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.
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.
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.
Big data can be classified into 3 Structured data, Unstructured data, Semi-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 |
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”
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.
| 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 |
| 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 |
The main key difference between data science and big data are the following:
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.
