Big Data vs Data Science

The Big Data approach can not easily be accomplished by using conventional methods of data analysis. Instead, unstructured data requires advanced techniques, software, and frameworks for data modelling to extract knowledge and information when organizations need it. Data science is a scientific methodology that applies big data processing to mathematical and statistical ideas and computer tools. Data science is a major discipline that integrates several disciplines to prepare and align big data for intelligent analysis to extract knowledge and information, such as statistics, mathematics, intelligent data capture techniques, data cleansing, mining and programming. This blog is planned to provide you with a glance to discriminate both these disciplines and aids you in understanding the main terms such as what is big data, why big data, what is data science, why data science. You will also comprehend the applications and skills required to become a big data specialist or data scientist, along with key differences between them. Let’s start exploring the concepts.

What is Big Data?

Big data refers to large volumes of data that cannot be easily stored, unlike the conventional applications frequently employed. Big data analysis starts from raw data that is not aggregated and is most often difficult to store in a single computer's memory.

Big data can deluge a corporation daily, and a buzzword used to describe huge volumes of data, both unstructured and structured. Big data is used to analyze insights that can contribute to better decisions and strategic business movements.

Gartner gives the following description of big data: "Big data is high-volume and high-speed or high-variety information assets that involve cost-effective, advanced ways of analysis of information that allow better insight, decision-making and automation of processes."

Why Big Data?

Big Data lets businesses generate potential avenues for innovation and whole new types of firms that can integrate and interpret data from the market. These businesses provide sufficient knowledge to be collected and evaluated about products and services, customers and vendors, consumer choices.

What is Data Science?

Data science, dealing with unstructured and structured data, is an area that encompasses anything relating to data cleaning, preparation, and analysis.

Data science is a blend of statistics, mathematics, programming, problem-solving, ingeniously collecting data, the ability to look at problems differently, and data cleaning, preparation, and alignment of data. This umbrella term encompasses different approaches that are used to collect data from insights and facts.

Why Data Science?

Data scientists are employed to collect, pre-process and interpret data. Enterprises will make better decisions through this process. Various firms have their standards and use the information accordingly. Ultimately, Data Scientist's goal is to help organizations develop better.

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Distinguishing the Big Data and Data Science

Presently, all of us are experiencing an incredible growth of worldwide and Internet-generated knowledge that contributes to the idea of big data. Because of the complexities involved in integrating and applying various methods, algorithms, and complicated programming techniques to perform intelligent analysis in large data volumes, data science is quite a challenging field. Therefore, the domain of data science has evolved from big data, or it is inseparable from big data and data science.

This description refers to the broad set of heterogeneous data from various sources and is usually not available in traditional database formats that we are commonly aware of. Big data incorporates all forms of information that can easily be found on the internet, including structured, semi-structured, and unstructured data.

Big data comprises:

  • Unstructured data –Social networks, emails, blogs, tweets, digital images, online data sources, mobile data, sensor data, web pages, digital audio/video streams, etc.
  • Semi-structured – XML files, system log files, text files, etc.
  • Structured data – RDBMS (databases), OLTP, transaction data, and other structured data formats.

Both information and data can thus be known as big data, regardless of its type or format. Typically, the processing of big data starts by aggregating data from different sources.

Meaning

Big Data:

  • Large volumes of data that can not be dealt with using conventional programming for databases.
  • Typified by volume, range, and speed.

Data Science:

  • A data-focused on scientific activity.
  • Big Data Processing Methods.
  • Exploits the power of big data for business decisions.
  • Similar to data mining.

Concept

Big Data:

  • Varied types of data generated from multiple sources of data.
  • Comprises all data formats and types.

Data Science:

  • A technical field comprising scientific programming tools, models, and big data processing techniques
  • Offers tools for extracting insights and facts from huge datasets.
  • Supports organizations in decision-making.

Information Basics

Big Data:

  • Internet users/traffic.
  • Electronic devices (sensors, RFID, etc.)
  • Streams of audio/video, including live streams.
  • Online discussion forums.
  • Data generated in organizations (transactions, DB, spreadsheets, emails, etc.)
  • Data generated from system logs.

Data Science:

  • Implements the scientific strategies for extracting knowledge from big data.
  • Data filtering, preparation, and analysis related.
  • Track and build models of complex patterns from big data.
  • Working programs are built by coding the models developed.

Areas of Application

Big Data:

  • Financial services.
  • Telecommunications.
  • Optimizing business processes.
  • Performance optimization.
  • Health and sports.
  • Improving commerce.
  • Research and development.
  • Security and law enforcement.

Data Science:

  • Internet search.
  • Digital advertisements.
  • Search recommenders.
  • Image/Speech recognition.
  • Fraud, risk detection.
  • Web development.
  • Other miscellaneous areas/utilities.

Approach

Big Data:

  • To build agility in the market.
  • Competitive advantage to gain.
  • Leverage datasets for company benefits.
  • Develop practical ROI and metrics.
  • To attain sustainability
  • Understanding business and gaining new clients.

Data Science:

  • Broad usage of mathematics, statistics, and other frameworks are involved.
  • State-of-the-art database mining techniques/ algorithms.
  • Skills for Programming (SQL, NoSQL), Hadoop Systems.
  • Acquiring, preparing, processing, publishing, maintaining, or deleting data.
  • Data visualization, prediction.

