Manipulating Data

Understanding Data Manipulation

Data manipulation involves modifying, organising, and summarising data to derive useful information.
Various operations on data including sorting, filtering, grouping, summarising, calculation, and conversion are all a part of data manipulation.
The ability to manipulate data is crucial for analysing and interpreting data effectively.

Sorting involves arranging data in a specific order, either ascending or descending. This aids in the quick retrieval of data.
Filtering involves removing unnecessary data and focusing on relevant data. This can simplify data analysis.
Grouping is the process of categorising similar data together, which can make the data more comprehensible.
Summarising is a process to aggregate data, which can include calculations such as sum, average, minimum, maximum, and count.
Calculation involves performing mathematical operations on data, which can include addition, subtraction, multiplication, and division, among others.

Data conversion is the process of changing the data type of a variable or converting data to a different unit or format.
It can become necessary when one data type needs to be converted to another type to make it compatible with the other functions or methods.

Commonly used software tools for data manipulation include spreadsheet applications like Excel, database management systems like Access, and statistical software like SPSS.
Each of these tools provides different functions and commands for data manipulation.

In database management, data manipulation languages (DML) like SQL are used to manipulate the data held inside the database.
The main commands of DML include SELECT, INSERT, UPDATE, and DELETE.
SELECT is used to query and retrieve data. INSERT is used to add new data. UPDATE is used for changing existing data. DELETE is used to remove data.

Data manipulation is crucial to get meaningful insights from data, identify patterns and trends, and make informed decisions.
It aids in the efficient storage, retrieval, and maintenance of data.
It also plays an integral role in ensuring data quality and integrity.