Data Science

Data Science Vs Big Data

July 8, 2022, Learn eTutorial

In this module let us discuss what are the main differences as well as similarities between data science and big data. We will also discuss the roles and responsibilities, what are the most important skill sets which are required for becoming a successful data scientist, data analyst, and professional in big data.

What is data science?

Data science is a method in order to understand and realize the business requirements of each company using various concepts like machine learning, statistics, mathematics, programming, and many more concepts. Using all these concepts useful insights and patterns are extracted from the data which are collected from different sources. 

Mainly using AI and ML data scientists will find out the hidden patterns and useful insights extracted from the collected data and they will use it for business development.

What is big data?

Different types of Big data

It always refers to the vast amount of data that is generated in each second from different sources and the obtained data will be in various formats such as video files, audio files, text files, jpeg files, and many more different types of data in various format. 

We have a traditional system that is used to process data but the problem with the traditional system is they are incapable to deal with all the huge amount of produced data that are in different formats. A huge amount of data means the size of the data will be huge and the data will be growing very fast with respect to time, those type of data is known as big data.    If the data set is very huge and if they cant be processed using a traditional processing system then those type of data is known as big data. Some examples of big data are live road-mapping which is mainly done for autonomous vehicles, media streaming, personal marketing so on.

Different types of Big data 

Big data can be classified into 3 Structured data, Unstructured data, Semi-structured data

Different types of Big data

Structured data :  

If the collected data can be accessed, as well as processed and stored in a particular fixed format then those type of data is known as structured data. That means if the data is provided in a standardized format with proper classification then it is known as structured data.

An example of structured data

A ‘Student’ table that is in a database can be considered an example of structured data.

Student_ID Student_Name  Gender Department Mark
1234 John Francis Female CSE 92
4567 James Male ME 98
9876 John Doe Male CE 88
1357 Jennifer Female CSE 78
3542 Evelyn Male ECE 90

Unstructured Data

This is another type of big data where the information or data is not arranged in a particular schema. Best examples of unstructured data are audio files, video files, Log files, image files, and many more.

Example: the output that is returned from “google search”

Semi-structured Data

these type of data always contains both the format of structured data as well as unstructured data. Semi-structured data are organized but not well organized like structured data.

Example: Markup languages,  XML and zipped files, etc.

Different types of Big data

Data science VS Big data

Data science Big data
It always deals with data analysis. Handling large data
Decisions are made by understanding the patterns  within the data Here huge volumes of data are procced and insights are extracted
Tools : SAS, R, Python Tools: Hadoop, Spark,Filnk
Application area: Internet research,  image and speech recognition, Digital advertisements, etc. Application area: Health care, Travelling sectors, Gaming, etc

Skill sets required for  data scientists and big data professionals

Data  Scientists Big data professionals
Deep knowledge in Machine Learning and programming Creativity
Analytical and Statistical skills Business skills
Deep learning Data visualization
Mathematical skills MATLAB knowledge
SAS/R coding Base programming
Communication skills SQL coding
Team player skills Working with unstructured data

Data science VS Big data: The key differences

The main key difference between data science  and big data are the following:

  • The most organization uses big data mainly to improve efficiency as well as it also enhances competitiveness whereas data science uses modeling techniques and methods in order to extract useful information and patterns from the collected data.
  • The data which are collected from different companies as well as from different sources are huge and for handling this large amount of data, big data is used. All this data contains useful insights, information, and patterns so, in order to extract patterns and useful insights, data science is used.  
  • To guide the huge data set the     3Vs of big data are used. The 3Vs of big data are volume, velocity, and variety. For measuring big data the factors such as volume,  velocity, and variety are used. But in data science, some techniques are mainly used to analyze the data.
  • Theoretical and some practical approaches are used in data science in order to dig useful information and extract patterns which are used to improve business. Big data is nothing but huge volumes of data and it can not be handled using the conventional data analyzing method.

Who is better? Data scientist or big data engineer

A data scientist will mainly build models and the aim behind this is to get insights and hidden patterns from the data and it will also help to make predictions about the future business of the company. A big data engineer will build data, will test as well as maintain data pipelines. The starting salary of an experienced big data engineer is Rs 866,234 (approx) annually. A junior big data engineer with a minimum of 1 to 4 years of experience will be rs 566,234 (approx) annually.

The data scientists will always train the predictive kind of data models along with the data which is received from the big data engineers. The big data engineers will find out the solutions that will help out a data scientist in accessing and analyzing data.

If someone who always wanted to be a data scientist will always think a data scientist is better than a big data engineer and vice versa. Data interpreting can be done by a data scientist only if it is received in an appropriate format. The main role of a big data engineer is to give data to data scientists in order to extract useful insights from the data.

Big data is a part of data science.

Different types of Big data