While big data and data science are sometimes used interchangeably, they’re tangibly different concepts. Big data is a result or outcome of technological innovation and refers to extremely large sets of data, such as from sensors and smart devices that make up the Internet of Things. Data science refers to the process and technique of working with that data and the skills one develops to do so. Many fields intersect in data science, including statistics and computer sciences.
Big Data: Big data refers to expansive sets of data that are too large to be analyzed through traditional data-processing methods. Big data includes:
- Unstructured Data: This is data that has no identifiable structure or organization and does not fit with existing data models, rendering it incompatible with traditional databases or processing.
- Semi-Structured Data: This data has some structure but is not in a database. It often has metadata tags to help categorize group subsets of data, and it may have a hierarchical structure. These elements make the data easier to organize and interpret.
- Structured Data: Structured data is organized data, with a defined length, format, and data model, so it’s easiest to store, query, and analyze. It’s stored in the relational database (RDBMS) and is more accessible to algorithms or human-generated queries.
Data Science: Data science involves recording, storing, and analyzing massive amounts of data to gain valuable business insights – and developing new methods for doing so. You can become a data scientist as your primary occupation, or you can learn data science skillsets to add value to other occupations, such as cybersecurity or business management.
“What are big data and data science?” is a complex question, but knowing the answer may help guide your education and career decisions. This article will provide an in-depth breakdown of the differences between the two.
Are Big Data and Data Science the Same?
Data science and big data are two different concepts, but they’re related in that data science is needed to process and utilize big data efficiently. The following points may help you further understand key differences and how big data relates to data science:
- Organizations use big data to be more efficient, understand markets, and maintain competitiveness, while data science provides the means to identify and utilize big data’s full potential.
- It’s significantly challenging to extract all valuable information from big data, but data science assumes the responsibility of finding all useful information within big data through the development of theoretical and experimental approaches, as well as inference and deduction.
- Big data analytics involves identifying relevant information in expansive datasets. It usually has a specific question or goal in mind, and it analyzes the data to find a solution.
- Data science, on the other hand, aims to extract all useful information from datasets; it’s not limited to one particular goal or problem. It engages in machine learning and statistical methods to teach computers how to make predictions from the data, and it develops new ways to process and model it.
Big data is used in tools and software for distributed computing, analytics, and technology (like Hadoop, which is an open-source framework that aids in the storage and analysis of big data). Data science is used to develop business strategies and guide decisions while using disciplines like mathematics, statistics, data capturing and mining, and computer programming.
The table below further evaluates the fundamental differences between big data and data science:
Big Data vs Data Science Comparison Table
|Area of Comparison||Big Data||Data Science|
|Meaning||Data characterized by its velocity, variety, and volume (the 3Vs)||The scientific discipline of processing and analyzing big data|
|Concept||All types of data from numerous sources||A specialized science that develops and applies analytical tools, automation systems, data frameworks and processes to isolate and interpret meaningful data to guide an organization’s decisions|
|Formation||Derived from a multitude of sources, including:
||Uses scientific approaches and processes, like data filtering, to illuminate intricate data patterns and create models and working apps|
|Applications||Is used in a variety of areas and industries, including:
||Is used for applications like:
|Approach||Determines realistic business metrics and ROI, and enhances business agility, competitiveness, market advantages, sustainability, and customer acquisition||Involves mathematics, statistics, and programming, plus data mining, processing, visualization, and prediction|
Data Science vs. Data Mining vs. Big Data
Data science is a field of scientific study that builds data-centered products and deliverables for an organization or business, using a variety of techniques, approaches, specialties, and advancements to do so. It also develops new methods of working with data.
Data mining is just one method of processing big data in data science. It’s a technique that uses intelligent methods like machine learning and statistics to find patterns in large amounts of data, then structures that meaningful information so it’s accessible for future use or study.
Data mining applications include inventory planning, sales forecasting and target setting, database marketing, and customer loyalty and incentive programs. If you’re interested in data mining, potential careers include market research analyst, information security analyst, and computer network architect. Rice offers a Statistical Computing and Data Mining specialization in the Professional Master’s Program of Statistics.
Big Data vs Data Science vs Machine Learning
Machine learning is also integral to the use of big data in data science, as well as data mining. It refers to the process of training computers to understand sets of rules and apply them as directed. Once a rule or algorithm has been taught, it’s an automated process – the machine will continue to follow that procedure when needed. Each new set of data or algorithm taught to it will compound that knowledge, and the algorithms are self-defined, meaning the machine can alter the rules to fit situations as needed until a solution is reached.
In data science, machine learning is used to identify patterns in big data (after the data has been prepared) and make predictions or estimates based on it. Professionals in machine learning use their skills in technology, mathematics, statistics, business, data analysis, and several other technical and logical competencies. For those interested in machine learning, possible career titles include data scientist and machine learning engineer. Rice offers a Master’s of Data Science with a robust Specialization in Machine Learning with world-class faculty for prospective students who aspire to pursue a career in machine learning.
Learn More About Data Science
Data science is a broad field that encompasses a multitude of functions, specialties, and processes to help understand and utilize big data. Those with data science degrees and skillsets, including machine learning, are in high demand across numerous industries, with the ubiquity of data across industries, as big data continues to drive business growth and advancement.
If you’re interested in pursuing a career in data science or machine learning, learn more about data science and how Rice’s Master of Data Science degree program can help you stand out among other candidates in this competitive and demanding field. The MDS@Rice program offers specializations in related fields, an interdisciplinary curriculum, world-class faculty, and access to the Data To Knowledge (D2K) Lab and Capstone, where you’ll work on real-world projects that aid society through the use of big data. Learn more about the MDS@Rice degree program and the various specializations available to launch or advance your career in the exciting, innovative field of data science.