Data Science with Cloud Computing

Data Science with Cloud Computing

Data Science

Data science is a field of Big Data aimed at providing meaningful information based on large amounts of complex data. Data science, or data-oriented science, combine different fields of work in statistics and computing to interpret data for decision making.

Data Science can be defined as a multidisciplinary combination of data inference, algorithm development, and technology to solve complex data analysis problems. Data engineers are responsible for configuring the database and storage to facilitate the process of data mining, data collection, and other processes.

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Cloud Computing

Cloud Computing refers to manipulate, configure and access hardware and software resources remotely. It offers online data storage, infrastructure, and applications.

In other words, we can say that Cloud is something that is present in a remote place. The cloud can provide services in public and private networks, for example, WAN, LAN or VPN. Applications such as email, web conferencing, customer relationship management (CRM) are run in the cloud.

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Types Of Cloud 

  • Public Cloud:

    The entire computing infrastructure is located on the premises of a cloud computing company that offers the cloud service.

  • Private Cloud:

    Hosting all your IT infrastructure yourself and not shared. The level of security and control is greater when a private network is used.

  • Hybrid Cloud:

    This type of cloud can be used for both types of interactions – B2B (Business to Business) or B2C (Business to Consumer). This method of implementation is called a hybrid cloud, since computing resources are linked by different clouds.

  • Community Cloud:

    Here, computer resources are provided for a community and organizations.

Cloud Computing Service

Infrastructure-as-a-Service (IaaS)

Infrastructure as a service (IaaS) is an instantaneous computing infrastructure, provided and managed by the Internet. IaaS quickly increases and decreases with demand, allowing you to pay only for what you use. It helps avoid the expense and complexity of buying and managing your own physical servers and other data center infrastructures. Each resource is offered as a separate service component and you only have to rent one in particular for as long as you need.

Platform-as-a-Service (PaaS)

The platform as a service refers to cloud computing services that provide an environment on demand for the development, testing, delivery and management of software applications. It also has several benefits, since it has lower costs and only the user has to pay for essential things. The host of a PaaS has its own hardware and software. This frees the user from installing the hardware and software to run a new application.

Software-as-a-Service (SaaS)

Software as a service is a method to deliver software applications through the Internet, on demand and generally by subscription. With SaaS, cloud providers host and manage the underlying software application and infrastructure and handle any maintenance, such as software updates and security patches. Users connect to the application through the Internet, usually with a Web browser on their phone, tablet or PC.

Data Science With Cloud

A data scientist needs to be an expert in computer science and software programming, written and verbal communication, probability and statistics and business mastery. As computer systems and storage capacity have become increasingly accessible, several solutions now use several computer systems that cooperate together and that are not very exorbitant at scale, rather than dimensioning solutions by acquiring a super powerful computing machine and extremely expensive When a specific group of computer systems is connected to the same network and cooperating with each other to fulfill a similar assignment or set of appointments, it is called a cluster.

A cluster can be thought of as a solitary computer system that can offer huge improvements in performance, availability, and scalability. A cloud describes the circumstance in which an establishment or individual owns, controls and manages a group of networked computer systems and shares resources to provide and host software-based solutions.

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How Data Science is related to the cloud?

If you are well acquainted with the Data Science process, you will perceive that, on a regular basis, the vast majority of Data Science processes are completed in the local Data Scientist team. Mainly R and Python would be installed along with the IDE used by the Data Scientist. The other essential configuration of the development environment is the introduction of related packages through the Anaconda package manager, or by entering individual packages manually.

Cloud Computing And Data Analytics Comparision

Basis for ComparisonCloud ComputingData Analytics


• An infrastructure for the delivery of IT services, available in different models of service and implementation• A structure or tool to process data from various flows to create analytical models to obtain information



• Provides access to IT resources through the Internet

• Involves virtualization and abstraction. The features are availability, robustness, flexibility and scalability to support a variety of IT needs

• Data from various sources is modeled for analysis

• Tools have the ability to model and manage big data sources



Basis of formation

• Cloud service infrastructures provide dynamic IT services to organizations

• IT services are standardized

• Ensure that IT administration costs are reduced

• An outsourced system

• Helps organizations achieve competitiveness

• Data-driven discovery and innovation models data

• Integrates data from multiple sources in real time

• Support to make effective decisions based on real information



Application areas

• Cloud applications are mainly in the delivery of IT services

• Meets various requirements of corporate computing and IT infrastructure

• Implemented by almost all sectors (product and service)

• Cloud services can be customized for all organizations, regardless of its size or scale

• Big data modeling and analysis • Business and personal insights • Health care – disease diagnosis, forecasts

• Solutions for retail

• Understanding consumer behavior

• Finance

• Risk management and fraud detection

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