Data Analytics and Data Mining
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? Please provide an example of how each is used. Also explain how you may use data analytics and data mining in a future career. Lastly, be sure to utilize at least four/five scholarly source from either the University library or Google Scholar.
Sample Answer
Data Analytics and Data Mining
Difference between Data Analytics and Data Mining
Data mining is collecting and deriving raw data, with its effectiveness depending on how the data is structured (Vadim, 2018). On the other hand, data analytics is the process of examining data sets and coming up with conclusions and can be performed on structured, semi-structured, and unstructured data. Data mining tries to create a meaningful function in data and does not include visualization tools (Hariri et al., 2019). On the other hand, data analytics aims at proving the hypothesis of data to help in decision making and is constantly led by result visualization. Data mining results are trends and patterns compared to data analytics, which outputs a set of verified hypotheses (Hariri et al., 2019). Data analytics involves a series of steps starting from preparing raw data, cleaning, transforming, modeling, and then presenting it in different forms such as charts.
Data mining entails the interaction of different systems such as machine learning, database, statistics, mathematical and scientific models to identify trends and patterns ((Vadim, 2018). Data analytics mainly requires one to use the knowledge of statistics, computer science, subject knowledge, mathematics, and machine learning to understand the data mined using analytics models (Hariri et al., 2019). Data mining tries to extract and discover meaningful structures and patterns in the data, while data analytics focuses on developing models, testing, hypothesis proposal, and explanation using analytical methods.
Examples of how each is used
Data mining is significantly being used in the education sector for educational enhancement. It has sprawled new research areas in the education sector, changing normal learning. Data mining is being used to exploit the large data volume in the education sector (Marcu & Danubianu, 2019). This data is collected from different areas such as social media, business environment, and other scientific areas. Education is focused on adapting different data models to analyze online reality evidence for training activities. Increased use of computer-based pre-university learning and the development of web-based courses has increased the popularity and adoption of data mining in the education sector (Marcu & Danubianu, 2019).
Data analytics has gained significant popularity in the medical care, where data analytics is used to increase the quality of care and patient outcome (Chen et al., 2018). Data analytics enables healthcare facilities to identify and predict adverse health events being experienced and initiate necessary interventions to prevent them. In the medical industry, data analytics plays a critical role in creating a framework where healthcare professionals and key stakeholders can decide treatment methodology, care plan to be adopted and necessary changes requited based on the research conducted (Chen et al., 2018). It acts as the basis for implementing evidence-based practices in healthcare.
Use of Data Analytics and Data Mining in Future
In my future career, I may use data analytics and data mining to manage a large customer base. The two will help me identify hidden customer insight to improve and optimize marketing campaigns, relationships, and business forecasting. I will use data mining as the basis for determining the different patterns and models present in this environment (Vicario & Coleman, 2020). I will then use data analytics to develop verified hypotheses and insight into the collected data for effective decision-making. This will help my company offer more targeted campaigns that consider the customer needs, thus maximizing profit.
