Extracting meaningful information from your large datasets can be challenging. Nonetheless, studying the key question, “What is data mining?” can offer deep insights to mitigate this problem. In addition, using the age-old and proven science of statistics in modern data analytics can save a lot of time, effort, and money for you. Statistics brings economy to data analytics because you study only a part of a large dataset to derive useful business information. In addition, statistics has proven itself across several sciences and countless applications for more than two centuries. Consequently, this science also offers reliability when you analyse large datasets.

For today’s large datasets, this blog illustrates for you:

- What is data mining
- What is statistics
- How data mining and statistics work together
- How statistics for data science presents several advantages.

**What is Data Mining?**

Simply stated, data mining is the science of discovering useful data patterns in large datasets. These patterns provide vital information to organisations to support critical business decisions and strategising. For this reason, Knowledge Discovery in Databases (KDD) is a term often used to describe data mining. Data mining is technology-intensive. Data mining tools provide specific functionalities to automate the use of one or a few data mining techniques. Data mining software, on the other hand, offers several functionalities and presents comprehensive data mining solutions.

However, these two terms are frequently used interchangeably.

While several technologies are involved in data mining, the following are most commonly used:

- Database Management: Today, data is distributed across many machines within organizations and across the Internet. The discipline of organizing such disparate data for easy access and analysis is database management.
- Artificial Intelligence: This technology mimics human intelligence. If an analyst can carry out a complex, data and rule-based procedure, so can AI.
- Machine Learning: Given the complexity of today’s digital life, intelligent machines are a need, not a luxury. Machine learning involves teaching machines to make intelligent decisions and carry out complex tasks based on data.
- Pattern Recognition: This technique involves analysing how myriad data points are interrelated in a meaningful way to extract useful information.
- Data Visualisation: A visual abstraction of tons of data can instantly convey several valuable attributes of such large datasets.

Pattern recognition is used to

- Segment data based on set criteria, using specialized algorithms;
- Use algorithms to discover regularities in large datasets;
- Use a combination of explorative and descriptive pattern recognition to extract useful information from data;
- Apply the derived information in business and technical areas like stock markets, sentiment analysis, face detection, voice recognition, and so on.

**Introduction to Statistics**

The answer to “What is Statistics” lies in descriptive and inferential statistics. Descriptive statistics is used to:

- Draw a small sample from a large population and deduce useful information about that population;
- Arrive at data centrality by calculating the mean, median, and mode of a given data set;
- Analyze the spread of data;
- Understand the distribution of data;
- Choose tools for statistical analysis corresponding to the observed data type;
- Exclude data, called outliers, to ensure only relevant data is used for analyses.

To understand centrality, consider the Google Play Store. If you want to download an app, you would first check its average rating from many customer reviews. If the average rating is high, say 4.1, you would perceive the app favourably and proceed to download it. If the average rating is 3.0, you would look for another similar app with a higher rating. The same example illustrates data spread. If there are several 1-star ratings, along with 4- and 5-star ratings, then you would be more sceptical. A large spread indicates high variation in the variable being measured, something that usually indicates inconsistencies. However, if most reviews are in the 3 to 4 range, this is a narrow spread and gives you a positive feel for the app.

Inferential statistics is used, among other applications, to:

- Use a sample to estimate the values of a population’s parameters;
- Carry out hypothesis tests to see if two datasets are similar or disparate;
- Carry out correlation analysis to examine if two variables are interdependent;
- Conduct linear- or multiple-regression analysis to explain causation.

Hypothesis testing is used to mathematically compare two datasets. For instance, you may feel (hypothesize) that your sales volume is the same, or better, than that of your main competitor. You can then use hypothesis testing to mathematically confirm or reject this assumption. Correlation analysis is a simple tool to isolate the variables of interest from numerous random variables, often observed in large datasets, to see which business variables significantly affect the desired business outcome.

While correlation analysis broadly indicates dependence but not necessarily causation, linear regression identifies causation and is used to predict how one causative variable will affect another dependent variable. Multiple regression, however, is a statistical model that more closely represents real-life situations: How multiple causative variables affect one or more dependent variables (outcomes or results). With this brief on what is data mining and an intro to statistics, we can now examine some ways in which data mining and statistics can be used together.

