tools of data science

1. Tools
Popular Data Processing Tools In Data Science
This collection of tools is used in data science to perform various operations on data to extract
useful insights. Most of them are widely used in the industry and they usually get the job done
easily.
Jupyter
Current version: 5.7.2
The Jupyter Notebook is an open-source tool that permits users to create and distribute
documents that contain coding, equations, visualizations, and narrative text. It is favorable for
data cleansing, data transformation, statistical modeling, data visualization, machine learning,
and countless more. It supports Python, R, Scala, and Julia. It helps in leveraging big data
engines such as Apache Spark, Python, R, and Scala. One can explore libraries like pandas,
scikit learn, keras, matplotlib and many more which belong to python. It provides support for
TensorFlow for computer vision analysis.
Currently, many big organizations like IBM, Google, Microsoft, Berkeley, and NYU are taking
advantage of this tool to work with machine learning and big data. It's a simple web page which
uses the system browser to carry on its operations. Most of the libraries that are needed for data
imputation and data processing are included in Jupyter. Others can be installed directly through
Jupyter Terminal or the systems command prompt.
Founded in 2014 as an open-source project, later it evolved to support interactive data science
and scientific computing across all programming languages.
Majority of the data science enthusiasts and professionals are aware with the flexibility and
reliability Jupyter offers and hence it is their most preferred tool to carry data cleaning, feature
engineering, model implementation and data visualization for Python.

2. Jupyter is still a common tool for data scientists and data analysts to use when performing
operations on data in python. It will continue to dominate as a favorable tool for python due to its
reliability and easy interface.
R Studio
Current version: 1.1.463
R Studio is an open-source tool used to perform operations on data using the R language. It
includes packages for data imputation and manipulation like mice, dplyr, Hmisc, and
missForest. R takes care of visualization by providing a shiny tool. RShiny takes care of
interactive web applications for visualizing data which brings data analysis in R to life.
NASA, Accenture, GE Global, Nestle, CAVA and countless more multinational companies are
using this tool to enlighten their data-driven capabilities. If a newbie is interested in working on
simple and complex datasets and wants to implement the same using R then, this is the ideal
tool. It brings tons of functionalities on the table like data imputation, data cleaning, data
manipulation, exploratory data analysis in the form of scatter-plots and histograms, SQL-
integration, natural language processing, model fabrication in machine learning, visualization of
the efficiency of the model and artificial intelligence.
JJ Allaire is the brain behind R studio. He wanted to make a tool for R which is both, universally
accessible and effortless to use. Its first beta version was released in 28th February 2011 which
was v0.92. Later, a stable build was released on 1st November 2016(v1.00).
RStudio is downloadable in two editions: RStudio Desktop and RStudio Server. The desktop
variant permits the program to run locally on any local machine which has R installed. Rstudio
server edition allows accessing RStudio on a web browser while it is operating on a remote
server.
R studio includes an array of functionalities in its tool to ease the pain of installing packages and
finding help for queries through external sources. The interface has been designed in a way
which provides four panes for different functions- The first left pane is reserved for coding, the
second pane is left for output and errors. The third pane which is at the right reserves the history

3. of variables executed and stored in the memory. The fourth pane is used to install packages,
get help for any functions, query or library, the access file directory of the system and visualize
plots for better understanding.
This tool will stay for a while in the computers and servers for where and when R is initiated and
executed. R is still followed as a norm in data science and data analytics by companies who still
use python as some functionalities in R are better when compared to Python.
SAS
Current version: 9.4
SAS was the first analytics tool which was developed at SAS Institute. It was meant for
business intelligence, multivariate analysis, and conventional data management. They designed
it to match the requirements of descriptive and predictive analytics. It was there in the market
even before R and Python and catered to a specific set of audience at that time.
It was first designed at a State University in 1966. Its development was further instrumental in
the 80s and 90s with the addition of new statistical features and additional components
SAS is a software suite which the enables managing and changing the retrieved data from a
diversity of sources and conduct statistical analysis on it. It has a graphical point-and-click user

4. interface for non-technical professionals and more advanced options through the SAS
language.
It been prevailing in the market for more than 40 years and still being used for analytical
decisions and statistical manipulations. It makes it quite easy to realize which data is important
and which isn’t. Making intelligent decisions and help companies grow has been its key factor
throughout the years.
SAS has been proved to make important key business decisions and will accelerate the current
scenario in doing so.
Apache Spark
Current version: 2.4
Apache Spark is an open-source shared software which specializes in the cluster-computing
framework. Spark provides a platform for programming complete clusters coupled with data
parallelism and fault tolerance. It can be used to execute projects in Scala, Java, SQL, Python,
or R. It can be considered as a unified engine which is used to take analytical decisions from
large-scale data processing.
A cluster manager and a distributed storage system are the important factors in Apache Spark.
For managing clusters, Spark provides standalone Hadoop YARN. For distributed storage,
Spark offers an interface with a wide variety of applications like Hadoop Distributed File System,
MapReduce File System, Cassandra, or else a custom solution can be implemented. Spark can
run on a single machine with one executor per CPU core.

