Difference between Big Data and Data Science

We live in a digital era. In this era, data plays a significant role in the industry, and almost every company produces a vast amount of data. The volume of digital data generated is growing at a rapid rate. This was not expected initially. But, industries are witnessing that the data collected is getting doubled every two years.

According to the article of Forbes reports, it is concluded that by the end of 2020, almost 1.7 megabytes of new information will be created every second for each human being that exists on this planet. To handle such a massive volume of data, we need to know at least the basics of this field. Our future lies in this field itself, and therefore this field is crucial for us. 

Today, we are going to differentiate Data Science from Big Data in several aspects. They are separated based on a few factors such as what they are, skills required to become a professional in the field, prospects of salary in each area, etc. 

First,nunderstand what the two concepts are, and then we will go more in detail. 

What is Big Data?

Big Data is referred to as a volume of data that is not easily handled, and that cannot be quickly processed. It is not possible to process that large volume of data with the help of traditional applications. Big Data is prepared to start from raw data, that is not aggregated. The amount of data is so significant that it is impossible to store the available information in the memory of one computer. 

Big datanconsists of both structured and unstructured data. The buzzword is used tondescribe the immense volume of data present. Businesses are inundated by bigndata almost daily. Big data is generally used to analyze insights that often lead to better decisions and several business moves that are strategicnconcerning the business. 

A prevalent definition of Big Data is given by Gartner, which says "Big Data is high volume or High-velocity, or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation."

  What is Data Science?

As farnas unstructured and structured data is concerned, data science deals with them.nIt is a field that consists of everything related to preparation, datancleansing, and analysis. 

Data science skills a mixture of several aspects including mathematics, statistics, problem-solving, programming, capturing data in ingenious ways, etc. It is much more than what people expect. It provides the capability of looking at things differently, and the recreation of preparing, cleaning, and also aligning available data. 

To explain simply, Data Science is an umbrella term used for varied techniques that are utilized to heave insights and other relevant information from the data. 

Afternunderstanding the basics of the two concepts, we will learn about thenapplications of each. Both data science and big data are used for severalnapplications to provide maximum benefits. 

Application of Data Science

• Digital Advertisements - the entire spectrum of digital marketing uses algorithms of Data Science. There are several areas where data science is implied so that marketers get accurate results. It is used right from billboards to display banners. Data science makes it possible for digital advertisements to get higher CTR as compared to the traditional ones. 
• Internet Search - Data Science is used by search engines as well. By using its algorithms, search engines deliver accurate results based on the query made in just a fraction of seconds. 
• Recommender Systems - Recommender systems work in a way that it helps people to find products from the list of billions of products available. This enhances user experience. Companies are using such systems to expand their reach for products, and also similar suggestions. This is done keeping in mind the demand of consumers and relevant information available. Recommendations are made based on searches made by consumers. 

Application of Big Data

• Big Data on Communications - Retaining Consumers, Gaining new subscribers, and enhancing operations among the current subscribers are a few priorities of a telecommunication company. However, such challenges do have solutions, and to solve such problems a company requires customer-generated data.
•  Big Data for Financial Services - Companies that provide financial services such as credit card companies, private wealth management advisories, venture funds, retail banks, etc. use big data to provide financial services. However, they generally face a common problem that occurs because of the massive amount of multi-structured data that lives in several disparate systems. All such problems are easily solved through big data. Big data is hence utilized in several ways such as Fraud Analytics, Customer Analytics, Compliance Analytics, Operational Analytics, etc. 
• Big Data for Retail - Whether it is an offline or online store, to stay competitive and in the game, it is required for a business to understand its consumer better. By doing this, they can serve them in a better way. This is only possible when a company has the capability of analyzing varied sources of data with which a company deals every day. The sources include consumer transaction data, web blogs, loyalty program data, social media, etc. 

We just saw that both concepts have several applications in varied fields.

If you are planning to take up either field for your future, then there are a few things that you need to know. There is a separate set of qualifications required to work in either area. So to choose anyone, you must know the skills that are needed to work in the respective field. 

Withoutnfurther ado, let us know the different skills that are required to becomeneither a data scientist or a prominent data analyst. 

Skills required to become a Big Data Specialist

• Creativity: A Big Data Specialist should have the capability of creating new techniques to gather data, interpret and analyze it for a data strategy. This skill is extremely suitable for a Big Data Specialist to possess. 
• Computer Science: Every data strategy is possible because of the workhorses that are computers. Hence, programmers are required to continually emerge with algorithms that help them to process data into insights. 
• Analytical Skills: This is referred to the capability of making sense to keep the piles of data that a business gets. Analytical skills help to determine the data that will be relevant to the solution you someone is looking for. It is more like the ability of problem-solving. 
• Mathematical and Statistical Skills - An old fashioned-skill that is hugely beneficial for crunching numbers. These skills are required and necessary whether it is on data science, big data, or data analytics. 
• Business Skills: Professionals opting for Big Data as a career should have the ability to understand the different business objectives. The objectives that are present there should be known, and all the underlying processes that drive a business to enhance, and its profit margins grow as well. 

