What is Data Science & How Does Data Science Works?

 

What is Data Science?

Data Science continues to be a hot topic among skilled professionals and organizations that are focusing on collecting data and drawing meaningful insights out of it to aid business growth. A lot of data is an asset to any organization, but only if it is processed efficiently. The need for storage grew multifold when we entered the age of big data. Until 2010, the major focus was towards building a state of the art infrastructure to store this valuable data, that would then be accessed and processed to draw business insights. With frameworks like Hadoop that have taken care of the storage part, the focus has now shifted towards processing this data. Let us see what is data science, and how it fits into the current state of big data and businesses. 

Data Science Definition

Broadly, Data Science can be defined as the study of data, where it comes from, what it represents, and the ways by which it can be transformed into valuable inputs and resources to create business and IT strategies. 

what is data science graphical representation
(Source: datascience@berkeley)

Why businesses need Data Science?

We have come a long way from working with small sets of structured data to large mines of unstructured and semi-structured data coming in from various sources. The traditional Business Intelligence tools fall short when it comes to processing this massive pool of unstructured data. Hence, Data Science comes with more advanced tools to work on large volumes of data coming from different types of sources such as financial logs, multimedia files, marketing forms, sensors and instruments, and text files.

Read more

Comments

Post a Comment

Popular posts from this blog

What is Deep Learning? What are its applications?

Image
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning concepts are used to teach machines what comes naturally to us humans. Using Deep Learning, a computer model can be taught to run classification acts taking image, text, or sound as an input. Deep Learning is becoming popular as the models are capable of achieving state of the art accuracy. Large labelled data sets are used to train these models along with the neural network architectures. Simply put, Deep Learning is using brain simulations hoping to make learning algorithms efficient and simpler to use. Let us now see what is the difference between Deep Learning and Machine Learning. Deep Learning vs. Machine Learning How is Deep Learning Used: Applications Deep Learning  applications have started to surface but have a much greater scope for the future. Listed here are some of the deep learning applications ...
Read more

Artificial intelligence against Diabetes and COVID-19

Artificial Intelligence  has multiple applications in the healthcare domain. We have also seen many AI solutions being developed to identify symptoms of COVID-19 among patients. Here’s one such case of AI-based screening of visitors at Mumbai railway stations to identify  COVID-19  symptoms. Also, the application of Artificial Intelligence in adjusting insulin dose to control glucose levels among Type I Diabetes patients. AI-based COVID-19 screening at Mumbai stations Body-screening facility “FebriEye thermal cameras” have been set up at Chhatrapati Shivaji Maharaj Terminus and Lokmanya Tilak Terminus in Mumbai to scan passengers for  COVID-19  symptoms. FebriEye is an AI-based thermal screening system for real-time and non-intrusive thermal monitoring. It ensures that a person entering the premises does not have a high fever. It uses heat sensors that record the heat generated by a human entity and creates a 2D image showing different temperature levels. The Fe...
Read more

Is a mid-career MBA the right choice for me?

  Introduction While many suggest pursuing an MBA degree in the early 20s, as one has lesser responsibilities and is yet to enter the work environment, some beg to differ. As per the Graduate Management Admission Council reports, the proportion of candidates for the two-year full-time MBA program having work experience of more than six years rose from 24% to 51%. The bi-fold increase in the percentage indicates the preference for an MBA degree while individuals in the mid-stage of their professional career.  MBA isn’t for everybody but can add tremendous value for those willing to pursue it. While the statistics have their say, the real question that arises is, ‘Is a mid-career MBA worth it?’. Here are a few checkpoints that will help you decide and conclude. Know more about  mba courses What is your motive for pursuing a mid-career MBA? Being crystal clear about your motive will save you a lot of time and headache as an MBA degree requires proper commitment and is time-c...
Read more