Data Science is the future as it is the most lucrative career option in the upcoming year. 2025 looks promising and poses to begin a new era in data science. The best data certification courses can provide you with the best career opportunities worldwide. It is among the fastest-growing technology now and thus industry is poised to offer huge career opportunities in the coming years. The Data Science market is expected to reach the project value of $ 241.2 billion in 2025(market. US). The data science industry will drive a 27.9% surge in employment through 2026(US Bureau of Labor Statistics). According to LinkedIn, it is the fastest-growing profession in the present market.
“Data science isn’t about the quantity of data but quality”
Joo Ann Lee
Marketing Data Scientist at Witmer
WHAT IS DATA MODELLING?
Data Modeling is the process of creating a visual representation of a complex system or dataset to describe its structure, relationships, and rules.
The data model is like creating a blueprint for how data is organized and used. It’s like planning and designing before building something.
Technological advancements are fueled by the data modeling market, which is expected to grow significantly through 2025.
Here, we start with the basic questions and then progress through intermediate ones, followed by advanced ones.
TOP 11 DATA MODELING INTERVIEW QUESTIONS FOR 2025 (WITH RESPONSES):
Q. What are the three types of Data Models?
A. The three types of data models:
Physical data model –It describes how data is stored in a database.
The conceptual data model focuses on the high-level, users’ view of the data in question.
Logical data model – Detailed structure without focusing on physical implementation.
Q. What is the table?
A. A table consists of data stored in rows and columns. Columns, also known as fields, show data in vertical alignment. Rows also called a record or tuple, represent data’s horizontal alignment.
Q. What is the purpose of normalizing in data modeling?
A. Normalization reduces data redundancy and improves data integrity by organizing attributes into tables.
Q. What is Denormalization, and What is its purpose?
A. Denormalization is a technique where redundant data is added to an already normalized database. The procedure enhances read performance by sacrificing write performance.
Q. What Does ERD Stand for, and What is it?
A. ERD stands for Entity Relationship Diagram and is a logical entity representation, defining the relationships between the entities. Entities reside in boxes, arrows symbolize relationships.
Q. What is the definition of a surrogate Key?
A. A surrogate key is a unique identifier for a record in a table. It is used to maintain consistency and avoid dependency on natural keys, which might change over time.
Q. Explain the Two Different Design Schemas.
A. The two design schemas are called Star Schema and Snowflake Schema. The Star Schema has a fact table centered with multiple dimension tables surrounding it. A Snowflake schema is similar, except that the level of normalization is higher, which results in the scheme looking like a snowflake.
Q. What is Granularity?
A. Granularity represents the level of information stored in a table. Granularity is defined as high or low. High-granularity data contains transaction–level data. Low granularity has low-level information only, such as that found in fact tables.
Q. What is Data Sparsity, and How Does it Impact Aggregation?
A. Data Sparsity defines how much data we have for a model’s specific features. If there is insufficient information stored in the dimensions, then more space is needed to store these aggregations, resulting in an oversized, cumbersome database.
Q. What are Recursive Relationships, and How Do You Rectify Them?
A. Recursive relationships happen when a relationship exists between an entity and itself. For instance, a doctor could be in a health center’s database as a care provider, but if the doctor is sick and goes in as a patient, this results in a recursive relationship. You would need to add a foreign key to the health center’s number in each patient’s record.
Q. Why Are NoSQL Databases More Useful than Relational Databases?
A. NoSQL databases have the following advantages:
1. They can store structured, semi-structured, or unstructured data.
2. They have a dynamic scheme, which means they can evolve and change as quickly as needed.
3. NoQSL Database has sharding, the process of splitting up and distributing data to smaller databases for faster access.
4. They offer failover and better recovery options thanks to the replication.
5. It’s easily scalable, growing or shrinking as necessary.
CONCLUSION:
In the world of data-driven decision-making, mastering data modeling is an essential skill for any data professional. Through this blog, we’ve covered key questions and answers that can help you build a strong foundation and prepare for an interview effectively. Understanding concepts like normalization, Scheme design, and relationship mapping will not only boost your interview performance but also enhance your ability to solve real-world problems. Keep learning, and get ready to excel in your data modeling journey!
