Data Mining vs. Statistics vs. Machine Learning

Understand the difference between the data driven disciplines-Data Mining vs Statistics vs Machine Learning

Data Mining vs. Statistics vs. Machine Learning
 |  BY ProjectPro

Data science is solely based on data. If your data is good you will get good results else, you might have heard of famous data science proverb – Garbage in Garbage out. A good (rather useful I should say) data science product is like a recipe even if one ingredient is not good, final product will not amuse the audience.

Data Science Products

The picture shown above talks about following important parts of a Data Science product:

  • Data – Data part of it, needs no introduction. For a data science product data is enough but for a good Data Science product good and sufficient data is needed and that is primary task of Data Mining which we will discuss in detail
  • Business – When we try to solve a business problem using data, we need to ask questions even before looking into data. The same problem can be solved in different ways given the business needs. For example, let say an airline wants to increase their customer base. If marketing team is solving this problem they will look for potential target population, operation team will look forward to change flight timings or increasing flights etc.
  • Math – Assuming you have good data and you understand the business objective as well the next part is to solve the problem. Which hypotheses to solve, how to prove/ disprove hypotheses, which method to use in order to solve a particular problem are few areas which are tackled by Math or Statistics
  • Technology – Once you have techniques in mind, you have finalized the hypotheses the next task is to solve the problem in a best possible way and in a shorter time that’s what we need Technology for. You might have built best regression techniques in theory but if you can’t run it on real data in shortest possible time there is no use of that

Linear Regression Model Project in Python for Beginners Part 1

Downloadable solution code | Explanatory videos | Tech Support

Start Project

Assuming you understand your business requirement sufficiently, let’s discuss what do we mean by Data Mining, Statistics & Machine Learning? What are the differences among them?

 

ProjectPro Free Projects on Big Data and Data Science

Data Mining vs. Statistics vs. Machine Learning

 Difference between Machine Learning, Statistics and Data Mining

Data Mining, Statistics and Machine Learning are interesting data driven disciplines that help organizations make better decisions and positively affect the growth of any business.

According to Wasserman, a professor in both Department of Statistics and Machine Learning at Carnegie Mellon, what is the difference between data mining, statistics and machine learning?

“The short answer is: None. They are … concerned with the same question: how do we learn from data?”

Considering Wasserman’s answer, the three disciplines are considerably the same but with minor differences, rather they can be referred to as identical twins which make use of different words and terminology and follow different notations.

Data Mining

Data mining is a very first step of Data Science product. Data mining is a field where we try to identify patterns in data and come up with initial insights.

E.g., you got the data and you identified missing values then you saw that missing values are mostly coming from recordings taken manually.

Few people mistake Data mining with data extraction. Data mining comes into play once you have collected data.

Companies use powerful data mining techniques coupled with advanced tools to extract valuable information out of large amount of data.

E.g., Walmart collects point of sales data from their 3,000+ stores across the world and stores it into their Data Warehouse. Walmart suppliers have access to this database and they identify the buying patterns among Walmart customers and use this to maintain their inventory in future. Walmart data warehouse processes more than a million such queries every year.

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

Data mining uses power of machine learning, statistics and database techniques to mine large databases and come up with patterns.

Mostly data mining uses cluster analysis, anomaly detection, association rule mining etc. to find out patterns in data.

In short Data Mining is finding out hidden and interesting patterns stored in large data warehouses using the power of statistics, artificial intelligence, machine learning and database management techniques.

Statistics

Statistics is the base of all Data Mining and Machine learning algorithms.

Statistics is the study of collecting, analyzing and studying data and come up with inferences and prediction about future.

Major task of a statistician is to estimate population from sample metrics. Statistics also deal with designing surveys and experiments in order to get quality data which can further be used to make estimation about the population. If we have to formally list down the task of statistics, it will be as follows

  • Designing surveys and experiments
  • Summarizing and understanding data
  • Estimating population behavior
  • Prediction or estimation of future

Get FREE Access to Machine Learning Example Codes for Data Cleaning, Data Munging, and Data Visualization

Statistics is used to summarize numbers for example finding out descriptive statistics like Mean, Median, Mode, Standard Deviation, Variance, Percentiles, Testing hypotheses etc.

Machine Learning

Machine learning is a part of data science which majorly focuses on writing algorithms in a way such that machines (Computers) are able to learn on their own and use the learnings to tell about new dataset whenever it comes in.Machine learning uses power of statistics and learns from the training dataset. For example, we use regressions, classifications etc. to learn from training data and use those learnings to estimate test dataset.

Data Mining vs. Statistics - Similarities and Differences Unleashed

The objective of Data Mining and Statistics is to perform data analysis but both are different tools. Data mining process involved modelling, predicting and optimizing a dataset while Statistics describes how efficient a dataset is –more or less.

