Difference Between Data Science and Big Data

According to a report by NASSCOM, India’s big data analytics market is projected to grow from $2 billion in 2016 to $16 billion by 2025. For professionals aiming at effectively harnessing the power of information in today’s data-driven world, it is important to realize how data science differs from big data. 

Although they are both data-focused fields, the difference between data science and big data is well-pronounced. They each serve different purposes and require a different set of skills. This article will explore the distinctions between data science and big data, pointing out their unique characteristics, as well as their applications, browsers, and compatibility.

Also read: MCA vs MSc in Data Science

What Is Data Science?

Data science is a discipline that combines statistics, mathematics, programming and domain knowledge to derive meaning from data. Among the techniques used in this field are predictive modeling, machine learning, data mining, and many others that are used to sift and decipher complex data sets. The main aim of data science is to find patterns, give predictions, and assist in decision making throughout different industries.

Key aspects of Data Science:

• Data Analysis: Where datasets are subjected to look for trends, patterns, as well as anomalies.
• Modeling: Where algorithms or predictive models are created so as to foretell what lies ahead.
• Visualization: Using graphical representation, helps people understand information better.

What Is Big Data?

Big Data refers to large amounts of data that cannot effectively be handled using traditional data processing tools because it is extremely large, unwieldy and disorganized in nature. 

Key aspects of Big Data:

• Volume: Huge amounts of data generated from various sources.
• Velocity: Fast speed with which new data comes in requires quick processing.
• Variety: Different types of data including structured, semi-structured and unstructured data.

The focus of Big Data is on the storage, processing and analysis of these vast data sets in order to extract useful information out of them. Technologies such as Hadoop, Apache Spark and NoSQL databases help effectively store and process large datasets.

Key difference between data science and big data

Despite the fact that data science and big data share common ground, they vary in several ways:

Parameter	Data Science	Big Data
Definition	Interdisciplinary field focusing on extracting insights from data using statistical and computational techniques.	Refers to large, complex datasets that require advanced tools and methods for processing and analysis.
Focus	Analyzing and interpreting data to inform decision-making.	Managing and processing vast amounts of data efficiently.
Data Size	Can work with datasets of any size, including small and medium-sized data.	Deals primarily with large-scale datasets that exceed traditional processing capabilities.
Techniques	Utilizes machine learning, predictive modeling, and statistical analysis.	Employs distributed computing, parallel processing, and NoSQL databases.
Goals	Derive actionable insights, make predictions, and optimize processes.	Efficiently store, process, and analyze large volumes of diverse data.
Tools	Python, R, SQL, Tableau, TensorFlow, and Scikit-Learn.	Hadoop, Apache Spark, MongoDB, and cloud computing platforms.

It is important for businesses and professionals to understand the difference between data science and big data if they want to effectively apply data science and big data in their operations.

The coming together of Data Science and Big Data

Big data and data science, though different, are two sides of the same coin. To extract meaningful insights out of such massive amounts of raw information, data science relies on big data. On the one hand, without big data, data science would not possess extensive datasets necessary for the purpose of accurate analysis, and on the other hand, data science helps analyze big data, thereby unlocking its real value.

For instance:

• Healthcare industry — There are big data technologies that can combine patient records, genomic data and real-time health monitoring systems. Subsequently, using data science to analyze this data can predict disease outbreaks, personalize treatments and improve patient outcomes.

• Finance sector — Big data will deal with huge transactional numbers, while detection of fraud using data science models will facilitate decision-making based on market trends, hence facilitating investment decisions.

By merging big data with data science, businesses can improve their decision-making mechanisms, optimize their operations and gain a competitive advantage over others within the same sector.

You may also like: Launching a Data Science career without coding

Online Manipal’s MSc in Data Science

One major milestone that professionals who aim to become gurus in the world of data science can make is pursuing more advanced degrees. Manipal Academy of Higher Education (MAHE) through Online Manipal offers an online Master of Science in Data Science (MSc-DS) degree program. This program is UGC-entitled and is tailored to equip learners with a broad knowledge of the principles and practices of the data science discipline.

Program highlights:

• Full coverage of curriculum – Machine learning, big data analytics, statistics and data visualization are included in the course content.

• Flexible learning – Professionals can take care of their studies alongside other commitments with this online course.

• Industry-driven approach – Concentrates on practical solutions based on real-world scenarios to prepare graduates for analytical as well as leadership roles in all segments.

This course equips learners with the skills to manage complex datasets and extract actionable insights for data-driven decision-making. Additionally, it provides access to a dynamic alumni network, enhancing opportunities for professional growth.

The bottom line

To conclude, it is important for businesses, especially those that deal with huge volumes of information, such as banking institutions, to understand the difference between big data and data science. 

Big data is utilized by data science methods to solve problems. As such, understanding these differences can help professionals navigate the data landscape more effectively. Professionally, advancing in this dynamic industry may include pursuing programs like MAHE’s MSc in Data Science, offered at Online Manipal.

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