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AWS EC2 Real Story on Elastic Cloud Computing

The short name for Amazon Elastic Computing Cloud is EC2. You can keep this point as an interview question. The computing capacity has an elastic property. Based on your requirement you can increase or decrease computing power.
You need to be very attentive when you enable Auto scaling feature. It is a responsibility on Admins. Amazon AWS EC2Making your existing hardware to the requirement, always is not so easy. So the EC2 feature in AWS helps you to allocate computing power according to your needs. AWS EC2 instance acts as your physical server.It has memory.You can increase the instance size in terms of CPU, Memory, Storage and GPU.EC2 auto scaling is a property, where it automatically increase your computing power. List of Top Security Features in EC2  1#. Virtual Private CloudThe responsibility of Virtual Private Cloud, is to safeguard each instance separately. That means, you cannot access others instance, which is already created by other organization.
2#. Network Access Control L…

Data science career these 4 top skills absolutely you need

The data science is a combination of technical and general skills. As a analyst you need to provide valuable information to the client. The below is highly useful list of skills.

Top Skills You Need for Data Science Career


data science

1# Paradigms and practices

This involves data scientists acquiring a grounding in core concepts of data science, analytics and data management. 

Data scientists should easily grasp the data science life cycle, know their typical roles and responsibilities in every phase and be able to work in teams and with business domain experts and stakeholders. 

Also, they should learn a standard approach for establishing, managing and operationalizing data science projects in the business.

2# Algorithms and modelling

Here are the areas with which data scientists must become familiar:
  • linear algebra, 
  • basic statistics, 
  • linear and logistic regression, 
  • data mining, 
  • predictive modeling, 
  • cluster analysis, 
  • association rules, 
  • market-basket analysis, 
  • decision trees, 
  • time-series analysis, 
  • forecasting, machine learning, 
  • Bayesian and Monte Carlo Statistics, 
  • matrix operations, 
  • sampling, 
  • text analytics, 
  • summarization, 
  • classification, 
  • primary components analysis, 
  • experimental design and unsupervised learning-constrained optimization.

3# Tools and platforms

Data scientists should master a basic group of modeling, development and visualization tools used on your data science projects, as well as the platforms used for storage, execution, integration and governance of big data in your organization.
Depending on your environment, and the extent to which data scientists work with both structured and unstructured data, this may involve some combination of : 

  • data warehousing, Hadoop, stream computing, NoSQL and other platforms. 
  • It will probably also entail providing instruction in MapReduce, R and other new open-source development languages in addition to SPSS, SAS and any other established tools.

4# Applications and outcomes

A major imperative for data scientists is to learn the chief business applications of data science in your organization, as well as ways to work best with subject-matter experts. 
  • In many companies, data science focuses on marketing, customer service, next-best offer and other customer-centric applications. 
  • Often, these applications require that data scientists know how to leverage customer data acquired from structured survey tools, sentiment analysis software, social media monitoring tools and other sources. 
  • Plus, every data scientist must understand the key business outcomes—such as maximizing customer lifetime value—that should be the focus of their modeling initiatives.

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