Data Mining vs Statistics

Data Mining

Statistics

Explorative – Dig out the data first, discover novel patterns and then make theories.

Confirmative – Provide theory first and then test it using various statistical tools.

Involves Data Cleaning

Statistical methods applied on Clean Data

Usually involves working with large datasets.

Usually involves working with small datasets.

Makes generous use of heuristics think

There is no scope for heuristics think.

Inductive process

Deductive (Does not involve making any predictions)

Numeric and Non-Numeric Data

Numeric Data

Less concerned about data collection.

More concerned about data collection.

Some of the popular data mining methods include –Estimation, Classification, Neural Networks, Clustering, Association, and Visualization.

Some of the popular statistical methods include –Inferential and Descriptive Statistics.

Machine Learning vs. Statistics

  • Machine Learning and Statistics both are concerned on how we learn from data but statistics is more concerned about the inference that can be drawn from the model whereas machine learning focuses on optimization and performance.
  • Statistical learning involves forming a hypothesis (making assumptions that are validated before building models) before building a model. In machine learning models, the machine learning algorithms are directly run on the model making the data speak instead of guiding it in a specific direction with initial hypothesis.
  • Statistics is all about sample, population, and hypothesis whereas machine learning is all about predictions, supervised and unsupervised learning.
  • Machine Learning is about building algorithms that help machines emulate human learning whereas Statistics is about converting the data into aggregate numbers which help understand the structure in data.

Get More Practice, More Data Science and Machine Learning Projects, and More guidance.Fast-Track Your Career Transition with ProjectPro

In short,

  • Statistics quantifies data from sample and estimates population behavior
  • Data mining finds out pattern in data
  • Machine learning learns from training data and predicts or estimates future

Data mining vs Machine learning

Data mining and machine learning both come under the common umbrella of Data Science, since they both involve processing and analysis of large amounts of data. Both the techniques are used to solve complex real-world problems. Machine learning can be used as a means of conducting data mining and the data gathered from data mining can be used to train models to apply machine learning techniques.

 

Data Mining

Machine learning

Data mining involves extraction of information from large amounts of unstructured data.

Machine learning is about using algorithms to build a model and train it so that new information can be introduced based on data from previous occurrences.

In data mining, rules are obtained from the data available. 

In machine learning, the algorithm used teaches the computer to learn and comprehend the rules.

Data mining requires human intervention and is created so that the data can be further processed by people.

The idea of machine learning is to teach itself so that there is no dependence on human influence. Human interference in the case of machine learning is mostly limited to setting up the initial algorithms.

In the case of data mining, there is no concept of the system adapting. Data mining is as smart as the users who specify the parameters.

The entire goal of Machine learning is to teach itself to adapt based on the algorithms and new data inputs.

Data mining is all about working on large amounts of raw data to make forecasts for the business.

Machine learning is about applying algorithms to structured data.

 

Recommended Reading

Statistics in data mining 

Many of the techniques used in data mining were either invented by statisticians or are now integrated into the statistics domain. Many statistical software tools such as SAS, S-Plus, SPSS, and STATISTICA are primarily marketed as data mining tools rather than statistical tools. Data miners and statisticians use similar approaches to solve similar problems. However, it can be challenging to design and implement experiments for businesses without using data mining techniques. In the business world, the data is generally censored compared to uncensored data available in scientific databases. This means that data mining is generally applied on larger datasets that contain data that has to be handled securely. However, when looking at a particular methodology or algorithm, it is not very straightforward to determine whether it comes under the field of statistics or data mining. 

Statistics has always dealt primarily with numerical data. The datasets dealt with in data mining can be a mix of text, audio, images, videos, files, geographical data, etc. the goal here is to find interesting patterns in the data. Still, in order to find "interesting" patterns, the term "interesting" has to be defined. Generally, "interesting patterns" in data will have to be relevant to the application domain. In the case of data mining, its essence is that one does not know precisely what kind of pattern is to be found from the data. This makes it challenging to classify the information as relevant to the pattern or general. A definition that is too general may result in overfitting, while a definition that is too specific may result in leaving out patterns that should have been identified. The application of statistical training in such cases can be used to determine probabilistic models. It can drive the identification of measurement errors and the statistical significance of the various data points. Statistical analysis can be used to identify the data points that are affected by the root cause and those that are driven by pure chance.

Statistics make it possible to incorporate predictive analytics and develop various classifications that can impact the outcomes. Effective analysis cannot be performed without statistics. The use of advanced statistical methods applied during the process of data mining can help businesses increase their revenues, maximize operational efficiency, reduce costs and also help to improve customer satisfaction. The use of statistical software in data mining can truly give businesses an edge over their competitors by helping to increase their sales and also driving the execution of their business. Today, to stay competitive in the market, it is an ongoing challenge to keep up with market trends and make predictions on future outcomes. 

