Data Science vs Big Data - Top 8 Differences

Read it in 12 Mins

Last updated on
22nd Feb, 2022
Published
21st Feb, 2022
Views
5,647
Data Science vs Big Data - Top 8 Differences

Data Science vs Big Data is a hot topic of discussion within tech circles. Both, data science and big data are extremely popular today and seem to be very similar due to their data-focused nature. Hence, it is natural to wonder, 'Are big data and data science the same?'  The short answer is, no they are not the same. There are several differences between Big Data and Data Science. The key difference between them is their application or use in day-to-day life, by businesses and individuals.  

Big Data is a vast resource of information collected in structured and unstructured form but needs additional steps and processes to uncover the underlying information. Hence, big data cannot be processed without data science for business decision-making. Data science handles big data by transforming, analysing and visualizing it to bring out meaningful insights. As a result, both are distinct yet complementary. And both have their own importance and significance. To build knowledge and capacity in the data science field, you can start with a comprehensive data science certificate online. 

What Is Data Science?

Data science has become a critical decision-making tool for many businesses today due to the generation of huge volumes of data. Hence, it has become extremely popular in recent times. Many organisations have started using data science to grow their businesses. 

Data science is the science of exploring a large volume of data to reveal latent or hidden patterns. It utilises theories and conclusions drawn from various fields involving mathematics, statistics, machine learning, and deep learning. Different statistical tools like SAS, SPSS, Microsoft Excel, Minitab, etc. and programming languages like Python, R, Julia, etc., are used in data science to process big data.  

What Is Big Data?

Any data which is difficult to handle manually or with commonly available resources for analysis, can be called Big Data. It isn't easy to define a clear-cut size or volume for big data. It is a collection of different forms of data like unstructured, semi-structured, or structured data procured from various resources readily available these days. 

Data that is collected from web pages, social networks, blog articles, audio-video, chats, emails and online images, etc. is termed as unstructured data since it is not formatted. Data stored in formats like TXT, CSV, XML or JSON files is called semi-structured data since it is mostly available in a tabular form but might require some additional formatting. Lastly, the relational databases (RDBMS), online transaction processing (OLTP) and similar formats is known as structured data. This type of data is correctly formatted for data analysis. 

Key Differences: Big Data vs Data Science

The following table highlights the top 8 differences between big data and data science.


Big DataData Science
Meaning
  • Big data is massive, difficult-to-manage data volumes (both structured and unstructured format) generated by businesses on a daily basis. 
  • The special features are variety, veracity, velocity and volume. 

  • A data-driven approach based on scientific principles science.  
  • Includes different techniques to process big data.  
  • Visualizations after analysis are used to make business decisions 

Concept  
  • It consists of different formats of continuously generated data  
  • Data generations could be from a single source or multiple sources. 

  • A strategy to cover all stages of data pipeline and utilises technology, software, algorithms and innovative approaches to achieve the goal.   
  • Analysis results and predictions are being used to make business decisions. 

Basis of formation  

Several sources of formation include:  

  • Emails, tweets, SMS/chats  
  • System log  
  • Online transactions   
  • Audio-Video streaming  
  • IoT devices using various sensors 

  • It includes different stages for Data collection, Organisation and storage, Data manipulation using ELT (Extract-Load-Transform) and ETL (Extract-Transform-Load) methods. 
  • Prepared data is analyzed and visualized to build models 
  • Trained models are deployed for making predictions 
  • Better decision making is achieved through continuous evaluation of the deployed models 

Application Areas
  • Healthcare 
  • Banking and Finance 
  • Sports Analytics 
  • Ecommerce 
  • R&D 
  • Cyber Security 

  • Search Engines 
  • Computer Vision 
  • Natural Language Processing 
  • Digital Marketing 
  • Agriculture  
  • Pharmaceuticals  

Approach 
  • Gather all sorts of required data at enterprise level or through cloud services 
  • Pre-processing, transformation and storage  
  • Leverage this acquired data appropriately to improve company performance 
  • Arrange for necessary infrastructure, manpower and other resources

  • Programming languages like Python, R, Java, Julia, MATLAB 
  • IDE’s like Jupyter, PyCharm, Spyder, Visual Studio or online platforms like Google Colab 
  • Visualization and Analytical tools like Excel, Tableau, Power BI, Looker etc. 
  • Different Algorithms for supervised, unsupervised and reinforcement learning. 

