Full series Part 1 - What is Data Science, Big data and the Data Science process Part 2 - The origin of R, why use R, R vs Python and resources to learn Part 3 - Version Control, Git & GitHub and best practices for sharing code. Part 4 - The 6 types of data analysis Part 5 - The ability to design experiments to answer your Ds questions Part 6 - P-value & P-hacking Part 7 - Big Data, it's benefits, challenges, and future
This series is based on the Data Science Specialization offered by John Hopkins University on Coursera. The articles in this series are notes based on the course, with additional research and topics for my own learning purposes. For the first course, Data Scientist Toolbox, the notes will be separated into 7 parts. Notes on the series can also be found here.
Before the internet, information was in some ways restricted and more centralized. The only mediums of information were books, newspapers, and word of mouth, etc. But now with the advent of the internet and improvements to computer technology (Moore’s Law), information and data skyrocketed, and it has become this decentralized system, where information can be distributed to people without any kind of limits. As the internet became more accessible and world-wide, social mobile applications and websites gradually grew to become platforms for sharing data. Data, along with many other things, grows in value as an increase in size, where this value is applied in many ways, but mostly for analytics and making decisions. Here’s more about Big Data.
Big Data can be defined as large amounts of data, both structured and unstructured, usually stored in the cloud or in data centers, which are then utilized by companies, organizations, startups, and even the government for different purposes.
To utilize data means cleaning it and then analyzing it, forming patterns and connection, trends and correlations, to produce insights. This is what’s called Big Data analytics.
Big Data is also commonly described by its qualities, also known as the 4Vs
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.
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Big Data has played a major role in defining the expansion of businesses of all kinds as it helps the companies to understand their audience and devise their business techniques in accordance with the requirement.
The importance of ‘Data’ has been spoken very highly in the modern-day business. Thus, while using big data analysis, the companies must keep away from these minor mistakes otherwise it could have a major impact on their performances. Big Data analysis can be the silver bullet that can answer your questions and help your business to scale newer heights.
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The rapid growth of technology has led to many people opting for online services, and thus the collection and maintenance of data becomes a significant factor for any company. Big data analytics service providers can help the companies get a massive edge over their competitors as they would manage the data well and allow the businesses to make better business decisions. It will provide you with a combination of increased customer experience, revenue, and reduced cost and thus will create a win-win situation for your business. Big data technologies will be your perfect ally in excelling in the cut-throat business environment and come out with flying colors.
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An extensively researched list of top Microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.
An exclusive list of Microsoft Big Data consulting and solution providers, after examining various factors of expert big data analytics firms and found the equivalent matches that boast the ace qualities with proven fineness in data analytics. For business growth and enterprise acceleration getting inputs from the whole data of the organization have become necessary, thus we bring to you the most trustworthy Microsoft Big Data consultants and solutions providers for your assistance.
Let’s take a look at the List of Best Microsoft big data solutions Companies.
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