The Gamble utilizes time series models to more


The popularity
of the term “Data Science” has bombarded in technical, business
environments and academia, as indicated by a jump in job openings. However,
many critical academics and journalists see no difference between data science
and statistics implementation. Dealing with unstructured and structured data, Data
Science is a field that encompasses anything related to data cleansing,
preparation, and analysis. Data is everywhere and increasing at infinite
rate. In fact, the amount of digital data that exists is thriving at a rapid
rate—in fact, more than 2.7
zettabytes of data exist in today’s digital universe, and that is
projected to flourish to 180
zettabytes in 2025. That’s why more organizations of new world are
seeking professionals’ worker who can make sense of all the data. It’s the
future of development and present for sustainable development. For the future of data science, Donoho
projects an ever-growing environment for open science where data sets used for academic
publications are accessible to
all researchers. US
National Institute of Health has
already announced plans to enhance reproducibility and transparency of research
data. Data science is a discipline that
incorporates varying degrees of Data Engineering, Scientific-Method, Statistics, Advanced Computing, Visualization, Hacker
mindset, and Domain Expertise. A professional
practitioner of Data Science is called a Data Scientist. Data Scientists solve
complex data analysis problems. The job title has similarly
become very noted. On one heavily used employment site, the number of job
postings for “data scientist” inclined more than 10,000 percent
between January 2010 and July 2012. Data science extant makes the companies to
make stronger and smarter business decision.

Netflix data mines
movie viewing patterns to understand what drives user is interested in, and
uses that to make predictive decisions on which Netflix original series to

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Target features what
are major customer segments within it’s base and the unique shopping demeanors
within those segments, which helps to guide messaging to distinct market

Proctor & Gamble
utilizes time series models to more lucid and intelligible understand future need,
which help plan for production levels more optimally.

recommendation engines suggest items for the user to buy, determined by their
algorithms. Netflix recommends movies to the user. Spotify recommends music to
the user.

Gmail’s spam filter is
data product – an algorithm behind the scenes processes incoming mail and
determines if a message is junk or not and process accordingly.

Computer vision used
for self-driving cars is also data product – machine learning algorithms are
able to alert itself by recognizing traffic lights, other cars on the road,
pedestrians, etc.

The requisites for the professional industrial data scientists –

Mathematics Expertise

At the heart of mining data insight and building data product is
the ability to view the data through a quantitative and logical oculus. There
are delicacy, dimensions, and correlations in data that can be expressed
mathematically. Finding solutions utilizing data becomes a brain perplexing job
of heuristics and quantitative technique. Solutions to many business problems
involve building analytic models grounded in the hard math, where being able to
understand the underlying mechanics of those models is key to success in
building them.


Strong Business


It is important for a data
scientist to be a shrewd, tactical and stalwart business consultant. Working so closely with data, data
scientists are positioned to learn from data in ways no one else can. That creates
the responsibility to translate observations to shared knowledge, and
contribute to strategy on how to solve crux business problems. This means a
core competency of data science is using data to intelligibly tell a story. No
data-puking – rather, present a cohesive narrative of problem and solution,
using data insights as supporting pillars, that lead to guidance.

Technology and


First, let’s clarify on that
we are not talking about hacking as in breaking the information by
getting into computers. We’re referring to the technical coder subculture
meaning of hacking – i.e.,
creativity and ingenuity in using technical skills to build things and find
tactical solutions to problems as expressed in Fig. 1.




I.     Pandas


Pandas is a BSD-licensed,
open source library providing effecient-performance, easy-to-use data
structures, algorithms and data analysis tools for the Python programming language. Pandas is a NumFOCUS sponsored
project. This will help ensure the success of development of pandas library as a
world-class open-source project, and makes it possible to donate to
the project. Python has long been great for large data manipulation and
preparation, but less so for data analysis and modeling. Pandas helps fill
this vaccum, enabling you to carry out your entire data analysis workflow in
Python without having to switch to a more domain specific language like R.

together with the marvelous IPython toolkit
and other libraries, the environment for working in data analysis in Python
excels in performance, productivity, and the ability to collaborate. Pandas does not
implement significant modeling functionality outside of linear and panel
regression; for this, look to stats models and scikit-learn. More work is still needed to make Python
a outstandingly brilliant class statistical modeling environment, but at
present it is well on its way toward the goal.


a.       Installation


The optimum solution for
installing the pandas on system.

conda install pandas


Also can be installed from the PyPI where it has been

pip install pandas


b.       Specifications and library highlights


Tools for reading
and writing data between in-memory data structures and
different formats: CSV and text files, Microsoft Excel, SQL databases, and the
fast HDF5 format, Intelligent data alignment and integrated
handling of missing data:
gain automated label-based alignment in computational techs and easily
manipulate messy data into an orderly form, Intelligent label-based slicing, fancy indexing, and sub
setting of cosmic data sets, High performance merging and joining of data sets,
Python with pandas is
in usage in a wide variety of academic
and commercial domains, including Finance, Neuroscience, Statistics,
Economics, Advertising, Web Analytics, and more.


II.    seaborn


Seaborn is a Python interactive
visualization library based on matplotlib. It provides a h