Data mining vs. Data science

When you hear the terms data science and data mining, what do you think of? If you're like most people, you might believe that these two terms are interchangeable, but there are quite a lot of differences between the two concepts. Here we will learn about data mining and data science, will explore the differences between data science and data mining, how each concept came to be and why it matters to understand their similarities.

 

Table of content 

  • Data science vs. Data mining: Introduction 
  • Data science vs. Data mining: Skills 
  • Data science vs. Data mining: Differences 
  • Where to Use: Data Science / Data Mining?
  • Frequently Ask Questions
  • Conclusion


Data science vs. Data mining: Introduction 


Data science is a popular buzzword thrown around by tech giants, health care, banks, and even governments. It's important to understand that data science is a multi-disciplinary field; it covers big data processing, business intelligence reporting tools, predictive modelling, and more. 

Data science is a discipline that extracts usable insights from data by combining technical knowledge, programming skills, math, and statistical ability. Numbers, text, images, video, audio, and other data are utilized to develop artificial intelligence (AI) systems that can perform tasks that would generally need intellectual ability.

Data mining is a technique for extracting useful information from a vast amount of data. It entails utilizing one or more software to analyze data patterns in big batches of data. Data mining is used in a variety of sectors, including science and research.


Data science vs. Data mining: Skills

Now let's discuss skills that require both to become an expert in these fields.

  •  Data Mining Skills
  • Programming languages (R, Python, Java, C++, Matlab, SAS, SQL)
  • Knowledge of big data frameworks (Hadoop, Spark, Apache, Flink)
  • Operating system (Linux)
  • Database knowledge (Relational and non-Relational Databases)
  • Basic Statics Knowledge
  • Machine Learning 
  • NLP


Data Science Skills

If you're looking for a way to get into data science, these are the skills that you need to learn.

  • Programming languages (R, Python, Java, Matlab, SQL)
  • Machine learning
  • Deep learning
  • NLP
  • Knowledge of Databases 
  • Statistics
  • Knowledge of analytical tools(Tableau, Power BI, SAS, Hadoop, Spark, Hive)
  • Knowledge of unstructured data


Where to Use: Data Science / Data Mining?

Data science is engaged in many aspects of our lives and can assist businesses in dealing with the following scenarios:

  • Predictive analytics for fraud prevention
  • Machine learning is being used to streamline marketing procedures.
  • Using data analytics to make actuarial operations more efficient

Data mining is now widely employed in a variety of fields, including business, science, technology, medical, and telecommunications.

  • Data mining applications include credit card transaction analysis.
  • Housing and communal services data analysis, loyalty card programs in retailers based on consumer preferences.
  • National security (intrusion detection), and human genome research.

When it comes to data mining and data science, what's the difference?

The majority of data mining study focuses on structured data. Structured, semi-structured, and unstructured data can all be used for data science. Data mining uses mathematical and scientific methods to find patterns and trends, whereas Data science employs business problems, health problems analytics models in short in prediction.

Utilization Areas: Data science can be primarily utilized for business purposes such as assessing business decisions, predictive analytics to predict outcomes, managing businesses efficiently whereas

Data mining can be primarily utilized for scientific purposes such as discovering patterns and relationships in the data to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty.

Scope:

While discussing both of them we should keep it in mind that data science is a field and data mining is just a technique that can be used by data science experts so if you are focusing on any one of these it is better to focus on data science as it teaches you more efficient techniques to use which will be far better than data mining technique


Frequently Ask Questions:

Let's discuss some questions which are frequently come to mind when anybody sees these terminologies

   1. Is data mining a viable career option?

This is a fantastic opportunity for people to learn data mining skills and take advantage of the industry's predicted expansion in the coming years. Analysts that specialize in data mining can be found almost anywhere. Different types of businesses in various industries must better utilize their data.

     2. Data science and Data Mining are both the same?

The concept of data science is becoming more popular by the day, but a lot of people use it interchangeably with terms like data mining. While there are some similarities among them both, both are not the same. 

Data mining refers to processes and tools used for identifying patterns in large amounts of data that are stored within databases. On the other hand, Data science is used for prediction.

   3. Is data mining a type of artificial intelligence?

Data mining is a crucial component of Artificial Intelligence (AI). Predictive algorithms derived from data mining will serve as the foundation for the AI application.

  4. Is Excel a tool for data mining?

The majority of data mining software programs cost thousands of dollars, but there is one program on your desktop that is ideal for beginners: Excel is an excellent one. Data mining, also known as knowledge discovery, is a useful method for identifying patterns or connections in relational data.

5. Is data mining considered a skill?

Data mining experts examine data and produce commercial solutions using statistical software. As a result, data mining experts must be proficient in technical abilities, particularly programming software, and business intelligence.

Conclusion:

A common misconception is that data mining and data science are synonymous. They're not, although they're frequently used interchangeably. While both fields involve gathering and analyzing large amounts of data, they approach it from a different perspective. Data mining uses automated techniques to retrieve useful information from a set of data. The goal is to discover hidden patterns or trends that could help improve decision-making processes. Data science doesn't use these techniques; instead, it deals with interpreting massive sets of data for other purposes, such as scientific research or even marketing campaigns.

Hope that by reading this blog you will get a clear idea about Data mining and Data science and after this you can differentiate between data science and Data mining projects as well.

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