Data Science vs Data Mining

By | December 27, 2020
Data Science vs Data Mining

Are you looking to see the difference between data science vs data mining?

Introduction to Data Science

Data is ruling the universe right now, and new data is getting added every second, which means the data processing techniques are becoming better equipped. As humans, we are trying to cope with and develop new approaches to process and make sense of so much data. The processing and analysis of data, in other words, is called data science.


Data is not just numerical data as we would have thought a few years back. It is not a set of numbers [10,20,30,40].

Today’s digital age data encompasses everything: quantitative data, qualitative data, images, videos, documents, emotions, medical data, business transactions, email text reports, etc. The list is endless. Everything is written on the web; everything spoken by someone, shared by someone, is DATA.

With so much information in hand, it is not easy to know what is relevant for a business.

Data science does just that – understand and retain only what is essential in the data accumulated from various sources to generate business insights to be strategically enhanced.

Where does Data Mining fit?

Data mining wasn’t coined until the 1990s when discovering more patterns and information became complex and required more resources. As the digital presence of companies was increasing, so was data. Data mining is a smart way to extract useful information while eliminating redundant parts of data. It is also called Knowledge Discovery or knowledge mining from the mess of data.

Data Mining is a step in the data science lifecycle that involves the following tasks:

  • Data acquisition and cleaning: Obtaining data from multiple sources, integrating the data, and removing the inconsistencies, redundancies, missing values, etc.
  • Data Selection: In this step, big datasets are transformed into a more useful subset that is required to solve a particular business problem
  • Data Transformation: The data is further transformed by performing aggregation, normalization, and applying statistical methods to get insights
  • Data Analysis and Mining: This is the step where data is analyzed to get patterns. Some standard techniques are association and clustering.
  • Data evaluation: The important insights and patterns are retained while the others are discarded to arrive at informed business decisions

Data Science working vs Mining working

We have understood that data mining is an essential step in the data science process. Web scraping and data manipulation are data mining processes that help get better insights from structured or unstructured data. The scope of Data mining is limited when compared to Data Science.

Here is how data science is performed (don’t be overwhelmed with the terms; you will get them when you do!):

  • Data Collection: Data is collected from various sources and integrated. Sometimes, this process is also included in data mining (say for smaller projects)
  • Data Cleaning, wrangling: The integrated data is cleaned, sorted, and other functions like aggregation are applied to the data. Then, data is transformed into a new, better set with the necessary variables.
  • Data mining: Data mining is then performed on the data to get some first-hand insights. Data mining also uses algorithms to gain insights. Some popular algorithms are clustering and association rules.
  • Data Analysis: After the initial analysis and understanding some patterns and insights about data, machine learning algorithms are applied to understand data and arrive at more accurate results.
  • Data Visualization: It is easier to view and analyze data in graphs and charts than writing many words. Visualization helps to see data mining results and analysis in an easy to understand manner and offers some more useful insights.
  • Predictive modeling: The model is applied to real datasets, and then the accuracy of the model is checked
  • Evaluation and performance improvements: If the model is overfitting or underfitting, various techniques are applied to improve the model’s performance.

You see that Data mining is just one step in the entire data science process.

Then why are the words used interchangeably so many times?

That’s because data mining is the most significant and most time-consuming task in the entire lifecycle. Even with many tools and techniques, data mining involves many processes, as we have seen above.

Data Science tools vs Mining tools

Data mining is a part of data analytics, and data analytics is a part of data science.

Data Science tools vs Data Mining tools

Data Science tools include big data frameworks, statistical tools, visualization tools, mining tools, analytical tools, machine learning tools, etc. However, when data reaches the mining stage, it is mostly structured, so data mining tools do not involve big data tools. Data science tools are generic, whereas data mining tools are more specific.

