Data Science is ideal merger of programming, analytical, and
business skills that allows extracting significant insights from raw and formless
data.
Data Science is a study which deals with recognition, depiction
and mining of meaningful information from data sources to be used for big
business purposes.
Through massive quantity of facts generating each minute,
the necessity to dig out the constructive insights is a must for the business
to stand out from the crowd. Data engineers group the database and data storage
in order to facilitate the process of data mining, data munging and other
processes. Every other organization is running behind profits, but the
companies that devise efficient strategies based on fresh and useful insights
always win the game in the long-run.
The occupation of a Data Scientist is to excavate into the
granular level in order to understand complex behaviours, trends, inferences,
analytical creativity, time series analysis, segmentation analysis, inferential
model, quantitative reasoning, and more.
“Data Scientist is better at statistics than any software
engineer and better at software engineering than any statistician.” ― Josh
Wills, Director of Data Engineering at Slack .
There is no precise definition of what accurately comprises
a Data Scientist’s roles and responsibilities. It can involve anything from
optimizing the sales funnel to getting the right strategy for a company to
enter the next lucrative international market. So it is a bit tricky trying to
define the work of a Data Scientist in a simplistic manner. There can be a lot
of ambiguity regarding whether something falls under the purview of a Data
Scientist.
Advantages of Data Science
- · Data scientist helps the organization to come up with enhanced and quicker decisions.
- · It empowers the decision-makers with rock-solid data and outlines a pathway to achieve business goals.
- · You can foresee new challenges and opportunities through the authority of data.
- · Spotting trends and capitalizing on it before the competition.
- · Setting the guidelines for most excellent practices and tried and experienced methodologies.
- · Rigorously testing the decisions until it achieves excellence.
S6 Important Tools in Data Science
1. R Programming
The R programming is a statistical programming language that
is equipped with a wide range of features, functionalities. It has been the
most promising language when it came to data analytics and machine learning.
2. SQL
SQL refers to the structured programming that is used to
work with relational database management systems. This SQL is useful for data
follows a certain format like the row and column standard type that is used to
depict a huge amount of data even in today’s world of unstructured data. SQL is
extensively used by database administrators and developers alike.
3. Python
Python is a high-level, powerful, object-oriented
programming language that is highly versatile. It is used for a variety of
applications but none more important than in the data science domain and
machine learning applications. It has a huge set of libraries that is one of
the distinct features of Python programming language.
4. Hadoop
This is a tool used for big data applications and it is the
most powerful as well as an open source solution. It has a huge ecosystem that
comprises of some of the best tools for working with big data. You store, compute;
deploy real-time analytics among things on big data through the Hadoop and its
ecosystem of tools.
5. SAS
SAS is a powerful business intelligence and analytical tool.
It is a software suite for extracting, analyzing and reporting on a wide range
of data and derive valuable business insights from it. It includes a whole set
of tools for working across the various steps of converting data into business
insights.
6. Tableau
This is the most powerful data visualization, analysis and
reporting tool. The best of Tableau is that you don’t need any technical
knowledge or programming skills in order to derive valuable insights from
Tableau.
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