SOFTWARE & TOOLS
FOR DATA SCIENCE
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Tutors India
Group www.tutorsindia.com
Email: info@tutorsindia.com
Today's Discussion
OUTLINE
Introduction
Need For Software And Tools
Efficiency Of Software And Tools
Recent Tools And Software In Data Science
Future Scope
Summary
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.
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.
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.
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.
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.
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.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 –bigml.com.
FUTURE SCOPE
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.
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.
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.
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.
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What's Next in Tech Workspace
01 Increased task automation and use of artificial intelligence.
02 Extra focus on high-value tasks.
03 Continuous investment in cybersecurity and security technology.
04 A better conscious focus on mental health.
05 Greater geographic distribution and representation of the workforce.
MATT MULLENWEG
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