Skills required to become a Data Scientist

• Education: Usually, more candidates have a Master's degree. Others have https://nygoodhealth.com PHDs. 
• Hadoop Platform: Although Hadoop is something that is not so vital, but for the field if a person knows Hadoop, it is an additional benefit. Additionally, if the candidate is experienced in Pig or Hive, then it is another huge advantage. 
• Python Coding: Python is one of the standard coding languages used. It is used in Data Science, and along with it Perl, Java, C++ are used. 
• Unstructured Data Working: A data scientist needs to be able to work with unstructured data. No matter whether it is on Video feeds, Audio, or Social Media. 
• SQL Database/ Coding: Hadoop and NoSQL are indeed the most significant parts of the Data Science background. SQL is still preferred in the field if a candidate can write and then execute complex queries comprehend.

Big Data vs Data Science Salary

Since the two fields are different in several aspects, the salary considered for each track is different. Although the concepts are from the same domain, the professionals of these platforms are believed to earn varied salaries. Whether it is a Data Scientist or Big Data Specialist, different salaries are collected in respective fields. 

• According to Glassdoor, the average salary a data scientist earns is almost equal to $108,224 annually. 
• Again looking at the stats of Glassdoor, the average salary earned by a prominent data specialist is believed to be $106,784 every year. 

Eachnprofessional is believed to give the best possible performance. Payscalenincreases depending on the knowledge and expertise a candidate brings to thentable. 

Key Differences between Data Science and Big Data

Therenare several significant differences between Data Science and Big Data. The twonaspects are different from each other. Till now, we have known the basics andndetails of these two platforms. But, when we talk about the differences,nseveral points are taken into consideration. 

Structured and Unstructured data is collected and used by organizations. It is convenient for organizations to understand structured data, but when it comes to unstructured data, personalized modeling methods are required. 

Let us know the key points of differences between Big Data and Data Science. 

Basis	Data Science	Big Data
Meaning	Skewed towards the scientific approach of interpreting the data and retrieves the information from a given data set.	Revolves around the huge volumes of data which cannot be handled using the conventional data analysis method.
Concept	Obtained with big data is heterogeneous that indicates a diversified data set which has to be per-cleaned and sorted before running analytics on them.	Scientific techniques to process data, extract information and interpret results which help in the decision-making process.
Formation	Internet users/ traffic, live feeds, and data generated from system logs	Data filtering, preparation, and analysis
Application areas	Internet search, digital advertisements, text-to-speech recognition, risk detection, and other activities	Telecommunication, financial service, health and sports, research and development, and security and law enforcement
Approach	Uses mathematics and statistics extensively along with programming skills to develop a model to test the hypothesis and make decisions in the business	Used by businesses to track their presence in the market which helps them develop agility and gain a competitive advantage over others

Anprofessional may not find any significant differences between the two concepts,nbut they have always instigated the mind of several people. They are often leftnin a dilemma regarding which one relates to what. The real point of differencesnbetween the two has been revealed in a much elaborative manner. You must havenby now understood the core concepts of the two varied aspects. They arendifferent from each other. 

The Data Science course is more of an evolutionary extension of statistics that deals with more massive datasets. This is done with the help of technologies in computer science. People often tend to confuse data science with machine learning, but in fact, they are both different. Machine Learning is regarded as a subset of data science, and they are entirely different. 

Big Data, on the other hand, deals with a massive collection of heterogeneous data from varied sources and is generally not accessible in the standard database. The formats that we are usually aware of don't work for big data. This simply implies that you cannot tabulate the available data in a table or chart form if you want to do so. 

Data is classified into two different categories by Big Data. However, there is a third category as well that comes into the picture when half of the processing of the available data is done. The two classifications are structured data and unstructured data. The third category is known as the semi-structured data. 

The different kinds of data are collected from various sources-

• Unstructured Data - It is collected from social networks, digital images, blogs, emails, content, etc. 
•  Structured Data - It is generated from OLTP, RDBMS, and other structured formats of data. 
• Semi-Structured Data - XML Files, Text Files, etc. are the types of semi-structured data. 

It is effortless to understand structured data but to understand unstructured data, different methods and techniques are used. Unstructured data is of no use until it is being processed using different algorithms of data science. 

The approach of Big Data is not achieved easily using traditional data analysis methods. Big Data is processed using the approach of Data science, which applies several different approaches, including statistics, mathematics, etc. Multiple areas are combined into a single plan to obtain maximum benefit. Data science is the only concept that makes it possible to process Big Data. It collects useful information that the available data holds. 

Conclusion

The field of data science and big data are both emerging. We are living in an era where nothing is possible without data. Hence, Big Data will stay for years to come, and there is no denying the fact that it will improve more.

If younare planning to take up a field as a career option, then you are in the rightndirection. The grounds have a massive scope, and with such career options, younwill only succeed.