Data Mining interview questions related are focused to check your knowledge and skill to work ass Data Analysts, AI developers, Data Scientists, Data Consultants etc. These questions discussed in this post provide a concise and to the point overview of the topic for all levels of Data Mining Interview.
What is the difference between OLAP and OLTP?
Online analytical processing (OLAP) is used for performing multi-dimensional analysis at high speeds on large volumes of data. The data is mostly from a centralized data store. OLAP is a great technique for data mining, business intelligence and for business reporting functions like financial analysis and sales forecasting.
Online transactional processing (OLTP) is used in the real-time execution of large numbers of database transactions by great numbers of resources, typically over the Internet. OLTP systems are used in our everyday transactions for ex. ATMs, grocery purchases, hotel reservations etc. OLTP can also be used in password changes and text messages storage and processing.
What is the difference between data warehousing and data mining?
| Data Mining | Data Warehousing |
| Data mining refers to a process of extracting relevant patterns from a dataset. | Data Warehousing is the process of compiling, sequencing, and organizing groups of data in a commonly accessible database. A data warehouse is used for dataset management and retrieval |
| Data mining can use concepts of Artificial Intelligence, statistics, databases, and machine learning systems | Data warehouses can be classified as topic-oriented, integrated, non-volatile and time-varying |
| Data mining use pattern recognition logic to identify patterns | Data warehousing does data extraction and storage for better reporting practices |
| Can be done by business owners with the some assistance of data analysts in some cases | This is performed by the organization’s data scientists and technical teams |
Define Clustering and how is it used in Data Mining?
Clustering is the technique that is used in Data mining to group similar data. Cluster analysis is used in many areas such as pattern recognition, image analysis, retrieval of information, bioinformatics, machine learning etc.
Depending on the cluster models, many clusters can partition information into a specific data set. There are various clustering algorithms that can be selected based on the usage. Some methods of clustering in data mining are:
- Density-based Method
- Centroid-based Method
- Hierarchical Method
- Grid-Based Method
- Model-Based Method
What are the different types of Data Mining?
Data Mining can be classified into the types:
- Selection
- Data cleaning
- Integration
- Pattern evaluation
- Data transformation
Explain all stages of Data Mining.
Stages of data mining are as follows:
Preparing data:
The data is prepared based on the problem statement of the given data. This is the first step in data mining and is crucial to prepare data and remove unrelated/null values to avoid improper processing.
Exploring the data:
Various explorations like calculating the maximum, minimum, calculating mean, median, other analytical evaluations are done in this step.
Building data models:
A data mining structure is created by defining columns to be used in the further steps.
Validating the data models:
The data mining model created in the previous step is validated in this step.
Deploying and updating the models:
The model is deployed for the clients to use.
What is Bayesian and how does noisy data affects the algorithm?
Adding noise reduces the quality of Bayesian results as it does for Frequentist and Likelihoodist methods. It will also slow down the model. This can be seen with a simple, degenerate example.
Define Data Purging
Data purging is an important procedure in database management systems. This helps maintain the relevant data within a database. This refers to the removal or deletion of unnecessary NULL values from rows and columns.
It is important to first purge any data that does not belong to the new data that we are trying to load into the database. We need to quickly remove junk data from our dataset as it will reduce the database’s performance.
Define Frequent Pattern Mining
Frequent Pattern Mining is a technique is used to find irregularities in data. It is used to find patterns that occur frequently in a data set (mostly large). It is used in – Market basket analysis (https://csveda.com/market-basket-analysis-association-rules-in-r-programming/), sequence analysis etc.
It was first introduced for frequent itemsets and association rule mining. It aims to find regularities behavior of customers in supermarkets, on-line shopping websites etc.
Differentiate between data mining and data analysis
| Data Mining | Data Analysis |
| It is used to discover hidden patterns in raw data. | It is involved in examining data to find conclusions and make further actions. |
| Requires mathematical and statistical models and tools | Requires Analytical and business intelligence models and tools |
| It generally does not require visualization | Data Visualization is a must |
| Primary motive is to make data useable. | Primary motive is to make data driven decisions. |
| It involves machine learning, statistics as well as databases. | It requires the in depth knowledge of computer science subjects and mathematical subjects such as statistics, probability along with the knowledge of Machine Learning. |
What is similarity in Data Mining?
The similarity in Data mining is the measure of how much alike two data objects are in a dataset. The similarity in data mining is usually described as the distance of data points with dimensions representing features of those objects. A small distance between the objects indicates a high degree of similarity and a large distance indicated a low degree of similarity. The similarity is directly dependent application domain of the data.
These are the most frequently asked Data Mining Interview Questions. You can share your experience for a Data Mining job interview.