Big Data is big news these days. There is continuous streaming of Data, flooding each business on a given day. This Data consists of information that is collected from a huge number of sources, such as, Production, Market Prices, Finance, Material Flow, Supply Chain, Security Data and so on. These huge masses of Data may be Structured and Unstructured. But it is not the quantity of Data that directly interests the Data Scientists or the organizations that generate and collect them. It is what this Data can yield which may lead to better insights and strategic decisions for the whole organization. What follows next is therefore a brief Introduction To Big Data, its workings and its capabilities.
A Little History
Big Data as a fore-runner of a new branch of knowledge called Data Science, first gained currency in the early 2000s. In 2001, Industry Analyst Doug Laney first coined the term ‘Big Data’ and the ‘3Vs’ of Volume, Velocity and Variety. These 3Vs now form the core definition of Big Data.
How Does it Work?
Big Data is said to consist of the 3Vs standing for Volume, Velocity and Variety. Let us discuss each V first, as follows:
- Volume: Data is streamed from various sources and storage of this mass of information had become a major problem. Data is collected, by the organizations, from Industrial Equipment, Videos, Social Media, Business Transactions, Smart IoT (Internet of Things) Devices and many more. But modern storage that is at once cheap and capable of absorbing vast quantities of Data, like, Data Lakes and Hadoop, have made such storage a possibility at last.
- Velocity: The Velocity of Data streaming has suddenly entered a phase of rapid upsurge, riding on the back of the IoT. These speeds were truly un-thought of previously. With Censors, Smart Meters and RFID Tags pushing the boundaries of collecting masses of Data in real time, the need for rapid collection of Data is foremost.
- Variety: Data these days stream in with a vast Variety of formats. Traditional Data Bases providing Structured Data are more easily absorbed, but Unstructured Data, such as, Ticker Tape Data, Stock Figures, Audios, Videos, Emails and Financial Transactions create a mixed bag that needs special storage.
Two other Vs are also selected as important for Big Data. These are:
- Variability: The Data itself is often changeable and sometimes seasonal. In order to predict Trending, on a daily basis, Social Media and other such sources of Data generation need to be considered on Variable platform.
- Veracity: This is an important feature which evaluates the quality of the Data. In order to confirm the Veracity or Truth about the quality of Data received and it usefulness, businesses must be able to figure out relationships and linkages of Data on a mass scale. Otherwise overall control of Big Data may soon be lost.
Impact and Importance
In Big Data, the quantity of Data collected is less important than how the Big Data is handled and what it is used for. Analysis of Data can enable the Data Analyst to guide organizations to produce, firstly, Cost Reductions and Time Reductions. New products can be developed and optimized. Decision making thus becomes smart. Powerful Analytical Tools used by Data Scientists can yield insights on the following:
- Root causes of failures and defects can be determined in close to real time.
- The entire Risk Portfolio can be re-calculated in a matter of minutes.
- Fraudulent functioning can be detected before the organization is affected, or at its early stages.
The Future of Big Data
The future belongs to Big Data and DI (Data Integration). With so many different types and sources of Data, with operational time varying from Real Time to Streaming Time, Data Integration uses Data Science to extract meaningful insights by mining Big Data. This Introduction To Big Data is only a brief glimpse of how our future is being built by Big Data and Analytical Strategy. A future can be conceived where Cloud, Containers and On-demand Computation Power can create a situation, where organizations can depend fully on the reliability of the Big Data-driven decisions across lines of business. Big Data is our Business’s staircase to a Big Future.
This age is considered to be the Age of ‘Big Data’, much as the previous age was considered to be the Digital Age. Since the middle of the last century, the word Data has been used to mean computer information which is formatted in a special way that can be transmitted, stored and used for calculation. With the entry of the world of Big Data, the need for immense storage capabilities were felt and this situation continued until 2010. But now, ‘Hadoop’ and similar frameworks have solved the need for storage, and Big Data Processing is the next phase. The science derived from Big Data, called ‘Data Science’, is a new interdisciplinary field that uses Science to create methods, processes, systems and algorithms to extract insightful knowledge from large masses of both Structured and Raw Data. With the omnipresence of the Online, physically located schools for training are becoming time-consuming and therefore outdated. Online Schools for Data Science are now becoming accessible everywhere, and this gives our Generation X a wonderful opportunity to learn and practice a brand new profession, that is at the cutting-edge of information technology.
Why Is Data Science necessary?