Tools  
  • Gather all sorts of required data at enterprise level or through cloud services 
  • Pre-processing, transformation and storage  
  • Leverage this acquired data appropriately to improve company performance 
  • Arrange for necessary infrastructure, manpower and other resources

  • to identify correctly the problem on hand and gather required data using relevant data collection technique 
  • evaluate models based on different algorithms and select the best performing model using optimization techniques.  
  • Deploy the trained model to make predictions and check for accuracy of prediction, make modifications to improve the performances 
Skills  
  • Data Analysis 
  • Data Visualization 
  • Data Mining 
  • Data Transformation 
  • SQL/NoSQL 
  • Business Intelligence 
  • Some amount of programming 

  • Data Wrangling  
  • Data Visualization 
  • Calculus, Linear Algebra, Statistics 
  • Database Management 
  • Machine Learning 
  • Deep Learning 

Salary  

Average Salaries according to role:  

  • Database Developer – $92K/yr. 
  • Data Analyst – $69K/yr.  
  • Database Administrator – $84K/yr.  
  • Data Modeler – $94K/yr 
  • BI Analyst – $82K/yr 
  • Database manager – $76K/yr 
  • Database architect – $1L/yr 
  • Big Data Engineer – $1L/yr 

Average Salaries according to role:  

  • Data Scientist – $1.2L/yr  
  • Data Engineer – $1.1L/yr  
  • Machine Learning Engineer – $1.3L/yr  
  • System Engineer – $78K/yr  
  • Software Engineer – $1.1L/yr 
  • DevOps Engineer – $1.1L/yr

The salaries mentioned in the table are approximate values as gathered from glassdoor.com. Hence, they may vary depending upon location, size of company, role, responsibilities, and experience.

Meaning 

  • Data Science 

Data science is a science that mainly focuses on all aspects of data and its manipulation to bring out useful insights.  

  • Big Data 

Big data is a term coined for relatively large size of data that is difficult to be handled manually and normally available hardware. The special features of big data are variety, veracity, velocity, and mainly volume.       

Concept 

  • Data Science

Data science can be termed as a strategy to cover all stages of data pipeline right from mining to visualization through analysis and bring out meaningful insights or right prediction. Data science makes use of technology, software, several algorithms, and innovative approaches to achieve the above.  

  • Big Data

Big data is a collection of data containing different formats of data like TXT, CSV, XML, Oracle, JSON, etc. This huge volume of data is being continuously generated from either a single source or multiple sources.

Basis of formation

  • Data Science 

A typical data science process includes steps like data collection, organization, storage, data transformation (ELT and ETL), data extraction, analysis & visualization, model building, and prediction, as well as deployment and continuous evaluation for better decision making. 

  • Big Data 

Several sources of big data generation identified are Emails, tweets, SMS/chats, system logs, online transactions, audio-video streaming and IoT devices using various sensors, etc.

Application Areas

Both data science and big data can be applied to similar fields where data-driven business decisions are required.  Here is a big data analytics vs data science comparison based on the application areas.

  • Data Science 

Data science finds its applications in domains like Search Engines, Computer Vision, Natural Language Processing (NLP), Digital Marketing, Agriculture, Pharmaceuticals, etc. 

  • Big Data

Big data is applicable in fields like Healthcare, Banking, and Finance, Sports Analytics, Ecommerce, R&D, Cyber Security, etc. 

Application Areas

Both data science and big data can be applied to similar fields where data-driven business decisions are required.  Here is a big data analytics vs data science comparison based on the application areas. 

  • Data Science 

Data science finds its applications in domains like Search Engines, Computer Vision, Natural Language Processing (NLP), Digital Marketing, Agriculture, Pharmaceuticals, etc. 

  • Big Data 

Big data is applicable in fields like Healthcare, Banking, and Finance, Sports Analytics, Ecommerce, R&D, Cyber Security, etc. 

Approach

1. Data Science 

Data science follows the following approach to solve a practical problem. 

  • Identify the problem correctly on hand  
  • Make available necessary relevant data 
  • Try different algorithms to build a set of models and choose the best one through optimization techniques 
  • Deploy and check for accuracy of prediction 
  • Make necessary modifications in the model for improving the results. 

2. Big Data 

Big data processes consist of the following approach- 

  • Collect relevant data at an enterprise level or through cloud 
  • Pre-processing and storage 
  • Utilize in a proper manner to achieve the company's target in a cost-effective way. 
  • Arrange for necessary infrastructure, manpower, and other resources.  

