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Data Visualization becomes more effective and simpler when done with Tableau, R and Python
04 January 2019
its barest, data visualization is, as the term denotes, visualization of data.
This refers to data that is represented not just in text or word format, but in
the form of infographics, tables, pictures, charts, diagrams, maps, graphs, etc.
As is evident from this simple explanation, data visualization involves the use
of these aids to give greater clarity to information (data) and make it easier
visualization is a set of tools but more importantly, it is a creative means of
putting across data that becomes interesting and interactive. Towards giving
data this character, data visualizers create aids such as storyboards and dashboards
to make data more lively and presentable rather than being in a staid, raw
form. In the context of business, it brings in a method or way of understanding
data and how to use it for making business decisions.
the beauty of data visualization is that it need not be restricted to business.
Even data from areas seemingly completely unconnected to business can be
represented in a wonderfully creative manner using data visualization. Data
from areas as varied as history, government, finance, technology, urban
planning, astronomy, fashion, demography and many more subjects can be
visualized for a very attractive effect.
What makes a
career in data visualization valuable in today’s world?
all the areas in which data visualization are very active in today’s world and
are a strong source for data visualization, there is one truly critical factor
that has brought data visualization to the fore today. It is data science. Data
science is a branch of Big Data that generates lots of data to help in
decision-making. The role of data visualization can never be understated in
such a data-dependent area as data science.
Data and data science have the potential to make an unimaginable impact on our
future in many ways. Being able to generate data for almost everything that we
can think of, Big Data is set to alter our approach to life in the coming
is in a data-heavy field such as Big Data or data science that the role of data
visualization becomes central. Data in themselves don’t mean much unless there
is a method of making sense out of them. This is the core function of data
visualization. Data visualization goes beyond just presenting raw data
colorfully. The core of data visualization is that it should produce meaningful
and critical insights.
should generate intelligence and should lead to proper decision-making. It
should be able to mix the elements of creativity and presentation with
statistics and data judiciously and attractively. It should strike the right
balance between content and presentation. A superbly visualized presentation
could convey little and very powerful data could fall by the wayside due to a
pedestrian form of presentation. A data visualizer has to strike the right mix
of the worlds of creativity and technology to bring about not just added
effect, but sensible intelligence.
all the uses to which data visualization can be put, it is natural that it is a
profession that is in great demand today. Data visualization is gaining
prominence as a very valuable teaching aid in many parts of the world.
Types of data
visualizers visualize the data they have with them in three distinct ways:
Abstract data: This involves
abstracting internal data that have no spatial elements and creating graphs out
of them. Business Analytics is one of the core areas in which internal abstract
data is visualized.
Mapping of data: In this
method, data visualizers map spatial data to show where the inputs are coming
from, where they are going, and what can be done about it to improve the
business bottom line. A typical example is online food services, which track
data to get an idea of where people are buying from, what they are buying, how
much they are spending, what they are looking for and such other parameters.
This helps them to analyze data to target their marketing and advertising more
Storytelling: Data is then
presented in the form of stories to make the data more pleasant and interesting.
This helps the business to deliver its message and its products to its market
The role of Tableau,
R and Python in data visualization
visualization becomes possible with the use of a few tools and programming
languages. These are Tableau, R and Python. While tableau is used for the
visualization part per se by being
able to help produce a range of all the inputs needed for data visualization,
such as charts, tables, graphs, etc.; R and Python are scripting languages that
make its realization possible technically. This makes Tableau, R and Python
indispensable to data visualization.
when starting a career in data visualization, isn’t it necessary to get a
thorough and solid understanding of Tableau, R and Python? Who would want to be
foolhardy to venture into a career in a highly developed technical area without
the requisite knowledge and skill?
is where Simpliv is of tremendous help to you in taking up a career in data visualization.
This Fremont, CA-based learning platform has designed a course on data visualization
which will help you understand the concepts behind using Tableau, R and Python
in this field. This course is designed for those who want to make it in a
career in data visualization and graduate to a higher level in their profession.
over a period of 45 hours, this virtual classroom course on Data Visualization
with Tableau, R and Python will offer complete learning that professionals need
to grow in their careers and chart their own career path. This course will be
offered over a period of three weeks starting January 21, 2019 and will be held
from Monday to Friday, from 7 PM to 10 PM, IST. It will conclude on February 8,
taking up this course
R and Python skills constitute the core of data visualization. This course on
data visualization with Tableau, R and Python aims to give learners the
knowledge of these areas which will help them understand these concepts and
build a career in data visualization. With this knowledge, they improve their chances
of getting absorbed by the industry. They will understand how to choose a
combination of Tableau, R and Python for making Data Visualization effective.
heart of data visualization is how well its three elements are mixed. This
course will give participants a proper idea of how to mix and match these. It
will show them how to adapt the right tools for doing so. Participants will be
able to understand how and when to use Tableau, R and Python for Data
the end of the learning, participants will earn a certificate from Simpliv.
This will help them to gain credibility in the job market. It will be a
significant value addition to their CV and will strengthen their prospects in the
course will put learners on the path to a career in data visualization. The
career prospects for such professionals are attractive, considering that data
visualization is a relatively very new field of specialization in the IT realm:
on average, they earn between $74000 and $75000 or $30 an hour in the US. More
than anything else, data visualizers can work in multidisciplinary areas such
as data science.
the enormous value that a course of this nature offers, it comes at a surprisingly
competitive fee of just ₹9,999. This
special price is only for who enroll for this batch, for whom there is a discount
of over 40%. The regular price of this course is ₹16,665.
Simpliv has designed this course on data visualization for Tableau,
R and Python to help participants gain clarity on the following aspects:
to use the right tools
to connect to the right data source
to connect Tableau Desktop with R to optimize the functions of R
Python scripts for fields in Tableau in the same way as is done with R
course on Data Visualization with Tableau, R and Python offers learning on:
- An Introduction
Filters and Action Highlights
top 10 Gems
Calculations (Explaining various Table calculations)
between Table calculations and Calculated fields
to connect to databases (Video based)
to integrate Tableau with R (Video based)
Python and R basics
in R (Packages in R)
family functions in R