The world has never encountered, collected, and stored data and as fast as it is today. In fact, the variety and volume of data are expected to grow in the future. As a result, data has become an important asset for most organizations in the digital era we live in.
Big Data Hadoop in Riyadh and Data Science are the two hot topics across the data space but are often misunderstood. So, while working with all the terminology related to data, it’s crucial to understand the different scopes of work related to it. This topic is often talked about in various data science and big data online courses, even when you are trying to look for a data science certification in Chennai.
This article will give you a detailed insight into the difference between Big Data and Data Science. So, let’s get started.
What is Big Data?
Big Data is a collection of huge and complex data from different sources that are quite difficult to process and store. It is due to using the traditional data processing system or data management tools as these data are not available in standard database formats that we are aware of.
Big Data classifies data into three types:
- Structured – RDBMS, OLTP, structured data formats, and transactional data
- Semi-structured – Text files, XML files, system logs etc.
- Unstructured – Emails, blogs, tweets, mobile data, digital content
Structured data is quite simple to understand, while semi-unstructured and unstructured data need to be customized using data management tools to extract information.
What is Data Science?
Data Science is a field that uses scientific ways, algorithms, processes, and systems to extract knowledge and information from any structured or unstructured data. It is a combination of the field of computer science, statistics, and business together.
Comparison Between Big Data vs. Data Science
| Big Data | Data Science | |
| Definition | ● It is a collection of a huge volume of data that cannot be handled & processed using the traditional methods.
● Characterized by 3Vs (Volume, Variety, Velocity) |
● It is a focused scientific activity that approaches to process Big Data
● Similar to data mining |
| Concept | ● Uses scientific techniques to process data and seek information
● Includes all formats and types of data |
● A specialized area that involves programming tools, models, and techniques to process Big Data
● Supports organizations in decision making ● Helps to provide techniques to extract information from large data sets |
| Formation | ● Internet data
● Online discussion forums ● Data generated from system logs ● Data generated within an organization (emails, transactions, spreadsheets, etc.) |
● Data Analysis
● Data preparation and data filtering ● Extract knowledge from Big Data ● Capturing patterns from Big Data and developing models |
| Application Areas | ● Security & Law
● Healthcare ● Telecommunication ● Sports ● Research & Development ● Financial services |
● Digital advertisement
● Internet search ● Recommendation system ● Speech and image recognition ● Risk detection |
| Approach | ● To develop business
● To achieve sustainability ● To understand market and user behavior ● To gain competitiveness |
● Data visualization
● Data prediction ● Extensive use of statistics, mathematics, and other tools ● Programming skills |
Differences Between Big Data and Data Science
When we talk about Big Data and Data Science, there are some major differences; let’s talk about them:
- Organizations need Big Data to improve efficiency, understand the new markets, and enhance competencies, wherein Data Science provides the method to understand and utilize the potential of Big Data.
- The 3Vs characterize Big Data, basically known as (Volume, Variety, and Velocity), but Data Science provides the methods to analyze the data characterized by 3Vs.
- Currently, the amount of data collected by companies is huge, which pertains to Big Data, but to use all the data to extract information, Data Science is required.
- Big Data is bent more towards technology like Hadoop, Pig, Hive, and analytical tools, but Data Science focuses on strategies for business decisions.
- Big Data analysis performs extracting information from large data sets. But Data Science uses statistical methods and machine learning algorithms to train computers to make predictions from Big Data without using many programming languages.
- Big Data provides the potential of performance but digging out insight information from Big Data for utilizing its potential for enhanced performance is quite challenging as compared to Data Science, as it uses theoretical and experimental approaches.
Skill Required To Become a Data Scientist
- In-depth knowledge and experience of R & SAS.
- Well-versed in Python, Java, C, C++, Perl
- Understanding of Hadoop platform, along with Hive and Pig
- Write and execute complex queries in SQL
Skills Required To Become a Big Data Specialist
- Analytical skills – Essential for determining which data is relevant while creating reports
- Statistical skills – Able to create new methods to gather, interpret and analyze a data strategy
- Computer Science – Programming is the need of the hour
- Business skills – Able to understand the business objectives and process
Payscale in 2022
According to Indeed, the average salary of a Data Scientist is $75,167 per annum, and the average salary of a Big Data Engineer is $117,082 per annum.
The current salary stats are for the USA, based on 3.1k salaries reported on Indeed. However, the salary varies from organization to organization, location, and the amount of experience and expertise you bring to the table.
Conclusion
It is fairly evident that the two Big Data and Data Science are quite similar to each other but different. However, Big Data and data science will stay in the future due to the rapid growth of data every day, and the demand for individuals to process/manage these data will grow accordingly. Hence, if you are looking forward to a career in the data space, it’s the right time to step in.