You can start answering this question by saying that you will start with finding the objective of the given problem and defining it so that there is solid direction on what needs to be done. The purpose of asking this question is that the interviewer wants to understand how you approach a given data problem and what is the thought process you follow to ensure that you are organized. How will you begin with and what are the steps you will follow? You are assigned a new data analytics project. So, this guarantees that the database will keep track of pending changes in such a way that even if there is a power loss, crash or any sort of error the server can recover from an abnormal termination. So basically each and every transaction is independent.ĭurability: Durability makes sure that your committed transaction is never lost. Isolation: Isolation keeps transactions separated from each other until they’re finished. So, this basically makes sure that the transaction never leaves the database without completing its state. So, even if a single transaction fails, then the entire transaction fails and the database state is left unchanged.Ĭonsistency: This feature makes sure that the data must meet all the validation rules. Here a transaction refers to a single operation. If you have to define each of these terms, then you can refer below.Ītomicity: Refers to the transactions which are either completely successful or failed. This property is used in the databases to ensure whether the data transactions are processed reliably in the system or not. In the dialog box of Less Than, specify the value as 0.Īnswer: ACID is an acronym for Atomicity, Consistency, Isolation, and Durability. Go to the Highlight Cell Rules and click on the Less Than option. Go to the Home tab and click on the Conditional Formatting option
Select the cells which you want to highlight with the negative values. How can you highlight cells with negative values in Excel?Īnswer: You can highlight cells with negative values in Excel by using conditional formatting. You can refer to the below image to know the various ways to deal with missing data. It is the process of identifying and removing errors to enhance the quality of data. What is data cleansing and what are the best ways to practice data cleansing?Īnswer: Data Cleansing or Wrangling or Data Cleaning. This is one of the value-added data analysis technique that is used industry-wide while processing a large set of data.ĥ. The main use of this is to understand and take an executive decision whether the data that is available is readily used for other purposes.Īnswer: The clustering is defined as a process of grouping a definite set of objects based on certain predefined parameters. It can be performed interactively with the help of data wrangling tools, or as batch processing through scripting.Īnswer: The data profiling is nothing but a process of validating or examining the data that is already available in an existing data source, so the data source can be an existing database or it can be a file.
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There are different ways of performing data cleansing in different software and data storage architectures. The purpose of all these activities is to make sure that the database contains only good quality data, which can be easily worked upon. What do you understand by data cleansing?Īnswer: Data Cleansing, also referred to as data scrubbing, is the process of modifying or removing data from a database that is incomplete, inconsistent, incorrect, improperly formatted, or redundant. Today, data analysts can be found in a wide array of industries utilizing programming languages and statistics to pull, sort and present data in many forms in the benefit of the organization, people, and/or company.Ģ. In 1980, the development of the relational database gave a new breath to data analysts, which allowed them to use Sequel (SQL) to retrieve data from databases.
Early data analysts use tabulating machines to count data from punch cards. With the development of computers and an ever-increasing move toward technological intertwinement, data analysis began to evolve. In some other later, but still early forms, data analysis can be seen in censuses, taxing, and other governmental roles across the world. Analyzing data begins with its roots in statistics which, itself, stems into a long history into the period of pyramid building in Egypt.
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