Having identified the business problem, a data analyst has to go through the data provided by the client to analyse the root cause of the problem.
The three elements include the entities for which someone is seeking information, the attributes of those entities, and the relationships between the entities.
This is the most crucial step of the data analysis process wherein any data anomalies (like missing values or detecting outliers) with the data have to be modelled in the right direction.
Thinking mathematically, it is the number of elements in a set. Thinking in the database world, cardinality has to do with the counts in a relationship, one-to-one, one-to-many, or many-to-many.
Database candidates should be familiar with most if not all of these without needing to lookup definitions. Some of the other normalization forms are less commonly known/used, but could theoretically be asked. Knowing the differences between second and third is probably a good idea.
A measure of the dispersion of data that is shown in a box plot is referred to as the interquartile range. It is the difference between the upper and the lower quartile.
A transaction is a single logical (atomic) unit of work, in which a sequence of operations (or none) must be executed. A transaction has a defined beginning and end. You can commit or roll back a transaction.
Logic Regression can be defined as:
This is a statistical method of examining a dataset having one or more variables that are independent defining an outcome.
Data Validation is performed in 2 different steps-
☛ Data Screening – In this step various algorithms are used to screen the entire data to find any erroneous or questionable values. Such values need to be examined and should be handled.
☛ Data Verification- In this step each suspect value is evaluated on case by case basis and a decision is to be made if the values have to be accepted as valid or if the values have to be rejected as invalid or if they have to be replaced with some redundant values.
A LEFT JOIN returns all records from the left table, even when they do not match in the right table. Missing values become NULL. In a similar manner, a RIGHT JOIN returns all records from the right table, even when they do not match those in the left table. Missing values become NULL.