Artificial Intelligence and Machine Learning are considered the future of the information world, and due to the growing importance of these fields, there is a rising demand for experts in both ML and AI. Since Data Mining is an important part of both of these fields, a solid foundation in Data Mining basics is required if you are going to an interview in ML or AI-related jobs. Here is a collection of popular Data Mining interview questions and Data Warehousing concepts interview questions.

Top 25 Data Mining Interview Questions

These data mining and data warehousing interview questions and answers could most probably be asked in your interview. 

1.    What is the Scope of Data Mining?

It helps automate the process of analyzing and identifying predictive information in a huge amount of databases and datasets. Data Mining tools can help scrape and sweep through a diverse range of data in order to identify a pattern that was previously hidden.

2.    What are the different stages of Data Mining?

The three main stages are:

a.    Exploration

b.    Model Building and Validation

c.    Deployment

3.    Define the Exploration Stage in Data Mining?

The Exploration stage is mainly focused on collecting data from various sources and preparing it for later transformation and cleaning activities. 

4.    Define metadata?

Metadata can simply be defined as data about data. Metadata is the summarized data that takes us to the detailed data.

5.    Name a few Data Mining Techniques.

  • Classification Analysis
  • Association Rule Learning
  • Anomaly or Outlier Detection
  • Clustering Analysis
  • Regression Analysis
  • Prediction
  • Sequential Patterns
  • Decision Trees

6.    What are the different types of Data Mining?

You can classify Data Mining into the following types:

  • Integration
  • Selection
  • Data cleaning
  • Pattern evaluation
  • Knowledge representation
  • Data transformation

7.    Could you give a small introduction to Data Mining processes?

It is a process of discovering hidden information that is valuable by analyzing a huge collection of data. It is very advantageous to many industries. 

8.    Why are the Model Building and Validation stage important in Data Mining?

It is important since, in this stage, data is validated by using different models and is compared to finalize the model with the best performance.  

9.    In Data Mining, what are “Continuous” and “Discrete” data?

“Continuous data” is the data that changes continuously in a well-structured manner. The perfect example of this is age. “Discrete data” is when data is finite and has a specific meaning present in it. The most suitable example of this is gender.

10.    Could you list a few areas in which data mining can be applied?

Data Mining would most likely be used in the fields of:

  • Healthcare
  • Energy
  • Telecommunication
  • Retail
  • E-commerce

11.    What is OLAP?

OLAP, Online Analytical Processing, is the technology that involves complex analytical calculations and is used in various Business Intelligence applications. The main purpose of it is to minimize the query response time while enhancing the performance of reporting.

12.    What is ETL?

Extract, Transform, and Load (ETL) is a software that reads the data from a certain data source and extracts the required subset of data. 

13.    In data mining, what are the required technological drivers?

Query Complexity: In order to analyze a large number of complex queries, we require a very powerful system.

Database size: In order to process and maintain a huge collection of data, we require powerful systems.

14.    What does ODS stand for?

ODS stands for Operational Data Store.

15.    What is the Syntax for Interestingness Measures Specification?

Interestingness thresholds and measures are user-specified with the statement – with <interest_measure_name> threshold = threshold_value

16.    What is the Dimension Table?

A table that contains all the attributes of measurements of data that are stored in the fact table is called a Dimension Table.

17.    What does STING stand for?

STING stands for Statistical Information Grid. It is a multi-resolution, grid-based clustering method in which objects are stored in rectangular cells.

18.    What are a few data mining basic issues?

A few issues of data mining are:

  • Uncertainty handling
  • Dealing with noisy data
  • Dealing with missing values
  • Data selection

19.    What are the few methods of clustering?

  • Portioning method
  • Hierarchical method
  • Density-based method
  • Grid-based method
  • Model-based method
  • Constraint-based method

20.    What do OLTP and OLAP stand for?

OLTP- Online Transactional Processing

OLAP- Online Analytical Processing

21.    Define Data Warehousing in one sentence?

It is the repository of integrated information that is readily available for analysis and queries.

22.    What does ETL stand for?

ETL stands for Extract, Transform, and Load.

23.    What is SCD?

SCD is Slowly Changing Dimensions and is applied to areas where the data record changes over time.

24.    What are the best ETL tools you can use?

  • Oracle
  • Data Stage
  • Ab Initio
  • Informatica
  • Data Junction
  • Warehouse Builder

25.    What are the various levels of Data Mining analysis?

The various levels are:

  • The various levels are:
  • Rule induction
  • Data visualization
  • Genetic algorithms
  • Artificial neural network
  • Nearest neighbour method

Conclusion

Going through these data warehousing and data mining important questions while looking at various other data mining objective questions and answers can help you easily get through the interview for a data scientist job.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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