Access to a curated library of 250+ end-to-end industry projects with solution code, videos and tech support.

Request a demo

Data mining in statistics

Data mining involves the process of thoroughly searching through data in order to find patterns in the data. The data is usually not uniform but will have underlying patterns within it. The problem here is that sometimes some of the patterns identified may simply be random fluctuations that do not contain any other underlying information. Statisticians tend to look at data mining as a process of looking for patterns that are not actually there. Another issue is that statisticians have always dealt with smaller, more organized data sets when compared to the kind of data involved in data mining. Furthermore, due to the presence of larger data sets, statisticians are often not familiar with the data storage and manipulation techniques used to handle these large amounts of data. 

For statistics to remain up-to-date, statisticians need to incorporate some components of data mining into their techniques. Statistical data analysis should involve:

  •  learning more new algorithms,

  •  text-mining techniques,

  •  bagging, bumping and boosting algorithms  

  • exposure to Bayesian belief networks. 

Market basket and rule induction methods, decision trees, neural networks, classification using these trees and neural networks, clustering based on hierarchical methods and self-organizing maps, query association are all examples of techniques that are based on a close link between data mining and statistics. 

Suppose students from a statistics background are interested in taking up a profession in the applied statistics industry. In that case, it is beneficial for them to learn more about data mining algorithms and be more thorough with the software which can be used to implement these algorithms. In addition, these students can benefit from learning about the challenges associated with storing, retrieving, and manipulating large volumes of data and presenting this data with compelling visualizations.

Statistical Data Mining

Statistical Data Mining is an interdisciplinary field in software engineering. It is the computational technique of finding patterns in vast data sets and including strategies that involve brainpower, machine learning, and database frameworks to draw insights from the data. 

The data mining process aims to retrieve and isolate data from the datasets and build it into a structure for further analysis. Apart from the investigative research, it involves pre-preparing the data, model, and induction contemplations, identifying the relevance of various data points, analyzing variance in the data, and post-handling the patterns and structures identified.

How does data mining integrate with the components of Statistics?

Most data mining professionals tend to be unaware of the domain associated with statistics and their clients, whereas statisticians remain oblivious to the data mining and client domains. The focus in data mining is on database management and the processing of algorithms. Statisticians place all their focus on identifying uncertainties and handling them, and clients focus on incorporating the knowledge obtained in order to make business decisions. If all three of these parties widen their focus to achieve collaboration, there could be a real improvement in the final outcome. Statistics as a discipline is not particularly well-known for timely recognition of significant findings and has a good scope for improving this discipline.

Here are some examples of some techniques that involve incorporation of data mining with statistics:

  1. Descriptive statistics: This is typically used to analyze and determine which datasets can be further used for analysis and decision-making. Data visualization tools can be used to understand the distribution of data - normal, uniform, Poisson etc., and hence use the corresponding tools based on the distribution.

  2. Correlational analysis in data mining: Correlation analysis can be used to identify the variables which are relevant to a particular context.

  3. Hypothesis testing: Hypothesis testing is a method used in statistical analysis to compare specific statistical attributes to determine whether two large datasets are related or not. 

  4. Linear and Multiple Regression: In large datasets that are employed in data mining, there exist a high number of potential variables. Linear regression is used to identify and isolate the variables which significantly affect a particular outcome. Multiple regression is used to analyze how numerous factors working together can affect a specific outcome.

  5.  Outliers: Irrelevant values present in large datasets can significantly affect the spread and distribution of data. For example, suppose there is a product in a business that is of top-notch quality and is competitively priced but is still receiving consistent negative feedback from customers. In that case, it may negatively impact the business and lead to the business questioning its quality. It may be found that the negative feedback is because of issues related to delay in delivery which has nothing to do with the actual product itself.

  6. Dimensionality: Multiple regression models aim to determine how more than one independent variable can affect the outcome in different ways. However, every time a new variable is added to the regression model, the uncertainty of its predictive accuracy grows exponentially. The issue here is that for the prediction to be accurate, multiple variables have to be taken into account, but with the addition of each variable, the model's predictive accuracy model reduces. This is referred to as the curse of data dimensionality. This is a common challenge faced in data mining. The challenge is to maintain the dimensionality of the model but at the same time maintain its accuracy. Two statistical approaches can be used to meet this goal:
    1. Correlation analysis: Variables that affect the outcome in a similar manner are usually highly correlated. Hence, dropping some of these variables can reduce the number of variables involved without affecting the model's accuracy.

    2. Data visualization: Data visualization gives a good picture of the variables that are correlated. Correlated variables tend to be visually clustered into close groups and can be more easily identified.

Access Data Science and Machine Learning Project Code Examples

 

PREVIOUS

NEXT

Access Solved Big Data and Data Science Projects

About the Author

ProjectPro

ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

Meet The Author arrow link