There is much discussion regarding Data Analytics and Data Mining. Sometimes these terms are used synonymously but there is a difference. What is the difference between Data Analytics vs Data Mining

2 Response posts Substantive needed for the below posts of other students.

In the fields of business, social media, and other scientific areas, data mining and data analysis bring immense benefits. The same can be applied to the education system to build models that help in understanding the actual effects of the system on students and the relation between the course and students. The emergence of web-based learning can be additional reasons for data mining and analytics in the education system. Building courses that are more tailormade for the students can be useful for the system and provide instruments to improve the experience(Marcu et al., 2019). The mechanism of applying methods to mine data from the education system to understand student behavior is called Educational Data Mining(EDM). The process of extracting knowledge from the data collected by analysis is called Learning Analysis.

Over the past ten years, the research development in the field of data mining of the educational system has increased tremendously(Marcu et al., 2019). There are a growing number of web-based classes, university learning, and online live classes. With that, the need to cater to each student with tailormade material has also increased. Understanding students' and teachers' needs and appropriately delivering results require data mining on this data. Traditional methods of research are time-consuming and require a lot of material things. To understand how a school system works, for example, we can collect data such as the number of teachers to a student ratio, students' performance in the examinations or we can dig deeper and mine for data such as the geographical location of the school, socio-economic status of the town the school in, etc. But soon the factors may change, and we need new information about the school. In such cases, the process has to start from the beginning again. But with EDM, it is more efficient. Analysis of existing data with EDM tools will be very useful(Marcu et al., 2019).

It is said that learning is the product of the interaction between students and the teacher or teaching organization(Elias & Lias, 2011). Traditionally, the assessment of learning is deduced by the evacuation of the students' grades. Additionally, answering questions like how can a student improve, how a teacher can change the style of teaching, or in this case hoe can an interface change help students in learning better will help in assessing the learning. The wish to improve education quality is increasing more than ever. With the help of Learning Analytics on the collected data, meaningful and useful knowledge can be derived (Liu & Fan, 2014). Although data analytics was initially used to assess a company's sales, with the evolution of web technologies, tools like web analytics can be used to answer the questions on the web education data and provide useful information. The Web Analytics tools will analyze the data, such as student behavior on a website. It can explain how many times a particular module can be selected, etc. The information collected can be used in making the website in such a way that it is easy for the students to use and learn, thereby improving the experience with learning analytics.

In conclusion, while EDM and LA are different, LA can be seen as an extension to the EDM. In some cases like web analytics, EDM and LA can be used synonymously as the data is collected and analyzed at the same step.

As we know that we are leaving in the technology era and daily so much of data is transferred across and generates vast amount of data- which is called big data. Big data is going to helpful for organizations to improve their productivity, operations, and many more by analyzing the data.

Data Mining: Big data is volumes, comes of different domains, structured in different formats and different techniques and methods need to perform on the data for extracting on what we need. Data mining is defined as the science of extracting useful information from large volumes of data is called Data mining (Hand, Mannila & Smyth,2001). Data mining uses three key areas are statistics, artificial intelligence, and machine learning.

Data Analytics: Data Analytics is the science of analyzing data and goal of discovering useful information for decision-making. Data Analysis is used in different business, science and many more domains. (Hellerstein & Joseph, 2008).

Differences between Data Mining and Data Analytics:

Data Mining:

Data mining is a process that focuses on discovering patterns in large data sets using methods of statistics, machine learning and data base systems and it is used to transform the information into good format for further use.
It helps in identifying logical relationships and patterns to draw a conclusion about trends in consumer behavior.
Data mining is a subset of data analysis. All the data analysis does not require data mining technique.

Example of Data Mining:

Data mining is used in various fields. For example, ecommerce websites like Amazon it uses Artificial intelligence and machine learning for product recommendation systems. We knew that so many products and brands of different categories on websites. But after buying one product you will be able to see the similar products that you already brought. This type of recommendation system helps in buying the future products easy. These kinds of recommendation achieved by using behavioral trends of searching and purchasing the products data with the help of data mining techniques.

Data Analysis:

Data Analysis is the science of analyzing raw data and make conclusions about that information and helps in optimizing business process.
Data analysis is of several types descriptive, text analytics, predictive analysis and many more.

Example of Data Analysis:

There are various examples out there for big data analytics. For example, Amazon fresh and whole foods uses data analytics to improve innovation and product development and also helps in creating the greater value in the larger market. It is used to understand and investigate how customers buy groceries and how suppliers interact with the grocer.

Question from professor on the initial post:

Anil - Your example from the telecommunications industry is good. I would encourage more discussion on this topic of understanding customer consumption. For example, consider how understanding usage could be used by utilities responsible for water and electricity.