Key Differences Between Big Data Analytics and Data Science: Explained

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In today’s complex and constantly changing world, management is necessary. It has become possible for the human brain to comprehend the amount of data generated today. You might’ve come across terms like Big Data and Big data analysis; by 2025, the amount of data generated, consumed, copied, and stored will surpass 180 zettabytes. The estimated amount of data generated and consumed in the year 2020 was around 64.2 zettabytes

One term that is constantly used with Big Data is Data science. Even though these terms are usually framed together in one sentence, there are some key factors that set them apart. But, before diving into the deep end, let’s break down these terms to grasp the idea entirely. 

Big Data Analytics

Big Data is the term coined for exceptionally large amounts of data that cannot be processed with traditional or old-school methods. Big Data has four main characteristics – volume, velocity, variety, and veracity. Big Data is not a new concept. It is the culmination of decades of software and hardware growth, which enabled companies to manage vast amounts of complex information. Furthermore, data engineers discover more efficient ways to integrate and use Big Data as new technologies are developed to store and process it. This is where Big Data analytics comes in. 

Big Data analytics is the step where Big Data turns into insight for companies to take advantage of. Organisations can use Big Data analytics to gather, process, filter/cleanse, and analyse extensive datasets to develop, grow, and improve their products. Big Data Analytics not only enhances customer integration but also is capable of detecting fraud whilst driving supply chain efficiencies. 

Data Science

The primary objective of data science is to extract meaningful insights from data. It is a multidisciplinary approach to analysing large amounts of data that combines mathematics, statistics, artificial intelligence, and computer engineering principles. By analysing this data, data scientists can ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results. 

How does Big Data Analytics differ from Data science?

As per a Forbes report, nearly 2.5 quintillion bytes of data is created every day. In order to sum up this large amount of data, one should be familiar with the set of required tools. Although it may be confusing to set Big Data analytics and Data science apart, both have different impacts on businesses and other sectors. To better understand Big data, let’s comprehend the key differences between Big data analytics and Data science.

In spite of the fact that they are interconnected, they produce different results and follow different approaches. To increase the value of your business’s data, it’s essential to understand what they bring to the table and how they differ. To help you optimise your big data analytics, we break down both categories, examine their differences, and reveal the value they deliver. 

Basic Concept

  • Data science and Big data analytics can be considered the two sides of a coin. On the one hand, Data Science can be defined as an umbrella term that uses multiple ways to distract and interpret information from massive amounts of structured and unstructured data. In contrast, Big Data analytics make use of specialised software and tools for the analysis of Big data. 
  • Data science can be seen as a field of studies like Computer science or Applied mathematics. It makes use of machine learning algorithms and statistical measures to train computers to program and make predictions from big data. Whereas Big Data Analytics focuses on the mining of valuable information from exceptionally large volumes of data. 

Areas of Application

The areas of application where Data science proves to be helpful are:

  • Risk detection in business
  • Healthcare
  • Targeted advertising
  • Gaming

The areas of application where Big Data analytics proves to be helpful are:

  • Security Transportation
  • Healthcare
  • Travel
  • Education
  • Government sectors


  • Being an umbrella term, Data Science lays the foundation. It does not focus on particular queries. Instead, it creates broad insights upon which the questions are based. 
  • Big Data analytics, on the other hand, fixate on more specific topics. It can be considered as a part of the larger picture. It focuses on answering specific questions, mainly those that are derived through Data Science. It takes into consideration the already existing data to find the answers to particular questions. 

Tools and Techniques

Data science for the mining of information uses concepts like:

  • Data mining
  • Data inference
  • Machine Learning algorithm

Big data analytics to answer particular queries or questions uses concepts like: 

  • Statistics
  • Mathematics
  • Statistical analysis


  • Data science uses data to understand and make predictions for the future, i.e., it tries to build connections and shape the questions to answer them in the coming times. In contrast, Big Data analytics is used to solve problems and spot trends. Both fields have played and are continuing to play a crucial role in different sectors. 
  • Data Science and Big Data analytics have improved business growth, led to success, and had a significant impact on the global economy. In both cases, helpful information is found by exploring extensive data collection. The difference is in the way the process is conducted and the goal that is to be achieved.
  • For example, in healthcare, Data Science research can improve diagnosis, prevent diseases, or teach computers to read X-rays and MRIs. For Big Data analytics in retail, it includes order tracking, recommendations, and identification of store locations.  


There is also a significant difference between data science and data analytics regarding the skills required. The following skills are required for data analytics.