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Key differences between Big Data and Data Science

Some of the key distinctions between the conceptions of big data and data science are presented below:

  • To enhance productivity, enterprises require big data to comprehend emerging opportunities and maximize the competition. At the same time, data science offers the tools or processes to understand and use the potential of big data promptly.
  • For enterprises, there is no limit on the amount of useful data that they can gather. Still, data science is required to leverage all this data to obtain relevant knowledge for operational decisions.
  • Big data is distinguished by its variety and volume of velocity (popularly known as 3Vs), while data science offers methods or strategies for processing 3V data.
  • Big data offers performance opportunities. Even so, it is a daunting challenge to pull out insight data from big data to use its performance optimization ability. In addition to deductive and inductive logic, data science uses analytical and experimental methods. It is the job of revealing all secret informative data from a complex mesh of unstructured data to help companies understand big data's value.
  • Big data processing requires the extraction of valuable data from vast volumes of databases. Data science makes use of machine learning algorithms and statistical methods, as opposed to analysis, to train the computer to learn without any programming to make big data predictions. Therefore, data science must not be misunderstood with the analysis of big data.
  • Big data applies mostly to technology (Hadoop, Java, Hive, etc.), distributed computation, and analytics methods and applications. It is opposed to data science, which emphasizes business decision strategies, data dissemination using mathematics, statistics, data structures, and previously listed approaches.
  • It should notice from the discrepancies mentioned above between big data and data science that the concept of big data requires data science. In several application areas, data science plays a significant role. In a predictive analysis where outcomes are used to make wise decisions, data science works on big data to gain practical insights. Hence, instead of the other way round, data science is used in big data.

Applications of Big Data

Big Data for Financial Services

Big Data is used by credit card firms, retail banks, private asset management consultancies, insurance companies, venture funds, and institutional investment banks for their financial services. Huge volumes of multi-structured data residing in several fragmented structures that big data can solve are the common challenge for all of them. Big data, as such, is used in many ways, including:

1. Customer analytics.
2. Compliance analytics.
3. Fraud analytics.
4. Operational analytics.

Big Data in Communications

For telecommunications service providers, adding new users, maintaining clients, and expanding existing user bases are top targets. The answers to these problems lie in integrating and interpreting the volumes of data produced by consumers and data created by computers that are generated every day.

Big Data for Retail

The key to keeping in the game and being successful is considering the client better, whether it is a brick-and-mortar company or an online retailer. It includes the ability to examine all diverse data streams, including weblogs, retail purchase data, social media, store-branded credit card data, and loyalty program data, which businesses struggle with every day.

Applications of Data Science

Healthcare

The key challenge for hospitals is to handle as many patients as they can safely while still offering a high level. Increasingly, instrument and computer data are used to control and optimize patients' flow, treatment, and equipment used in hospitals. Incorporating software from data analytics firms shows that there would be a one percent productivity benefit that will produce more than $63 billion in global healthcare savings.

Travel

Data analytics can enrich the shopping experience through mobile/weblog and social media data collection. Websites for travel may provide insights into the interests of the consumer. Personalized packages and deals can upsell items by correlating current purchases to the corresponding browsing boost in browse-to-buy conversions. Personalized travel suggestions can also be delivered by data analytics based on social media data.

Gaming

To optimize and invest within and through games, data analytics helps collect information. It is also possible for gaming businesses to understand more about what their customers like and dislike.

Energy Management

Most businesses use data analytics for energy efficiency, including smart grid management, energy optimization, energy distribution, and building automation for utility companies. The application here focuses on the control and surveillance and the handling of service outages, network equipment, and dispatch crews. Utilities can incorporate millions of data points into the output of the network and provide engineers the chance to track the network using analytics.

Skills required to become a Big Data Specialist

Analytical skills: These skills are important to make sense of knowledge and decide which information is significant while reporting and searching for solutions.

Creativity: To collect, interpret, and analyze a data plan, you need to develop new approaches. 

Mathematics and statistical skills: Good, old-fashioned “number crunching” is also essential, be it in data science, data analytics, or big data.

Computer science: The foundation of any data strategy is computers. Programmers would need to come up with algorithms to turn knowledge into ideas continuously.

Business skills: Big data experts would need to know the strategic objectives in place and the underlying processes that fuel the company's growth and earnings.

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Skills required to become a Data Scientist

Education: 88% have master's degrees, and 46% have PhDs.
In-depth knowledge of SAS or R. Typically, R is preferred for data science.

Python coding: Python, along with Java, Perl, and C/C++, is the most common scripting language used in data science.

Hadoop platform: Recognizing the Hadoop platform is also preferable for the industry but not necessarily a prerequisite. It is also helpful to get some experience in the Hive or Pig.

SQL database/coding: Even though NoSQL and Hadoop have become an integral part of data science, it is still preferred if you can write and execute complex queries in SQL.

Working with unstructured data: Whether on social media, video streams, or audio, it is important that a data scientist may work with unstructured data.

Trends in Salary

While they are in the same field, data scientists and big data experts earn varying wages from each of these professionals.

Salary of Big Data Specialist:

The estimated base salary for a big data specialist is $103,000 per year, according to Glassdoor.

Salary of Data Scientist:

The annual base salary for a data scientist is $113,000 per year, according to Glassdoor.

Conclusion

In this blog, we have discussed the evolving domain of big data and data science. According to Forbes Magazine forecasts, big data is out to remain in the upcoming years since new data will be produced at a rate of 1.7 million MB per second, based on current data growth trends. This big data growth will have great potential and must be effectively managed by companies. For its role in realizing big data's potential, the domain of data science is explored here. Data science is evolving rapidly, with new techniques developed continuously to support data science professionals in the future.

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Saritha Reddy
Saritha Reddy
Research Analyst
A technical lead content writer in HKR Trainings with an expertise in delivering content on the market demanding technologies like Networking, Storage & Virtualization,Cyber Security & SIEM Tools, Server Administration, Operating System & Administration, IAM Tools, Cloud Computing, etc. She does a great job in creating wonderful content for the users and always keeps updated with the latest trends in the market. To know more information connect her on Linkedin, Twitter, and Facebook.