Data mining and statistics are integral components in the lives of data scientists. They study these concepts thoroughly and get good hands-on practice before appearing for a data science job interview. They are often asked to use various data mining and statistical software programs in their workplace almost every day. Data mining and statistics are so intermingled that they seem inseparable. Although they share multiple similarities, they have several differences.
Knowing their connection and differences is better before pursuing a data science online course from any website like the Simplilearn online learning platform. This article is the perfect destination if you wish to know the similarities and differences between data mining and statistics. You might be an absolute beginner or expert in data science. You can get answers to your confusion here, regardless of whether you are a newcomer or a specialist in the landscape.
So, fuel up and prepare yourself for the informational ride. Let’s dive into the article and understand the similarities and differences between statistical analysis and data mining.
What do you mean by data mining?
Data mining is the methodology of sifting through Big Data sets to find patterns and relationships that you can use to address business problems. Enterprises can use data mining techniques and technologies to forecast future trends and make better business decisions.
Data mining is an essential element of data analytics and one of the fundamental disciplines in data science. Advanced analytics techniques are used to extract meaningful information from large data sets. Data mining is a crucial phase in the knowledge discovery in databases (KDD), a data science methodology for obtaining, processing, and analyzing data at a more granular level. Although data mining and KDD are often used interchangeably, they are more frequently considered separate concepts.
An introduction to statistics
Statistics is the study and development of methods for collecting, analyzing, interpreting, and presenting empirical data. Statistical researchers use various mathematical and computational techniques to develop procedures and investigate the underlying hypothesis. Statistics is a highly multidisciplinary field; statistical analysis has applications in almost all scientific areas. Research concerns from many scientific fields stimulate the creation of new statistical methods and theories.
Data Mining vs. Statistics – The Connection
Statistics has data mining and vice versa. These subjects are dependent on each other. As a data professional, you will be required to simultaneously leverage both to reach the desired conclusion.
So, it’s better to understand how data mining relies on statistics and vice versa. Let’s investigate the relationship between statistics and data mining down below!
- Many data mining techniques were developed by statisticians or are now integrated into the statistics realm. Several techniques leveraged in data mining were invented by statisticians or are now integrated into the statistics domain. Many statistical software programs, such as SAS, S-Plus, SPSS, and STATISTICA, are promoted as data mining software rather than statistical software.
- It can be tedious to say whether a method or algorithm belongs in the discipline of statistics or data mining when looking at it. To overcome similar challenges, data miners and statisticians utilize identical methodologies. However, designing and implementing experiments for organizations without data mining techniques might be difficult.
- Statistics allow for the incorporation of predictive analytics and the development of numerous categories that can influence the results. Without numbers, practical analysis is impossible.
- Advanced statistical approaches performed during the data mining process can assist firms in increasing revenues, maximizing operating efficiency, lowering expenses, and improving customer satisfaction. The usage of statistical software in data mining may provide firms an edge over their competitors by assisting in increasing sales and driving company execution.
To be competitive in today’s market, keeping up with market trends and making predictions about future outcomes is a constant task.
Data Mining vs. Statistics – The Differences
We have already walked through various similarities between statistics and data mining. They share immense differences. Despite having dependencies, data mining is not equivalent to statistics.
So, it’s better to brush up on our knowledge of the justified points for data mining vs. statistics. Now, it’s time to review the differences between them. Let’s jump right in!
Data mining collects usable information, patterns, and trends from large data sets and puts them to use to make a data-driven decision. On the other hand, statistics related to the study and display of numerical data are critical components of all data mining techniques.
- Data type
Data mining can use numeric or non-numeric data. Statistical analysis, on the other hand, uses just numeric data.
- Data collection importance
Data collecting is very crucial in statistics. In data mining, though, it isn’t essential. It’s a mandatory idea that many pupils neglect.
Clustering, classification, association, neural networks, sequence-based analysis, visualization, and more types of data mining exist. In contrast, descriptive and inferential statistics are the two forms of statistics.
- Compatible data size
Data mining is well suited to extensive data collection. On the other hand, statistics are better suited to smaller data sets.
- The inductive and deductive difference
Inductive reasoning is used in data mining. It refers to the creation of new theories based on data. Statistics, on the other hand, is a deductive procedure. It refrains from making any predictions.
- Data cleaning importance
Data cleaning is a step in the data mining process. Clean data is utilized to implement statistical methods in statistics.
- Ease of automation
Data mining is simple to automate because it requires minimal user interaction to confirm the model. On the other hand, statistics necessitates human engagement to validate the model; hence, it has a complex automaton.
Financial Data Analysis, Retail Industry, Telecommunication Industry, Biological Data Analysis, and Certain Scientific Applications are just a few data mining applications. On the other hand, statistical applications include biostatistics, quality control, demography, operational research, etc.
Data mining and statistics are intriguing. Aren’t they? Their subtle similarities and differences make learning about them more exciting than ever. It’s high time to learn data mining and statistics to pave your way into the data-driven world.