9 Awesome Difference Between Data Science Vs Data Mining

Difference Between Data Science and Data Mining

Data Science and Data Mining need no introduction. They are the biggest buzz words of the 21^st century. That’s why it has become mandatory for everyone to understand these terms and the key differences between Data Science and Data Mining. Be it someone who is just exploring the ‘data-space’ or someone who is serious about making a career in this area.

Essentially, Science and Mining are completely dissimilar terms, but when it comes to data they often go hand in hand. However, there does exist a relevant difference between data science and data mining which makes it worthwhile to probe.

Historical Perspective

Before we move to the technical descriptions let’s have a look at the evolution of the terms. A historical investigation will clarify how the terms are used currently.

• The word ‘Data Science’ has been around 1960s but back then it was used as an alternative to ‘Computer Science’. Presently, it carries a completely different meaning.
• In 2008, D. J. Patil and Jeff Hammerbacher became first individuals to call themselves ‘Data Scientists’ in order to describe their role at LinkedIn and Facebook respectively.
• In 2012, Harvard Business Review article cited Data Scientist as the ‘Sexiest job of the 21^st Century’.
• The term Data Mining has evolved parallelly. It became prevalent amongst the database communities in the 1990s.
• Data Mining owes its origin to KDD (Knowledge Discovery in Databases). KDD is a process of finding Knowledge from information present in databases. And Data Mining is a major subprocess in KDD.
• Data Mining is often used interchangeably along with KDD.

Although these names have come into picture independently, they often come out as complementary to each other as, after all, they are closely related to data analysis.

Short Description

Data Mining is about finding the trends in a data set. And using these trends to identify future patterns. It is an important step in the Knowledge Discovery process. It often includes analyzing the vast amount of historical data which was previously ignored.

Data Science is a field of study which includes everything from Big Data Analytics, Data Mining, Predictive Modeling, Data Visualization, Mathematics, and Statistics. Data Science has been referred to as the fourth paradigm of Science. (the other three being Theoretical, Empirical and Computational). Academia often conduct exclusive research in Data Science.

Head to Head Comparison between Data Science Vs Data Mining (Infographics)

Below is the Top 9 Comparision of Data Science Vs Data Mining

Example Use Case

Consider a scenario where you are a major retailer in India. You have 50 stores operating in 10 major cities in India and you have been operational for 10 years.

Let’s say, you want to study the last 8 years data to find the number of sales of sweets during festive seasons of 3 cities. If that’s your objective, I would recommend you employ a person with Data Mining expertise. A Data Miner would probably go through historical information stored in legacy systems and employ algorithms to extract trends.

Consider another case where you want to know which sweets have received more positive reviews. In this case, your sources of data may not be limited to databases, they could extend to social websites or customer feedback messages. In this case, my suggestion to you would be to employ a Data Scientist. A person employed as a Data Scientist is more suited to apply algorithms and conduct this socio-computational analysis.

Key Differences Between Data Science Vs Data Mining

Below is the difference between data science and data mining are as follows

• Data Mining is an activity which is a part of a broader Knowledge Discovery in Databases (KDD) Process while Data Science is a field of study just like Applied Mathematics or Computer Science.
• Often Data Science is looked upon in a broad sense while Data Mining is considered niche.
• Some activities under Data Mining such as statistical analysis, writing data flows and pattern recognition can intersect with Data Science. Hence, Data Mining becomes a subset of Data Science.
• Machine Learning in Data Mining is used more in pattern recognition while in Data Science it has a more general use.

Note

• Data Science and Data Mining should not be confused with Big Data Analytics and one can have both Miners and Scientists working on big datasets.

Data Science Vs Data Mining Comparision Table

Conclusion – Data Science Vs Data Mining

So here you go! I am sure now you are more aware of what the key differences between the two are and in what context the two should be utilized. One thing you should remember is there are no formal and precise definitions of Data Science and Data Mining. There are still debates going on amongst the academia and the industry as to what constitutes an accurate definition. However, everyone is on the same page with respect to the high-level differences and description of the two terms which we explored in this article.

Recommended Article

This has been a guide to Data Science Vs Data Mining, their Meaning, Head to Head Comparison, Key Differences, Comparision Table, and Conclusion. You may also look at the following articles to learn more –

1. Data Science and Its Growing Importance
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3. Predictive Analytics vs Data Science – Learn The 8 Useful Comparison
4. 8 Important Data Mining Techniques for Successful Business