data science vs big data

Data Science vs Big Data: Key Differences

From clicking a like button on your social media platform to the sales entry on the spreadsheet, each of your actions serves as a data point. And the study of collections of such data points forms the base for the appearance of two critical data study fields: Data Science and Big Data.

Let us try to understand these two fields, their use cases, how they differ from one another, their applications, and the skills you need to learn them in this article.

Let’s start with the basics.

What is Data Science?

Data Science focuses on the scientific study of structured and unstructured data to capture, extract, manipulate, analyse, and process that data.

Data Science has no limitations on the type of data. It does not concern itself with the volume, nor does it specify the size of the data. The scientific study of any information in any size, shape, or form is a part of Data Science.

SAS and MATLAB are popular tools to analyse data in Data Science.

Still, the data analysed here can be categorised into two general categories: quantitative and qualitative data. We can form numerous further subdivisions of these data types. But, to help you understand the grand scheme of things, we will define the other data study, Big Data.

What is Big Data?

The classic definition of Big Data says that it is the information that comes with an extreme variety and volume coming at a great velocity. These 3 Vs imply an enormous size of diverse data that you gather at an astonishingly high volume coming at an exceptionally high speed.

Hadoop, ATLAS.ti, HPCC are some tools that will help you work with Big Data.

In simpler terms, this data is almost impossible to handle with traditional methods. Therefore, the operators need new and creative ways to handle these. We will talk more about data handling in the comparison point. But before that, you should know the types you will encounter while handling any information based on their structural arrangement and readability.

  • Structured data:

Information that can be stored, processed, and recalled in a fixed format repeatedly without exception is called structured data. Sales sheet and employee details are some examples.

  • Unstructured data:

It is an unorganised arrangement of raw data that is a little difficult to work with. This type of data is the cause of most of the frustration in the field. There is no structure to this data, making it challenging to work with.

  • Semi-structured data:

As the name suggests, this amalgamates structured and unstructured data. It has the benefits as well as the drawbacks of both data structure types.

Now that we know some basics of Data Science and Big Data. Let’s dig a little deep and understand the differences between these two.

Differences Between Data Science and Big Data

The main differentiating factor between Data Science and Big Data is that Data Science is a wide field of studies ranging from simple to complex, small to humongous. In contrast, Big Data is the specific study, collection, and analysis of only large data.

Let’s briefly encapsulate the key differences between Data Science and Big Data with a table.

Data Science

Big Data

Complete field with many branches

Specific study of a large sum of information

Focuses on the scientific study of data

Focuses on the volume and quantity of data

Has wide applications and countless opportunities

Specific and thorough in one field. Promising future

Scientifically deals with data of any type or size

Diverse and complicated data types that require new solutions every time

Uses mathematics/statistics and programming extensively to organise data

The only basic requirement is the knowledge of data processing. Anything else is an additional benefit.

Diverse job opportunities in the present

Limited job options at present

Data Science enables working with data with traditional methods

Specialised and newer methods are required in order to work with Big Data

These points may give you a brief understanding of how these two technological fields differ from one another. Let’s now dive into the applications of both fields.

Applications of Data Science

While there is almost no limit on where to use Data Science, the vast nature of the field ensures its use case in almost every domain where there is data and, it is almost impossible to cover all of them. Hence, we will go through a select few that hold greater significance in the industry. Search engine, e-commerce, social media, predictive modeling, and pattern recognition are obvious areas. We will examine three of the most powerful and relatable Data Science applications.

  • Banking or Finance:

One of the most beneficial areas of application of Data Science is the banking sector. All the technological advancements in banking are the direct or indirect result of applied Data Science. Not only does Data Science enable banks to run a lot faster than ever by making countless optimal decisions in a fraction of the time of the traditional methods.

But it also helps them run a lot more efficiently by detecting frauds, effectively managing risk, and predicting the outcomes of extreme scenarios in advance.

