Are you preparing for Data Mining job interview and wondering what questions you might be asked? In this blog we have hand-picked the questions you are likely to be asked in Data Mining job interview and provided to-the-point answers to each one of them so as to help you prepare better for Data Mining job interviews.
Always keep in the mind that, only academic knowledge is not enough to crack an interview. Employers expects from the candidate to have practical knowledge and hands-on experience as well. This Data Mining Interview Questions designed by industry expert will help you to gain practical knowledge of Data Mining.
MOLAP Multidimensional Online Analytical processing
In MOLAP data is stored in form of multidimensional cubes and not in relational databases.
ROLAP Relational Online Analytical processing
The data is stored in relational databases.
HOLAP Hybrid Online Analytical processing
HOLAP is a combination of the above two models. It combines the advantages in the following manner:
Answer: Data warehousing is merely extracting data from different sources, cleaning the data and storing it in the warehouse. Whereas data mining aims to examine or explore the data using queries. These queries can be fired on the data warehouse. Explore the data in data mining helps in reporting, planning strategies, finding meaningful patterns etc.
E.g. a data warehouse of a company stores all the relevant information of projects and employees. Using Data mining, one can use this data to generate different reports like profits generated etc.
Answer: Deleting data from data warehouse is known as data purging. While loading data into staging or in the target table fresh data loading may be needed every time. In this scenario, data purging is needed to stage or target table prior to loading fresh data. Usually junk data like rows with null values or spaces are cleaned up. Data purging is the process of cleaning this kind of junk values.
Answer: A data cube stores data in a summarized version which helps in a faster analysis of data. The data is stored in such a way that it allows reporting easily.
E.g. using a data cube A user may want to analyze weekly, monthly performance of an employee. Here, month and week could be considered as the dimensions of the cube.
Answer: Data mining can be used in a variety of fields/industries like marketing, advertising of goods, products, services, AI, government intelligence.
The US Federal Bureau of Investigation uses data mining for screening security and intelligence for identifying illegal and incriminating e-information distributed over internet.
OLTP: Online Transaction and Processing helps and manages applications based on transactions involving high volume of data. Typical example of a transaction is commonly observed in Banks, Air tickets etc. Because OLTP uses client server architecture, it supports transactions to run cross a network.
OLAP: Online analytical processing performs analysis of business data and provides the ability to perform complex calculations on usually low volumes of data. OLAP helps the user gain an insight on the data coming from different sources (multi dimensional).
Answer: Custom rollup operators provide a simple way of controlling the process of rolling up a member to its parents values.The rollup uses the contents of the column as custom rollup operator for each member and is used to evaluate the value of the member’s parents.
If a cube has multiple custom rollup formulas and custom rollup members, then the formulas are resolved in the order in which the dimensions have been added to the cube.
Exploration: This stage involves preparation and collection of data. it also involves data cleaning, transformation. Based on size of data, different tools to analyze the data may be required. This stage helps to determine different variables of the data to determine their behavior.
Model building and validation: This stage involves choosing the best model based on their predictive performance. The model is then applied on the different data sets and compared for best performance. This stage is also called as pattern identification. This stage is a little complex because it involves choosing the best pattern to allow easy predictions.
Deployment: Based on model selected in previous stage, it is applied to the data sets. This is to generate predictions or estimates of the expected outcome.
Answer: Data warehousing can be used for analyzing the business needs by storing data in a meaningful form. Using Data mining, one can forecast the business needs. Data warehouse can act as a source of this forecasting.
Answer: Discreet data can be considered as defined or finite data. E.g. Mobile numbers, gender. Continuous data can be considered as data which changes continuously and in an ordered fashion. E.g. age.
Answer: Models in Data mining help the different algorithms in decision making or pattern matching. The second stage of data mining involves considering various models and choosing the best one based on their predictive performance.
Answer: A decision tree is a tree in which every node is either a leaf node or a decision node. This tree takes an input an object and outputs some decision. All Paths from root node to the leaf node are reached by either using AND or OR or BOTH. The tree is constructed using the regularities of the data. The decision tree is not affected by Automatic Data Preparation.
Answer: Naïve Bayes Algorithm is used to generate mining models. These models help to identify relationships between input columns and the predictable columns. This algorithm can be used in the initial stage of exploration. The algorithm calculates the probability of every state of each input column given predictable columns possible states. After the model is made, the results can be used for exploration and making predictions.
Answer: Clustering algorithm is used to group sets of data with similar characteristics also called as clusters. These clusters help in making faster decisions, and exploring data. The algorithm first identifies relationships in a dataset following which it generates a series of clusters based on the relationships. The process of creating clusters is iterative. The algorithm redefines the groupings to create clusters that better represent the data.
Answer: Time series algorithm can be used to predict continuous values of data. Once the algorithm is skilled to predict a series of data, it can predict the outcome of other series. The algorithm generates a model that can predict trends based only on the original dataset. New data can also be added that automatically becomes a part of the trend analysis.
E.g. Performance one employee can influence or forecast the profit.
Answer: Association algorithm is used for recommendation engine that is based on a market based analysis. This engine suggests products to customers based on what they bought earlier. The model is built on a dataset containing identifiers. These identifiers are both for individual cases and for the items that cases contain. These groups of items in a data set are called as an item set. The algorithm traverses a data set to find items that appear in a case. MINIMUM_SUPPORT parameter is used any associated items that appear into an item set.
Answer: Sequence clustering algorithm collects similar or related paths, sequences of data containing events. The data represents a series of events or transitions between states in a dataset like a series of web clicks. The algorithm will examine all probabilities of transitions and measure the differences, or distances, between all the possible sequences in the data set. This helps it to determine which sequence can be the best for input for clustering.
E.g. Sequence clustering algorithm may help finding the path to store a product of “similar” nature in a retail ware house.
Answer: Data mining is used to examine or explore the data using queries. These queries can be fired on the data warehouse. Explore the data in data mining helps in reporting, planning strategies, finding meaningful patterns etc. it is more commonly used to transform large amount of data into a meaningful form. Data here can be facts, numbers or any real time information like sales figures, cost, meta data etc. Information would be the patterns and the relationships amongst the data that can provide information.
Answer: SQL Server data mining offers Data Mining Add-ins for office 2007 that allows discovering the patterns and relationships of the data. This also helps in an enhanced analysis. The Add-in called as Data Mining client for Excel is used to first prepare data, build, evaluate, manage and predict results.
Answer: Data mining extension is based on the syntax of SQL. It is based on relational concepts and mainly used to create and manage the data mining models. DMX comprises of two types of statements: Data definition and Data manipulation. Data definition is used to define or create new models, structures.
Data manipulation is used to manage the existing models and structures.