Tools 

Data Science vs Big Data - Top X Differences

Both data science and big data are highly technical fields, and hence, there is a certain level of expertise expected while working in each of the different roles like data scientist, data analyst, data engineer, database architect, big data engineer, etc. Without these coding and programming skills, it is quite challenging to be successful in these fields. In fact,knowledgehut applied data science with pythoncourse is an excellent option to gain some important skills to begin your career in data science and big data. Let us look at some key skills for each of these domains.

  • Data Science

Since working in data science requires you to be good at a variety of skills, including mathematics, programming, visualization, storytelling, etc. This field requires a strong command of the programming languages like Python, R, Java, Julia, Matlab. Additionally, you need to be good in at least one development tool (IDE) like Jupyter, PyCharm, Spyder, Visual Studio. Online platforms like Google Colab can be used to setup your data exploration and machine learning models. Since data science is all about uncovering valuable information from big data, any data science professional should be able to visualize the data and analyse it using visualization and analytical tools like Excel, Tableau, Power BI, Looker, etc. Finally, for model building, these professionals need to have an excellent working knowledge of different Algorithms for supervised, unsupervised, and reinforcement learning. 

  • Big Data

As most of the roles in big data are concerned about the generation, storage, and handling of data, the key skills are slightly different from that of data science. A general requirement in big data is to be able to query the stored data. For this, big data professionals need to have expertise in handling relational and non-relational databases. These call for a solid working knowledge of some of these storage and query processing tools like Apache Hadoop, Apache Spark, Apache Cassandra, MongoDB, etc. Moreover, they can also be proficient with cloud storage technologies since most big data is usually stored on the cloud these days. Cloud service providers like Amazon web services (AWS), Google cloud platform (GCP), and Microsoft Azure are popular cloud storage providers for companies to fulfil their different data storage requirements. Big data roles also expect some level of experience with data visualization tools like Tableau, Power BI, Looker, etc.

Skills

The top skills required in these two domains are directly dependent on the main tasks performed by individuals in specific roles.  

Data Science 

For data science, all aspects of data pre-processing require excellent skills in data wrangling, data visualization, analytical skills, along with a good knowledge of Calculus, Linear Algebra, Statistics, data management, algorithms for machine learning, and deep learning. 

Data Science vs Big Data - Top X Differences

Big Data 

This includes data storage and handling skills along with data analysis, data visualization, data mining and data transformation, etc. Moreover, working with relational and non-relational databases requires expertise with SQL/NoSQL tools. Additionally, some knowledge of business intelligence concepts as well as programming in Python is expected. 

Data Science vs Big Data - Top X Differences

Salary 

Although both these fields have data-driven roles, professionals working in these tend to earn different salaries. The average salary seems to vary based on the roles and responsibilities, the expertise required, location as well as size of the organization. Despite this difference, we can say that almost all roles in these two domains are certainly lucrative career options. 

Data Science

Typical average salaries p.a. for different data science roles are – A data scientist earns around $120K/yr while a data engineer earns around $110K/yr. A machine learning engineer with a graduate degree could earn up to $130k/yr. Similarly, IT-based roles like system engineer and software engineer usually earn between $78K-$100K/yr. Since DevOps roles are a combination of IT and software development, a DevOps engineer role can also earn a salary of $110K/yr. 

Big Data

Average big data salaries too vary according to the role. A database manager tends to be an entry-level post with an average salary of $76k/yr. On the other hand, a database developer and database administrator can earn between $80k-90k/yr, whereas data analyst and BI analyst can earn anywhere between $70k-85k/yr. A data modeler or a big data engineer can earn close to $1L/yr. Database architect is the one who decides how the electronic database should look like and function. This task is highly crucial for the success of implementing big data tools. Hence, it is a highly specialized and sought-after post with an average salary about $1.1L/yr and above.

Conclusion 

In summary, we saw the top differences between data science and big data. Although these two fields sound similar due to their dependence on data, they are nevertheless different. Big data is the basis on which data science is able to provide solutions to real-life challenges. On the other hand, without data science, the use of big data to uncover hidden pieces of information and generate innovative solutions would be a distant dream. This implies that both these fields need to co-exist in order to be useful. In conclusion, we can say the relationship between big data and data science is interdependent and that big data is a subset of data science. 

Frequently Asked Questions

Is big data and data science the same?

No, they are not the same, rather, they’re inter-related and inter-dependent. Data science exists because of big data and will continue to do so. 

Is big data necessary for data science?

Yes, Big data is essential for data science. Data science is a challenging field by itself and has grown in importance due to the generation of big data. Data science works on large volumes of data which is essentially ‘Big Data’. Hence, we can say that big data is required for data science.

Profile

Devashree Madhugiri

Author

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms.
She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.