  • Business, manufacturing, production, and sales:

The combination of structured data, availability, repeatability of the results, and many more factors in modern businesses work perfectly with Data Science technologies to deliver the best and most efficient management. Businesses incorporating the right Data Science techniques thrive with infinite technological leverage on existing conditions.

  • Healthcare:

The adoption of Data Science by the healthcare industry has revolutionised the industry. And it keeps on improving day by day. With image analysis, genomic Data Science, drug discovery, and many more Data Science features, it has never been easier to detect and diagnose a problem with absolute accuracy as it is now. Data Science’s role in the healthcare industry intensifies as it is the industry with the most variables.

These are some areas of Data Science applications that you can easily understand. Other than these, there are millions of Data Science applications, and most of them haven’t been properly explored yet. Let’s now see the places where one can apply Big Data.

Applications of Big Data

The recent surge in data collection, the popularity of internet running devices, and the cost efficiency of this newly available valuable data gave Big Data an unbelievable boost that highlighted its popularity in almost every data collection field.

  • Tracking consumer behaviour:

Each step of consumer feedback gets tracked and stored on retail sites like Amazon, Flipkart, and others. There are numerous data points to collect data from. The analysis of this Big Data gets used to improve the customer experience. Customers getting the right search results, correct or personalised feed recommendations, better offers, and ranking the products accordingly (for individuals or an entire community) convey some applications of Big Data in the e-commerce industry.

  • IoT:

Interconnecting and intercommunicating devices generate an enormous amount of (big) data. And because both are relatively newer fields, the stored data constantly gets used to improve services and their usability as standalone devices and as interconnected ones.

This field constantly depends on Big Data to provide correct results instantaneously every single time.

  • Media and entertainment sector:

Perhaps this sector has the most impact on the end-user. It has the reach, demand, and supply of the data that other industries try to compete with. Common examples of this would be video and audio streaming services and social media sites.

Big Data is the reason why your Netflix and Spotify recommendations are almost always on point. And while it is banned in India, TikTok is known to utilise Big Data more efficiently and effectively than any other platform giving it immense popularity in a short period.

Again, these are only a few notable and easy-to-understand examples of Big Data. Actual use cases have no limit, increasing with each passing day.

We will now quickly go through the skills required for both fields before concluding things up.

Skills required for Data Science

Here is the list of skills that you need to have if you want to become a data scientist

  1. Mathematics, Probability, and Statistics
  2. Programming languages and software: You should have a good understanding of R, Java, Python, Julia, MATLAB, TensorFlow, Hadoop, SQL, etc.
  3. Data analysis, manipulation, and visualisation
  4. Machine learning, cloud computing, and spreadsheet handling
  5. Interpersonal communication skills

Let’s now see what you need to become a Big Data professional

Skills required for Big Data

The problem-based and specific solution approach in Big Data makes it a little hard to define the skill set. Still, here are a few of the skills you should have.

  1. SQL and Programming Languages: As obvious as it may seem, this is one of the most important things you need to know if you want to make a career in Big Data. In-depth knowledge of Scala, C, Python, and Java will help you become a better Big Data professional.
  2. Analytical skills: Working on huge data sets comes with a responsibility. The responsibility gets a lot lighter if you are proficient with analytical skills. It is a must-have skill for Big Data as most of your work will be on numbers and data points.
  3. Familiarity with Big Data tools: Knowledge of Apache Hadoop, MongoDB, HPCC, or other Big Data tools is highly preferential for Big Data projects.
  4. Data mining skills and familiarity with Scala, Hadoop, Linux, MATLAB, R, SAS, SQL, Excel, and SPSS.
  5. Problem-solving skills: The problems in Big Data are rarely predictable. Hence, excellent problem-solving skills are required for Big Data professionals.
  6. Interpersonal communication skills: This is required in almost all fields and Big Data is no exception.


With all the above examples and details to refer to, we can safely conclude that Data Science and Big Data are related to each other in a few aspects. Both hold their respective significance, and the increasing demand for collection, storage, and analysis of vast data in the modern world fuels both fields to grow exponentially. Therefore, depending on your skill sets and interests, you should choose one of the two.

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