Introduction to Big Data
Big data is a combination of unstructured, semi-structured, and structured data that organizations collect for usage in machine learning projects, predictive models, and other advanced analytics applications and can be decomposed into information. Big data processing and storage systems, combined with tools that support big data analytics applications, have become a popular component of enterprise data management architecture. Big data is characterized by,
- Volume: Large amounts of data in many environments
- Variety: A wide variety of data types are often stored in big data systems
- Velocity: When the speed at which massive data is generated, collected, and processed
Companies employ big data in their systems to take other actions that can improve operations, provide better customer service, create personalized marketing campaigns, and ultimately increase sales and profits. Companies that use it effectively have a potential competitive advantage over those that do not because they can make faster, more informed business decisions.
Introduction to Data Science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains. Data science is evolving as one of the most promising and reliable career paths for professionals. Today, successful data professionals know that they need to overcome traditional big data analytics, data mining, and programming skills. To reveal helpful information for an organization, data scientists have the full range of the data science lifecycle and a level of flexibility and understanding to maximize profits at every stage of the process.
More and more companies admit the importance of data science, AI, and machine learning. Companies that want to stay in the age of big data, regardless of industry or size, need to develop and implement their data science skills efficiently. Otherwise, you run the risk of being late. Since it is an ever-evolving field that provides enormous job opportunities, enroll in Great Learning’s Data Science online training to learn and explore more about the subject.
Difference Between Data Science and Big Data
Implementing a big data approach with traditional data analysis methods is not easy. Instead, unstructured data requires specialized data modeling techniques, tools, and systems to extract insights and information according to the business requirements.
Data science specializes in preparing and coordinating big data for intelligent analysis to extract insights and information by combining multiple areas such as statistics, mathematics, intelligent data acquisition techniques, data cleansing, mining, and programming.
We are currently experiencing unprecedented growth in the information generated worldwide and on the Internet that leads to the concept of big data. The complex application of different methods, algorithms, and programming techniques makes data science a challenging area to perform intelligent analysis on large amounts of data. Therefore, the field of data science developed from big data, or big data and data science are closely related.
Key Differences
There are some significant differences between Data Science and Big Data that we should consider while learning about this topic:
- Data Science is a scientific approach to processing big data by applying mathematical and statistical ideas and computer tools, whereas Big Data is a technique for collecting, managing, and processing vast amounts of information.
- Data science deals with the collection, processing, analysis, and use of data in a variety of operations. Big data is the extraction of essential and valuable information from vast amounts of data, but it is more conceptual.
- While data science is the subject of computer science, applied statistics, applied mathematics, etc., big data is a technique for tracking and finding trends in complex datasets.
- Data science is a superset of big data because it consists of data scraping, cleaning, visualization, statistics, and many other techniques. Big data is a subset of data science, similar to mining activities in the data science pipeline.
- Data science generally focuses on the science of data, but big data is associated with processing large amounts of data.
Comparison Table of Data Science vs. Big Data
The following table shows the fundamental differences between big data and data science:
| Data Science | Big Data |
| A database focused on scientific activities that estimate the processing of big data. It uses the potential of big data for business decisions and rival data mining. | A vast amount of data that cannot be processed by traditional database programming or characterized by quantity, variety, and speed. |
| A discipline of scientific programming tools, models, and technologies for processing big data, providing the technology to extract insights and information from large datasets to help organizations make decisions. | A variety of data generated from multiple data sources, covering all data types and formats. |
| Applies scientific methods to extract knowledge from big data and captures complex patterns from big data to develop models related to data filtering, preparation, and analysis. Working apps are created by programming the developed model. | Information is based on Internet users/traffic, electronic devices (sensors, RFID, etc.), audio/video streams including live feeds, online discussion forums, data generated by the organization (transactions, DBs, spreadsheets, email, etc.), and data generated from system logs. |
| Applies to internet search, Digital advertising, Search for recommenders, Image/speech recognition Fraud, risk detection, Web development, other areas/uses. | Applies to financial services, telecommunication, optimizes business processes, Performance optimization, Health and Sports, Improve trade, Research and Development, Security, and law enforcement. |
| Extensive use of mathematics, statistics, and several other tools, state-of-the-art technologies/algorithms for data mining, programming skills (SQL, NoSQL), Hadoop platform, data acquisition, preparation, processing, publishing, storage, or destruction, data visualization, and prediction. | The approach is to develop business agility, Gain competitiveness, Use records for business gain, Set realistic indicators and ROI, Achieve sustainability, Understand the market, and Acquire new customers. |
Conclusion
This article looks at new areas of big data and data science. Big data will remain for the next few years, as Forbes Magazine predicts that new data will be generated at a rate of 1.7 MB per second by 2020, according to current data growth trends. This growth in big data has tremendous potential and needs to be effectively managed by the organization. Here, we will consider the role of data science in realizing the potential of big data. Data science is constantly evolving at a rapid pace with new technologies that data science professionals can support in the future. Enrolling yourself in the best data science program will give you in-depth knowledge about the subject and pave your successful career paths in data science and big data.
Reference Link:
- https://www.educba.com/big-data-vs-data-science/
- https://www.geeksforgeeks.org/difference-between-big-data-and-data-science/
