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Why is learning DBMS important in M.Sc in Data Science?

Data Science
Blog Date
April 10,

Data science aims to gain an understanding of data by applying statistical and computational techniques to large datasets. With the explosion of available data today, businesses, organizations, and governments need data science to make informed decisions. Database Management Systems (DBMS) are crucial to the practice of data science due to their role in data collection, storage, retrieval, and analysis. This blog post will discuss why learning DBMS is important for M.Sc. in Data Science students who want to set themselves up for success.

What is a Database Management System (DBMS)?

A database management system (DBMS) is a centralized data storage system that many users and programs can access. Information management is the primary function of a database management system (DBMS). Users can efficiently and effectively perform data storage and retrieval in data science.

Relational databases, object-oriented databases, hierarchical databases, and network databases are all examples of DBMS. The most used DBMS is the relational variant, which stores information in tabular format using rows and columns. It employs a common query language like SQL to access and modify information. Users must master SQL for data science to manipulate and analyze data effectively. 

In an object-oriented database management system, data is stored as “objects,” each with attributes and operations. Conversely, tree-like and graph-like structures are used to organize data in hierarchical and network database management systems.

Top job roles in DBMS vertical

With the increasing amount of data generated and collected, the demand for professionals with expertise in DBMS has grown significantly. Here are some of the top job roles in the DBMS vertical:

  • Data Scientist 

Database management for data scientists is essential for mining large datasets for actionable insights using cutting-edge statistics and machine learning methods. Data scientists create data-driven solutions to business issues by creating prediction models and mining data. These experts should be fluent in Python or R and have experience with statistical analysis and data visualization.

  • Database Administrator (DBA) 

A DBA’s job is to ensure that databases are set up, configured, maintained, and running at peak efficiency. They monitor the system’s health and performance and deal with related problems. Data administrators should be fluent in SQL and fully grasp database design and modeling.

  • Database Developer 

Database developers plan, create, and maintain databases for organizations. They create and manage database applications, create queries, and enhance database functionality. Database developers require SQL, Java, or Python knowledge and design and development skills.

  • Data Analyst 

Data analysts examine and make sense of information to help businesses make better decisions. They gather information from many resources, clean it, and load it into a database before analyzing it for patterns and trends. Knowledge of SQL, data modeling, data analytics, and statistics are all essential skills for data analysts.

  • Data Architect 

Data architects are responsible for creating and implementing data models that serve businesses with data accuracy, security, and consistency. They consult relevant parties to ascertain business needs and create appropriate database solutions. Professional data architects should be well-versed in all aspects of data, including modeling, design, and management.

Why should data science professionals learn DBMS?

Why use DBMS?
Reduced Application Development Timing 
Data Independence
Recovery from Crashes 
Data Security 
Uniform Data Administration

Since DBMS is a cornerstone of data management, mastering it is required for every data science career. Here are some of the DBMS applications in the data science industry:

  • Data Organization 

DBMS enables users to organize data systematically and efficiently to facilitate retrieval and manipulation. This is crucial for processing time-sensitive huge datasets. For a data scientist working on a project that requires evaluating consumer data, a DBMS can store and manage the data, making it more accessible and easier to analyze.

  • Data Security

Database management systems ensure the safety of user information by preventing its disclosure or modification by unauthorized parties. This is especially important for private information like bank account details or driver’s license numbers. For instance, if a data scientist is tasked with evaluating healthcare data for a project, they must ensure that the data is protected and per Health Insurance Portability and Accountability Act (HIPAA) standards.

  • Data Integration

Database management systems facilitate aggregating information from various sources for analysis and insights. A data scientist can use a DBMS to combine POS, CRM, and website analytics information to better understand customer behavior.

  • Scalability

Database management systems (DBMS) have the flexibility to scale to accommodate increasing data volumes. This is crucial for businesses that produce a lot of data or small businesses aiming to expand operations. For instance, a data scientist for a social networking platform must employ DBMS to handle massive amounts of user-generated data.

Is an M.Sc in Data Science right for learning DBMS?

An M.Sc Data Science is a great option for studying DBMS because it provides in-depth instruction. Data modeling, normalization, and querying are generally covered in an M.Sc in Data Science program. Popular database management systems (DBMS) that they learn to utilize include MySQL, Oracle, and Microsoft SQL Server. Students also gain knowledge about data security and performance optimization within DBMS.

In addition, data warehousing, big data management, and NoSQL databases are also covered in M.Sc in the Data Science curriculum. Professionals in data science must be well-versed in these areas since they provide foundational knowledge of data management.


In conclusion, mastering DBMS is essential for any data scientist, and completing a Master of Science (M.Sc) in Data Science is a great way to do so. The M.Sc in Data Science program at Manipal Academy of Higher Education (MAHE) provides students with the expertise they need to handle and modify data timely and accurate.

Graduates of MAHE’s Master of Science in Data Science degree can find work in various DBMS-related fields, such as administration, development, and analysis. They are also prepared to deal with data management and manipulation difficulties in the modern, data-driven environment. Enroll in our courses today!

Key takeaways:

  • This blog focuses on the significance of learning Database Management Systems (DBMS) in the M.Sc in Data Science program. 
  • There is a striking correlation between DBMS and data science.
  • This blog discusses the top job roles that combine the skills of both niches.


Information related to companies and external organizations is based on secondary research or the opinion of individual authors and must not be interpreted as the official information shared by the concerned organization.

Additionally, information like fee, eligibility, scholarships, finance options etc. on offerings and programs listed on Online Manipal may change as per the discretion of respective universities so please refer to the respective program page for latest information. Any information provided in blogs is not binding and cannot be taken as final.

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  • Online MSC Data Science

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