  1. Intermediate statistics 
  2. Excel, SQL database
  3. BI tools like Power BI for reporting purposes 
  4. A Hadoop-based analysis is used for getting conclusions from raw data.
  5. Skills in modelling, databases, statistics and predictive analytics are sure to benefit aspiring data analysts, and they only sometimes need an engineering background for the same.

Skills required for data science include the following:

  1. Advanced Statistics
  2. Mathematics
  3. Machine learning
  4. Predictive modelling
  5. In-depth knowledge of programming 
  6. Data Visualisation with D3.js, Tableau, QlikView, and other tools 
  7. SQL, NoSQL databases like MongoDB and Cassandra
  8. Programming languages like Scala, R, and Python

Type of Data

  • The data used in Data Science by Data scientists is raw and unstructured; it is further organised and sorted so that it can be sent for Data analysis.
  •  On the other hand, for data analysis, the data is structured, and a number of techniques and principles are applied to visualise the data. 


  • Data Science digs out new and undiscovered business issues that can further be converted into innovative use cases and business stories. 
  • Whilst in Data Analytics, problems are already known to the analysts, so they use analytics to come up with the most relevant solutions for the issues.

Role and Responsibilities

  • The role of a data analyst is to help people within an organisation make sense of the data. The data they collect will be used to create reports and visualisations, so others can more easily understand and use the information. As a result, they help the organisation uncover new insights that can be used to guide future business decisions. 
  • It is the job of a data scientist to collect and clean data to improve its usability and understanding. As a result, businesses are able to use the data collected and interpret it in different ways using algorithms and models created by these experts. 
  • Customer segmentation is an excellent example of data science. Using customer buying behaviour and demographics to target customers better can provide organisations with improved marketing strategies.

Why do the differences matter?

An organisation’s data science and analytics efforts can be impacted significantly by seemingly nuanced differences. It is important to note that data scientists and big data analysts perform different tasks. They often have differing backgrounds, so being able to use the terms correctly is crucial. When companies hire the right person for the job, they should be able to use the terminology correctly. There are different uses for data analytics and data science, and while both can be useful to companies, they will only be used in certain situations. In healthcare, gaming, and travel, data analytics is often used, while data science is used on a daily basis in internet searches and digital advertising.

These days, many companies use computer systems to sift through large amounts of data, such as enterprise flash systems, using algorithms to find the connections that will help them succeed. A growing and significant role is also played by data science in the development of artificial intelligence and machine learning. A number of industries can benefit from machine learning, and it is undoubtedly going to play a significant role in the future of business. Therefore, organisations and employees should be aware of the difference between data science and data analytics, as well as the roles each plays.

Although there are differences between data science and data analytics, both are essential to the future of work. It is imperative that companies use both terms if they want to be at the forefront of technological change and analyse the data that drives their businesses. 

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exploring the key differences between Big Data Analytics and Data Science has shed light on the unique roles they play in the ever-evolving world of data-driven decision-making. While both disciplines revolve around extracting valuable insights from vast datasets, they employ distinct approaches and methodologies to achieve their objectives.

Big Data Analytics primarily focuses on processing and analysing large volumes of structured and unstructured data to identify patterns, trends, and correlations. It emphasises data aggregation, querying, and visualisation to derive actionable insights and support business decision-making. By harnessing the power of tools like Hadoop, Spark, and SQL, Big Data Analytics empowers organisations to handle the data deluge effectively.

On the other hand, Data Science encompasses a broader spectrum of activities, including data exploration, hypothesis testing, machine learning, and predictive modeling. It delves into the entire data lifecycle, from data acquisition and cleaning to model building and deployment. Data scientists possess a strong mathematical and statistical background, enabling them to develop sophisticated algorithms and predictive models that solve complex problems and uncover valuable insights.


What is Big Data Analytics?

Big Data Analytics is a process of examining vast volumes of data to uncover patterns, trends, and insights. It involves using specialised tools and technologies to process both structured and unstructured data for decision-making and business optimisation.

What is Data Science?

Data Science is an interdisciplinary field that combines statistics, programming, and domain knowledge to extract meaningful information from data. It involves data cleaning, exploration, and building predictive models to gain valuable insights and support data-driven decision-making.

How do they differ in scope?

Big Data Analytics mainly focuses on processing and analysing large datasets to generate actionable insights, while Data Science encompasses a broader range of activities, including data exploration, hypothesis testing, and predictive modelling.

What are the primary objectives of Big Data Analytics?

The primary objectives of Big Data Analytics are to discover patterns, correlations, and trends in data, identify opportunities and challenges, and optimise business processes based on data-driven insights.

What are the key goals of Data Science?

Data Science aims to gain a deeper understanding of data, create accurate predictive models, solve complex problems, and enable intelligent decision-making across various domains.

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