Data Mining and Data Analytics

Data Mining and Data Analytics

Data mining refers to the process of identifying and discovering patterns in large data sets which involves methods that are at the intersection of statistics and database systems. It is the process by which usable datasets are extracted from a large dataset of raw data. Data analytics, on the other hand, is a superset of data mining. Data analytics may be defined as the process of sourcing, cleaning, transforming and analyzing data to develop meaningful pieces of information and insights that can be used in decision-making (Jaisal, 2018). Both data mining and data analytics are terms that are used interchangeably depending on the context, but there are differences between the two as discussed below.

Differences between Data Mining and Data Analytics

First, data mining is a process in the data analytics process. Data analytics deals with every step that is within the data-driven model. Data mining involves catering the data collection process and gaining meaningful insights. Data analytics makes use of these insights to create a model based on the data. Qualitative and quantitative techniques are used to derive meaningful information. Another difference is that data mining makes use machine learning, statistics and database systems to get the insights from raw data.

On the other hand, data analytics covers data mining, specialized software and tools, text mining and non-numerical data. The purpose of data mining is finding patterns in raw data (Pandurangan, 2017). Data analytics aims at manipulating the data to get an outcome as desired, which will aid in decision making. The final difference is that data mining is very structured, whereas data analytics is less structured.

Application of Data Mining and Data Analytics in Future Career

Data mining and analytics is an important part of software development. This is because, during the software development process, a large amount of data is collected from organizations such as requirements specifications, design diagrams, source codes bug reports and many others. Data mining is used in this case to help in determining useful knowledge and patterns in this data. Data analytics may be used to determine and gather information from this data that is useful in the software development process.