Any company looking to hire a young data analytics professional would expect them to know the very basics of querying data. This not only applies to young professionals, but also to seasoned professionals looking to diversify their skill set. Without delving into the basics that are often applied to data analytics projects, it’s not possible to last long in this fast-paced industry. These are the most used concepts in any data analysis project, which any professional should be able to articulate in an interview to gain the interviewer’s trust.
one. Classification – The concept of simply sorting data sounds very basic, simple and has very little application. However, it is important to understand how a particular tool performs this function, as it greatly affects the performance of your scripts. Sorting data files is also a prerequisite when combining or joining data sets. If the data is not correctly sorted on the primary and secondary keys, it would give incorrect results.
2. union of tables – This is a very powerful feature built into any tool that is capable of querying data sets like SQL databases, SAS, audit command language. It is important for users to understand how the tool processes the data files line by line to create the result of a merge, since different tools attempt the same goal in different ways. For example, in the audit script, both primary and secondary keys are present in the output table, while in SQL Server, the resulting table has only one column. Users need to develop clarity of thought in order to visualize the end result.
3. Identify distinct values – In most data analysis projects, this is a very common query that forms the basis for developing other data points to prepare final reports. Analysts should always consider how to identify unique values from raw data tables to new tables. When using audit command language scripts, the classify command or the summarize command provide this information, and the same can be achieved with SQL-based databases by using the distin keyword.
Four. summarizing data – This is an all time favorite and is on par with the concept of joints. Summarizing a data set for certain values allows users to extract new information about a data set with different fields. In fact, most exploratory queries can start with a few summary commands to understand the data points correctly. For example, summarizing a payroll data set at the employee level would give the number of unique employees and, if desired, the total wages paid to them over a period of time. There may be more such queries, which form the basis of the scope design of an analysis project.
Mastery of these concepts makes any professional ready to work in a variety of tools. This implies that familiarity with these concepts allows users to scale projects across different tools and therefore opens up more opportunities within the industry. It’s quite remarkable how many people aren’t even able to master these basics in today’s workforce.