manipulation.

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]

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

reliability.

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

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of time series data. The BigML has the feature of

online assessment from the source website –

bigml.com.

V. FUTURE SCOPE

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.

VI. SUMMARY

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.

REFERENCES

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 Learning for Health

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

visualization and analysis using R, RStudio, and

RMarkdown." Journal of Statistics Education 25.2

(2017): 60-67.