5. Spark’s development began in the year 2009 and was later open sourced in 2010. In 2013, it
was handed over to the Apache Software Foundation and was transformed to Apache 2.0.
Since 2015, Spark had many active contributors, making it one of the most active projects in the
Apache Software Foundation and one of the most active open source big data projects.
Microsoft Excel
Current version: 2016
Microsoft Excel or widely pronounced as MS Excel is a spreadsheet used to carry calculations,
visualize graphs. It can be integrated with Visual Basic Applications(VBA) for macro
programming language capabilities. There are many free online tools available for processing
huge chunks of data in the market, but to its array of functionalities and capabilities, most
enterprises prefer MS Excel. It grants a platform to the business users that enhances usability
and credibility.
Data is arranged in terms of cells which is made of rows and columns. It provides column
integration, pivot tables, option for charts and graphs. Many mathematical and statistical
functions and formulae have been fed to initiate lengthy and complex calculations which are
quite common for businesses.
Established on September 30, 1985, its first-ever public release was crucial for enterprises and
biggies who were in need of a tool that will serve as a platform to solve their day-to-day
problems.
It has a wide support for VBA, allowing the user to perform tons of algebraic calculations, for
example, for solving differential equations of mathematical problems, and then reporting the
results back to the spreadsheet. It also has a variety of interactive features allowing user
interfaces that can completely hide the spreadsheet from the user. This language has been
crucial for enabling many useful features and functions necessary for writing macros.

6. It has native support for Windows and MacOS and even runs on Android and iOS for on-the-go
access and capabilities. It is a market trend and is preferred by many enterprises to run records
which includes huge chunks of data and calculations.
Structured Query Language - SQL
Current version: 2016
SQL is a programming language used to handle and manipulate structured queries stored in
relational database systems also know as RDBMS. Its primary function is to handle the
structured data in which there can be associations between different entities of the data. During
the time when it was introduced, SQL had many tricks under its sleeve, it introduced the
concept of accessing many records with one single command, eventually eliminating the need
to specify how to reach a record with the help of its queries.
SQL is used to interact with data stored in relational databases. It was formally developed at
IBM Labs by Donald D and Raymond F. in the early 1970s. This version which was called
SEQUEL (Structured English Query Language) at the time of its advent, was created to
manipulate and retrieve data stored in IBM's original relational database management system.
Later, Oracle Corporation saw potential in this market and developed their own SQL-based
RDBMS with the hopes of selling it to the U.S. Navy, Central Intelligence Agency, and other
U.S. government agencies.
Today, it is practiced in enterprises when they need to retrieve data from the original database
for operational purposes. It has built-in queries which enable the users to extract, select,
manipulate and alter data at their own will. SQL uses clauses, expressions, predicates, queries,
and statements to interact with data.

7. Right now, MySQL, Oracle, PostgreSQL are some of the common tools used in this domain.
There are many alternatives for SQL even though companies are using it for all their databases.
SQL uses many different queries, both simple as well as complex. Joins help in combining two
or more tables stored in a database based on a related column. It includes inner and outer joins.
There are many queries like tagging a primary key to make an attribute unique, dropping or
altering values, union, group_by which are concerned with the way the values need to be
represented.
Tableau
Current version: 2018.3
Tableau is a data visualization tool used for representing data in terms of charts and
dashboards. It is available online as well as offline. It has the capability to handle relational
databases, OLAP cubes, spreadsheets and also generate a number of graph types depending
on the type of data retrieved. It can also retrieve data and store data in its in-memory data
engine. The latitude and longitudes features of a location are offered in Tableau which creates a
geographic representation of any reports regarding sales, profit or any other factors which can
be represented with the help of maps.
It was first developed in January 2003 by three visualization mavericks who specialized in
visualization techniques for exploring and analyzing relational cubes and databases. Later, it
went on to become a big product which slowly introduced its other products for the emerging

8. market- Tableau Desktop, Tableau Server, Tableau Online, Tableau Reader and Tableau
Public.
Tableau has the capability to open a ton of possibilities in business analytics and business
intelligence. Enterprises are taking full advantage of the vast opportunities, this tool can open
and generate useful business insights from the company's growth perspective.
In 2008, Tableau was awarded the best business intelligence solution for its easy and quick
visualization capabilities. It was at the top of all the visualization tools in the market and
continues to dominate the current scenario.
Realizing its future scope, companies have started deploying dashboards in their meetings and
discussions. These visualizations have now managed to gain some momentum with respect to
enterprise-level functionalities by helping in determining the factors that can contribute to the
acceptance of new market strategies and skills needed to stay in
PowerBI
Current version: 2.64
Power BI is a business intelligence tool developed by Microsoft. It provides interactive
visualizations coupled with business intelligence capabilities, where users can build their own
customized reports and dashboards, without having to depend on information technology users
or database administrators. It provides cloud-based BI services as well. It offers data warehouse
capabilities including data discovery, data preparation, and interactive dashboards at the blink of
an eye.
In 2016, Microsoft released their additional add-on service called Embedded Power BI on its
Azure cloud platform. One main differentiator of the product was the ability to load custom
visualizations.

9. Power BI was first created in 2010 and named Project Crescent. Later it got renamed to Power
BI and was unveiled by Microsoft in September 2013 for Office 365 Suite. Later, Microsoft
added additional features like Q&A, enterprise-level data connectivity and various security
options. Power BI made its first public entrance in the year 2015.
After Tableau had captured the market, Power BI came up with the simple philosophy of
capturing enterprise and analytics companies who want to just visualize their data on the go by
accessing functionalities of PowerBI either online or offline. It enables connecting to hundreds of
data sources in the cloud. It uses power query to simplify data ingestion, transformation,
integration, and enrichment.