Most of the Data that has been acquired, traditionally, has been comparatively small in size, and generally structured. Simple BI (Business Intelligence) tools were sufficient to process this Data. But as time goes on, the Data to be processed is mostly Unstructured Data, or at best Semi Structured. Data trends indicate that within the next few years, more than 75% to 85% of all Data will be Unstructured or Semi Structured. The bulk of this data will be generated from varied sources, with complex or no obvious interactions like — Financial Logs, Multimedia Forms, Text Files, Sensors, and direct inputs from instruments. This vast and varied mass of Data cannot be processed by the simple BI Tools that have been previously available. A vastly more advanced, complex and a complete science is required to tackle this Big Data. This is Data Science which can be used for predictive analytics. Weather Forecasting is a typical example. Data from multiple sources, such as, Satellites and Land Weather Stations, Ships and Aircrafts, Radars and Weather Balloons, can be collected and analyzed to build models that will not only forecast weather, but also help predicting the intensity, timing and route of major natural calamities. Customer’s preferences can be pinpointed from analysis of existing purchase history, age and income of the Customer. Companies also use valuable date to improve their products and services. For example adult apps like fuckbook and snapfuck among other local fuckbuddy apps can analyze data from their users to improve the functioning of their platforms. Data can be so pinpoint that they can specify to improve functioning for casual sex seekers using the mobile app in a specific location or age group of any other piece of data really.
The following background is necessary for Data Science Candidates – Statistics, Machine Learning, Programming with ‘R’ and ‘Python’, Multivariable Calculus and Linear Algebra, Data Wrangling, Visualizations and Communications and Intuition. The Candidate must have a Bachelor’s Degree in Mathematics, Physics, Computer Science, IT or similar discipline, and preferably Master’s Degree in Data and some experience in the expected field of work.
Online Schools are now available in almost all disciplines, sometimes in even the most advanced or cutting-edge fields. It is not surprising therefore that Data Science has gone Online, in a big way. But the general public is only dazzled by the paycheck for Data Scientists. They are not aware of the tough pre-qualification requirements to study this highly advanced field of learning (which has been outlined above). Some of the best sponsored
Online Schools for Data Science follows:
- University of California, Berkeley: Berkeley offers innovative program features like – Live Face-to-face Online Classes with Self-Paced Course work, MIDS Faculty-Administered Rigorous, Relevant Curriculum and finally a Degree from UC Berkeley, all without having to re-locate.
- University of Denver: The program from the Daniel Felix Ritchie School of Engineering and Computer Science can be attended by students without prior programming experience, by using three bridging courses.
- Southern Methodist University: Successful candidates will benefit from SMU’s vast connection to global business communities across a range of industries. It offers Project-based Approach, In-person Immersion, Interdisciplinary Curriculum and Live Online Classes.
- Syracuse University: Data Science at Syracuse is an 18-month Online Graduate Program featuring opportunities to network with Peers and Faculty, Face-to-face Online Weekly Classes, Immersive Course work fostering close collaboration.
There are several others offering Online courses like Bay Path University, Bellevue University, Cabrini University, CTU, CUNY, Drexel University, Elmhurst College, IIT Illinois and so on, which are also worth considering.
There are many people who are interested in land in the field of data science. It is an easy-to-read subject that looks interesting for many graduate students who come from a wide range of backgrounds. To deep dive into the world of data science the candidates need to master the basic concepts of data science from a reputed institution that possess skilled and trained instructors. To gain more knowledge read this article as it covers everything you need to know right from the introduction to data science and the true benefits of learning this skill.
Data science is related to that form of study from where the information comes and how it is represented.
What are the basics of Data Science?
The data science field thus includes disciplines such as computer science, statistics, and, mathematics and also incorporates techniques like visualization, group analysis, data mining, and machine learning. Data science is that discipline, which encompasses statistics and related branches of mathematics, machine learning and other analytic processes, that increase borrows from high-performance scientific computing, to extract future insights from data to address new information or data.
What is the need to learn Data Science?
If you aspire to become a Data Scientist it is one of the essential knowledge which you need to master in order to execute the tasks using its skills to tackle real-world data analysis issues and challenges. Data Science is also related to machine learning which helps the participants to build up various skills that help the users to learn other programming essentials so that they can stay ahead of their competitors.
Not only this if the individual wishes to become a data scientist then they need to make a positive contribution to bring out the changes to help the society. Data science is sure to offer you attractive superpowers that are beyond once imagination. One of the major concerns right now is to restructure the industries in the field of healthcare as many people with inadequate facilities in rural areas are losing their lives.
Data scientists should also possess an amalgamation of statistical skills, data mining, machine learning, and, analytic, and hold experience in coding and algorithms. In addition to manage and interpret large data, most of the data scientists are skilled to handle the tasks that include creating a model for data visualization to help demonstrate the digital information’s business value.
Data scientists are required to draw digital information from various sources. They are required to utilize the list of growing channels that include social media, electronic gadgets such as smartphones, and internet of things (IoT) devices, surveys, purchases, internet behavior and searches.
I hope you got an idea of how data science is essential to face real-world problems. Many data scientists are thus working hard to recognize patterns that will bring solutions to varied issues by way of data mining. At present Data Science is used to reach company goals across a number of industries from agriculture to dating apps like tinder and banking institutions are utilizing mining data to boost fraud detection which helps refine and identify the right audiences.