Some of the many tools used for data science are:

  • Apache Spark: Spark is a powerful analytical engine that can handle batch processing and stream processing both. It provides in-memory computing making it one of the fastest big data processing frameworks. Spark also has a very efficient cluster management system and is much better than Hadoop and about 100 times faster than MapReduce.
  • SAS: SAS is a closed source proprietary tool for data analysis and statistical modeling. SAS is very reliable and has strong data analysis capabilities. It supports many data formats and data encryption algorithms and is suitable for large organizations with huge datasets to analyze.
  • MATLAB: MATLAB is useful not only for machine learning but also for advanced deep learning algorithms. Using MATLAB, data scientists can perform highly complex computations, matrix operations, statistical modeling, and create powerful visualizations. MATLAB is an excellent end to end tool from the data cleaning stage till obtaining the required data insights.
  • RStudio: RStudio is a rich IDE for R programming language. R features some top libraries for data analysis and complex statistical computations. It is user-friendly, has excellent documentation and broad community support. RStudio provides a friendly GUI for working with R. You can see colorful visualization, perform analytical operations, and create scripts easily. RStudio works on different platforms and is free and open-source.
  • Jupyter notebooks: Jupyter notebooks are trendy amongst Python developers, especially data scientists, because of their ability to display plots interactively. You can use Python libraries for data cleaning, transformation, machine learning, visualization, etc. It is flexible compared to PyCharm and free and open-source.

Many Data mining tools are user-friendly even for those with no technical knowledge as they provide no coding GUI. Some essential tools are:

    • Weka: Weka is a very user-friendly and open-source tool for data mining. It is much preferred by data miners and requires no coding. It has rich tools for clustering, visualization, association, and classification. Weka is written in Java and also contains pre-processing tools and machine learning algorithms.
    • RapidMiner: RapidMiner is a visual environment that requires no coding. The GUI makes it easier to process data and design accurate models. It supports R scripting and can use algorithms from libraries like H2O, Weka, and many others. It has intuitive visualizations and also gives provisions to transform unstructured data into structured data.
    • Apache Mahout: Mahout is a data mining framework in which you can perform mining tasks quickly and efficiently. It runs on top of Hadoop, so it works well in a distributed environment. It includes implementations of many algorithms like k-means, mean-shift, naïve Bayes, etc., that are commonly used for data mining.
    • Teradata: Teradata follows an open parallel architecture. Teradata miner allows for faster iterations for model feedback and correction and simplifies data profiling. You can create intelligent and intuitive data sets. Teradata is used mainly for data warehousing applications and supports over 50 petabytes of data.
  • Orange: Orange allows for easy creation and execution of workflows. It provides a visual feature-rich toolbox. You can go any level deep using the data visualization techniques available in this tool. You can do everything visually, so there is no need for coding. If you do want to code, you can do so with Python scripting. Orange is open source.

Data Science vs Data Mining: Head to head differences

Now that we are clear as to where data mining is placed in the data science lifecycle, it is time to summarize the differences side-by-side. You may get some extra difference points as well, so read on!

Data Mining Data Science
Involves data analysis and modeling to find trends and patterns in data using past and present data Involves the entire process of finding insights and arriving at business decisions starting with data collection, cleaning, analysis, and making relevant business decisions.
It is a knowledge discovery process from the data obtained and is a part of the KDD (Knowledge Discovery in Database) process It is a whole process from data discovery to achieving data wisdom from the data knowledge obtained in the mining step
Mostly involves using structured data that has been formatted and transformed before Uses both structured and unstructured data as well as tools, algorithms to extract relevant information
Doesn’t need visualizations in many cases. Scientific and mathematics methods and techniques are used to derive facts Uses analytical tools, business intelligence, visualization tools, etc
Limited to finding trends and patterns Includes deriving actionable insights, verify or reject a hypothesis, make business decisions
More involved in the process, and focuses on scientific & mathematical aspects of data Focuses on the overall business picture and contains the right mix of business and technical aspects
Some popular applications are market basket analysis, fraud detection, customer segmentation, CRM (Customer Relationship Management), Lie detection, surveillance, crime analysis, etc. Has a wide range of applications like managing logistics with best available resources, personalized healthcare/movie/product recommendations, detecting and predicting diseases, Image and speech recognition, etc.


Here is a quick recap of what we have learned in this article:

  • Data mining and data science are often interchangeably used; however, data science is the big umbrella that encompasses data mining as well
  • Data mining is the process in which data is turned into insights by digging (mining) through various past and present datasets and finding patterns and trends in data
  • There are several tools and techniques to perform mining, and there are even more for data science
  • Data mining requires a lot of technical and analytical knowledge, whereas data science needs business knowledge along with technical, analytical, and creative thinking skills

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