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Big Data and Data Science, What’s the difference?
While Data Science is more inclined towards Machine Learning and applying machine learning algorithms or models on the data, BigData is more inclined towards analytics, handling large amount of raw data, processing it and observing trends or patterns in the data.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. Its what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
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What is data science?
Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.
Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data.
Because of the large amounts of data modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.
Big Data Definition
Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data philosophy encompasses unstructured, semi-structured and structured data; however, the main focus is on unstructured data. Big data “size” is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many zettabytes of data. Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale.
“Variety”, “veracity” and various other “Vs” are added by some organizations to describe it, a revision challenged by some industry authorities.
A 2018 definition states “Big data is where parallel computing tools are needed to handle data”, and notes, “This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the guarantees and capabilities made by Codd’s relational model.”
The growing maturity of the concept more starkly delineates the difference between “big data” and “Business Intelligence”
Business Intelligence uses applied mathematics tools and descriptive statistics with data with high information density to measure things, detect trends, etc.
Big data uses mathematical analysis, optimization, inductive statistics and concepts from nonlinear system identification to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density to reveal relationships and dependencies, or to perform predictions of outcomes and behaviors.
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Artificial Intelligence vs Machine Learning Vs Deep Learning
What is Artificial intelligence (AI)?
Artificial intelligence is imparting a cognitive ability to a machine. The benchmark for AI is the human intelligence regarding reasoning, speech, and vision. This benchmark is far off in the future.
AI has three different levels:
Narrow AI: A artificial intelligence is said to be narrow when the machine can perform a specific task better than a human. The current research of AI is here now
General AI: An artificial intelligence reaches the general state when it can perform any intellectual task with the same accuracy level as a human would
Active AI: An AI is active when it can beat humans in many tasks
Early AI systems used pattern matching and expert systems.
What is Machine learning (ML)?
Machine learning is the best tool so far to analyze, understand and identify a pattern in the data. One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being. The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.
Machine learning uses data to feed an algorithm that can understand the relationship between the input and the output. When the machine finished learning, it can predict the value or the class of new data point.
What is Deep Learning (DL)?
Deep learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning and is called deep learning because it makes use of deep neural networks. The machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model. Deep learning is the new state of the art in term of AI. In deep learning, the learning phase is done through a neural network. Big Data and Data Science FAQ
Big Data News
Big Data Serve News! A place for information about Big Data. The site consists information on business trends, big data use cases, big data news to help you learn what Big Data is and how it can benefit organizations of all sizes. We promise to keep this landing place fresh with new and interesting information. The site’s goal is to provide you with a wide range of information that will enable you to learn all aspects associated with the Big Data space. We hope that you find our website interesting and informative – and that you will come back and visit on a regular basis.
The site is the industry’s online resource for exclusive stuff on Big Data. This site is dedicated to providing the latest news on Big Data Analytics, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning, Blockchain, Business intelligence, NoSql, Hadoop, MapReduce, Hive, HBase, MongoDB, Cassandra, R programming, Predictive Analytics and etc.
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Embrace the future of data science in your organization with private onsite training from the data experts at Big Data Serve. We’ll help you evaluate your company’s data needs—from a business-focused overview of data’s possibilities to hands-on data science training using the latest open-sourced applications—and find the right private training option for your organization.
If you’ve spent any time in the data science job market today, you know that this isn’t the case any longer.
There are still plenty of headlines about soaring demand for data scientists, but the truth on the ground is that it’s way harder to land a job offer than it used to be. Now that the field’s become more mature, companies are expecting more from candidates. Five years ago, they were asking about decision trees and Naive Bayes; now they assume you know sk-learn and TensorFlow like the back of your hand, and they ask about devops and deployment.
We’ve seen the same thing play out many times with the hundreds of candidates we’ve placed through SharpestMinds. The reason things have changed is that data science is flooded with people who’ve got basic skills and a modest portfolio of personal projects. As a result, employers can afford to be picky with entry-level candidates.
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What is data analytics and visualization?
Data visualization is nothing but, representing data in a visual form. This visual form can be a chart, graphs, lists or a map etc. Data analytics is the method of examining data sets (structured or unstructured) in order to get useful insights to draw conclusions about the datasets.
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Click link below to download dataset for this dashboard. Google Sheets Sales Dynamic Dashboard DatasetDownload
There are five scenarios: two that typically fail, two that sometimes work partially, and one that has emerged as best. Let’s take a look at each: 1. Helping for solving problems? This scenario often starts with the CEO (sometimes prompted by the board) deciding to...
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