### How Data Mining Works with Statistics for Knowledge Extraction

1. Descriptive statistics is typically applied to scrutinize which datasets should be selected for meaningful analyses and decision-making. For instance, to improve sales, you can quickly identify offices showing low *average *sales to analyze the root cause for poor sales. In a manufacturing process, machines and/or operators producing parts that have a high part-to-part *variation *(spread) can be quickly identified—from hundreds of machines and employees—for a higher level of quality checks. Data visualization can be used to instantly understand the distribution of data and use the appropriate analytical tools that correspond to a particular distribution (Normal, Poisson, uniform, etc.).

Descriptive statistics is typically applied to scrutinize which datasets should be selected for meaningful analyses and decision-making. For instance, to improve sales, you can quickly identify offices showing low *average *sales to analyze the root cause for poor sales. In a manufacturing process, machines and/or operators producing parts that have a high part-to-part *variation *(spread) can be quickly identified—from hundreds of machines and employees—for a higher level of quality checks. Data visualization can be used to instantly understand the distribution of data and use the appropriate analytical tools that correspond to a particular distribution (Normal, Poisson, uniform, etc.).

2. **Correlation Analysis in data mining:**

Data mining involves minute analyses of huge datasets. Given a business context, correlation analysis can be used to select only those variables that are relevant in that context.

**3. Hypothesis testing:**

Hypothesis testing is used to reliably compare various statistical attributes, like average and spread, to see if two large datasets are similar or different.As an example, consider the rating of your product against that of the market leader in your industry. Although your product and the market leader’s may have a similar average rating, hypothesis testing could indicate that the spread of feedback ratings for your product is higher. This means, customers are consistently giving a higher rating to the market leader’s product, while they are giving both low and high ratings to your products. This revealed inconsistency in your product’s ratings presents an opportunity for improvement.

**4. Linear and Multiple Regression: **

In the typically large datasets that you would encounter in data mining, the high number of potential causes can be daunting. Linear regression is used to isolate only those causes which significantly affect an outcome. For example, how does delivery time affect customer satisfaction, although the dress sense of the delivery person could be a relevant, yet insignificant variable. Multiple regression is closer to real-life situations than linear regression, because, using multiple regression, you can analyze how several causes affect one output. For instance, how do delivery time and product price, combined, affect customer satisfaction.

**5. Outliers**

Even in large datasets, irrelevant values can significantly affect centrality and spread. As an example, consider a well-conceived, competitively-priced product that consistently receives low feedback ratings on a popular e-commerce portal. This could perplex the seller and some happy customers. However, if many of the low ratings are due to delayed or damaged deliveries, then such reviews can be treated as outliers and excluded to determine what customers are saying about the actual product.

**6. The Curse of dimensionality**

Multiple regression models are used to predict how several independent variables affect more than one outcome. For instance, predicting how *traffic, weather, events *and* road conditions* affect the *safety* and *duration* of travel. However, with the addition of each variable, the uncertainty of a regression model’s predictive accuracy grows exponentially. While accurate prediction requires multiple variables in a model, adding more variables undermines its effectiveness. This is the curse of data dimensionality. Large datasets with many disparate variables, a common challenge encountered in data mining, are prone to this challenge. The challenge, therefore, is to reduce the dimensionality of a model without reducing its accuracy. We show you how two simple statistical approaches meet this goal: Correlation Analysis and Data Visualization.

Notably, variables having a similar effect on the outcomes are highly correlated as well. Therefore, dropping some of these variables will not affect the outcomes considerably. A domain expert can identify which correlated variables to exclude. This drastically reduces the number of variables you work with, without noticeably affecting the accuracy of your model. Moreover, data visualization gives you an instant snapshot of which variables correlate. Correlating variables are visually clustered, as in a 3-d scatter plot, into close groups. You can then visually identify redundant variables to reduce data dimensionality. This way, simple statistical tools can mitigate the curse of dimensionality for you.

**Statistics for Data Science **

As in data mining, statistics for data science is highly relevant today. All the statistical methods that have been presented earlier in this blog are applicable in data science as well. At the heart of data science is the statistics branch of neural networks that work like the human brain, making sense of what’s available. While such neural networks involve some initial configuration, iterative tuning, and the expertise of a data scientist, their downstream efficiency and accuracy pay-offs are extremely high; for instance, higher operating efficiency and considerably lower rates of customer loss to the competition.

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